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DTSTART:20190310T030000
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DTSTART:20191103T010000
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DTSTART:20200308T030000
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DTSTART:20201101T010000
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DTSTART:20210314T030000
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DTSTART:20211107T010000
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END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:COSC seminar: End-User Interfaces for In-Situ Sensemaking from Dr.
Pourang Irani
DTSTART:20200930T213000Z
DTEND:20200930T223000Z
UID:1
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Pourang Irani<
/a>\nTitle: End-User Interfaces for In-Situ Sensemaking\nLocation: https:/
/ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0UT09\nAbstract:
Our reliance on data for making decisions or sensemaking is undergoing a
transformation from being predominantly carried out in limited settings (s
uch as on traditional PCs) to taking place while on-the-go and during ever
yday activities. Sensemaking may be passive, such as when being informed b
y a wearable device to reach your daily goals, or active, when an athlete
may consult their network of bodyworn sensors to gauge their training perf
ormance. Common among these scenarios is the growing need for advanced use
r interfaces that will enable end-users to engage in sense-making, while o
n-the-go. Current tools and devices, such as smartphones and head-worn dis
plays, are not tailored to support the need for interacting with data for
in-situ. In this talk I will present on-going work that focuses on the dev
elopment of end-user software interface technologies for meeting data anal
ytic needs in ad-hoc mobile environments. Such tools can facilitate the ex
ploration and interaction with data through improved information navigatio
n, visualization and manipulation interfaces in-situ.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: CCA 2D: a computational convex analysis library for b
ivariate functions from Dr. Yves Lucet
DTSTART:20200922T220000Z
DTEND:20200922T230000Z
UID:2
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Yves Lucet\n
Title: CCA 2D: a computational convex analysis library for bivariate funct
ions\nLocation: https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1K
b0FYYXA0UT09\nAbstract: Critical to any optimization problem, duality can
be expressed using Fenchel's duality Theorem, which is formulated using th
e convex conjugate. Computing such a conjugate and other fundamental conve
x transforms is the goal of computational convex analysis (CCA). In this t
alk, we will look at the upcoming public release of CCA 2D, a numerical li
brary for computing with bivariate piecewise linearquadratic functions. We
will present optimal data structures for convexity checking, conjugacy, a
nd addition. How best to convert between the different function representa
tions will also be explained. The talk will finish with remaining challeng
es and open problems.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Population-level Behavior Analysis on Smart Environme
nt Ambient Data from Dr. Beiyu Lin
DTSTART:20201027T220000Z
DTEND:20201027T230000Z
UID:3
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Beiyu Lin\nTitl
e: Population-level Behavior Analysis on Smart Environment Ambient Data\nL
ocation: https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA
0UT09\nAbstract: Data about the ordinary routines people perform in daily
environment provide insights about human behaviors. Comparing norms among
population subgroups offers the potential to transform how important servi
ces are delivered in millions of people. With massive amounts of ambient d
ata, we have entered an era for a much greater understanding of complex be
havior through the development of innovative algorithms. In this talk, I w
ill discuss my work, population-level behavior analysis on smart environme
nt ambient data. I will first describe the algorithms that tackle the comp
utational challenges in ambient data to process behavior features in real
time. I will then introduce two stochastic methods to model indoor behavio
r and compare behavior differences among population subgroups. I will also
present our data-driven approach, inverse reinforcement learning, to mode
l and quantify residents’ behavior as well as to distinguish cognitively
impaired groups from healthy populations. I will conclude the talk with e
xtending the scope of behavior modeling in various directions.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Efficient 2D image segmentation from Brandon Graham-K
night
DTSTART:20201013T220000Z
DTEND:20201013T230000Z
UID:4
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Brandon Graham-Knight\nTitle: Efficient 2D image
segmentation\nLocation: https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF
3SWg4em1Kb0FYYXA0UT09\nAbstract: Image segmentation is a popular topic ena
bled by rapid advances in neural network processing. There is also a trend
towards computational efficiency in segmentation algorithms, brought abou
t by economic and environmental interests, and often acutely motivated by
constraints in how the solutions are deployed. Recent exploration of neura
l network computational efficiency has largely focused on the depth and wi
dth of the network, as well as image size. Ensembling and gradient boostin
g are two wellknown methods for increasing performance through a collectio
n of smaller networks. Ensembling is used when different models produce la
rgely uncorrelated predictions\; the combination of these predictions redu
ces overall error. Gradient boosting produces correlated models such that
the sum of their losses is minimized\; successive models are trained using
the residual loss of previous models, with each new learner correcting mi
stakes of past learners. This thesis seeks to explore the scalability and
efficiency of image segmentation neural networks by including the techniqu
es of ensembling and gradient boosting. The approach is evaluated on both
the Severstal Steel Defect Detection and Kidney Tumor Segmentation dataset
s, though it could easily be applied to any two-dimensional image segmenta
tion task. The final boosted model produces results comparable to the base
line in 5 of the 6 evaluated classes, while using only 2.5% of the trainab
le parameters. The success of the approach has significant implications fo
r deployed applications\; not only is the overall network much smaller, th
e miniature sub-networks can each be calculated independently and aggregat
ed with simple addition and averaging. This introduces a real possibility
for efficient distributed inferencing, decoupling performance from the max
imum size calculable by a single piece of hardware.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Tiny Databases for Internet of Things (IoT) Applicati
ons from Dr. Ramon Lawrence
DTSTART:20201124T230000Z
DTEND:20201125T000000Z
UID:5
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Ramon Lawrence
\nTitle: Tiny Databases for Internet of Things (IoT) Applications\nLoc
ation: https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0U
T09\nAbstract: Our world is continually monitored by sensor systems collec
ting information about our economy, industry, and environment. Embedded sy
stems must be efficient and minimize energy usage. This presentation overv
iews the challenges on building database systems for these tiny devices. T
echniques designed for servers and Big Data systems do not typically apply
to systems with minimal hardware resources. Example algorithms and techni
ques are discussed, including two database systems designed for Arduino an
d other small devices. Applications of embedded database systems include e
nvironmental and agricultural monitoring, and a system developed and deplo
yed in partnership with the City of Kelowna is highlighted.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Motion Capture, Synthesis and Perception for Creating
Realistic and Intelligent Virtual Characters from Dr. Yingying Wang
DTSTART:20210317T010000Z
DTEND:20210317T023000Z
UID:6
LOCATION:https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
DESCRIPTION:Dr. Yingying Wang\nTitle: Motion Capture, Synthe
sis and Perception for Creating Realistic and Intelligent Virtual Characte
rs\nLocation: https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
\nAbstract: Character animation plays a key role in Games, Human-Computer
Interaction (HCI), Virtual Reality (VR), Augmented Reality (AR) applicatio
ns and Robotics simulations, In this talk, I will present my work that use
s motion capture, synthesis and perception methods to create realistic and
intelligent virtual characters. Specifically, I will introduce several mo
tion capture projects that capture accurate hand motions or shapes using d
ifferent devices. I will further discuss how to add detailed hand motions
appropriately to virtual characters, and how to synthesize conversational
gestures well coordinated with body motions. Given motion capture data, by
using well-designed multi-channel deep autoencoders, we can further extra
ct high level features for motion indexing, retrieval and reconstruction p
urposes. Furthermore, I will demonstrate motion synthesis research and inn
ovation projects that infuse intelligence into virtual characters.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Object Tracking in Dynamic Scenes from Dr. Mohamed Ha
med Abdelpakey
DTSTART:20210126T230000Z
DTEND:20210127T000000Z
UID:7
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Mohamed Hamed Abdelpakey\nTitle: Object Tracking in Dynami
c Scenes\nLocation: https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4
em1Kb0FYYXA0UT09\nAbstract: Visual object tracking is a fundamental task i
n the field of computer vision. Visual object tracking is widely used in n
umerous applications which include, but are not limited to video surveilla
nce, image understanding, robotics, and human-computer interaction. In ess
ence, visual object tracking is the problem of estimating the states/traje
ctory of the object of interest over time. Unlike other tasks such as obje
ct detection where the number of classes/categories are defined beforehand
, the only available information of the object of interest is at the first
frame.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Reinforcement Learning Applications from Dr. Yuxi Li
DTSTART:20210224T000000Z
DTEND:20210224T010000Z
UID:8
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Yuxi Li\nTitle: Reinforceme
nt Learning Applications\nLocation: https://ubc.zoom.us/j/64047865030?pwd=
SVltbHhpZmF3SWg4em1Kb0FYYXA0UT09\nAbstract: What is the most exciting AI n
ews in recent years? AlphaGo! What are key techniques for AlphaGo? Deep le
arning and reinforcement learning (RL)? What are application areas for RL?
A lot! In fact, besides games, RL has been making tremendous achievements
in diverse areas like recommenders and robotics. In this talk, we will in
troduce RL briefly, present several RL applications, and discuss issues fo
r successfully applying RL in real life scenarios.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Data Modeling and Learning For Dynamical Systems from
Dr. Sumona Mukhopadhyay
DTSTART:20210318T163000Z
DTEND:20210318T180000Z
UID:9
LOCATION:https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
DESCRIPTION:Dr. Sumona Mukhopadhyay\nTitle: Data Modeling an
d Learning For Dynamical Systems\nLocation: https://people.ok.ubc.ca/yluce
t/cosc/seminars.php?range=past\nAbstract: The core of Artificial Intellige
nce (AI) application is modeling from data and optimization for parameters
. A key goal of data modeling is to encapsulate the complexities of real-w
orld problems which involve incomplete and noisy data. In this talk, I wil
l discuss some of the requirements and challenges for building a data mode
l with applications in communications and robotics. Considering these chal
lenges, I will describe my proposed novel models that are analytically der
ived using techniques from nonlinear dynamics such as chaos theory and sym
bolic dynamics. I will also discuss my research projects where I use signa
l processing and nonlinear dynamics in designing Machine Learning algorith
ms for anomaly detection and behavioral monitoring for performance predict
ion in sports. The research talk will conclude by presenting some promisin
g future directions for research in the area of cognitive computing.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Linearized Robust Counterparts of Two-stage Robust Op
timization Problems from Dr. Amir Ardestani-Jaafari
DTSTART:20200227T233000Z
DTEND:20200228T003000Z
UID:10
LOCATION:UBCO, ASC 301A , COCANA CoLab (ASC 301A)
DESCRIPTION:D
r. Amir Ardestani-Jaafari\nTitle: Linearized Robust Counterparts of Tw
o-stage Robust Optimization Problems\nLocation: UBCO, ASC 301A , COCANA Co
Lab (ASC 301A)\nAbstract: In many decision-making problems, some decisions
must be made here-and-now before the realization of actual data. However,
other decisions are of a wait-and-see type, i.e., they can wait until unc
ertain data is revealed. It is a computationally challenging task to make
wait-and-see decisions fully adaptable while the problem remains computati
onally tractable. We propose a new tractable method that can be applied to
a class of two-stage optimization problems under uncertainty. The method
mainly relies on a linearization scheme employed in bilinear optimization
problems, therefore it gives rise to the “linearized robust counterpart
” models. We identify a close relation between this linearized robust c
ounterpart model and the popular affinely adjustable robust counterpart mo
del. We also describe methods of modifying both types of models to make th
ese approximations less conservative. These methods are heavily inspired b
y the use of valid linear and conic inequality in the linearization proces
s for bilinear models. We finally demonstrate the potential of our method
in operations management applications.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Investigating the Unexpected: What Happens When Robot
s do Weird Things? from Denise Y. Geiskkovitch
DTSTART:20210316T000000Z
DTEND:20210316T013000Z
UID:11
LOCATION:https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
DESCRIPTION:Denise Y. Geiskkovitc
h\nTitle: Investigating the Unexpected: What Happens When Robots do We
ird Things?\nLocation: https://people.ok.ubc.ca/ylucet/cosc/seminars.php?r
ange=past\nAbstract: Social robots can now be found in places such as scho
ols, shopping malls, hospitals, and even homes. These robots are designed
for individuals to talk and interact with, and the robots use gestures and
emotions to communicate with people. Surprisingly, people also respond to
such robots socially, attributing them with rights and morals. My researc
h explores what happens when robots exhibit unexpected behaviours, includi
ng errors and persuasion, and how these influence people’s actions and r
esponses. In my prior work, I explored a large range of related questions,
including how young children react to robot errors and how these affect t
rust, as well as how people respond to manipulative and persuasive robots.
Moving forward, I propose that social robots can be effective tools for h
elping children and adults to deal with emotional or mental health problem
s (e.g., stress, loneliness, emotional development, anxiety). By investiga
ting how simple robots can help individuals, and using common unexpected b
ehaviours purposefully, my research programme will provide insight into pe
ople's relationships with robots, and how we can build robots to help peop
le in return.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Software Development in Online Collaborative Platform
s from Dr. Gema Rodriguez-Perez
DTSTART:20210315T220000Z
DTEND:20210315T233000Z
UID:12
LOCATION:https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
DESCRIPTION:Dr. Gema Rod
riguez-Perez\nTitle: Software Development in Online Collaborative Plat
forms\nLocation: https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=p
ast\nAbstract: Open Source Software (OSS) products are created and maintai
ned by a myriad of diverse developers who collaborate globally through onl
ine collaborative environments. The way how these developers interact can
be the key to successful software products, or the cause of software quali
ty problems. Therefore, software development is typically a collective pro
cess that concerns not only technical aspects of how to build and maintain
software products, but also concerns human aspects. An important technica
l aspect of current software systems are defects, new vulnerabilities, and
bugs. They may cost the economy tens of billions of dollars annually. The
understanding of how software bugs are introduced leads to develop techni
ques that can avoid future changes that introduce bugs. On the other hand,
an important non-technical aspect of current software systems is diversit
y. Social diversity (e.g., race, age, gender) is beneficial beyond ethical
reasons, as it helps address a problem from different perspectives and de
signs more robust software products. Indeed, it has been recognized as a h
igh value team characteristic and many companies have increased their effo
rts to create more diverse teams. The mission of my research in technical
aspects is not only to develop techniques to avoid changes that introduce
bugs, but also for the sake of self-learning and peer- assessment, and for
identifying how effective the project testing and verification strategy i
s. With respect to non technical aspects, my studies lead to better unders
tanding the role of social diversity in online software development and cr
eating awareness of the social diversity during this process.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: COSC Undergraduate Research Conference from COSC Unde
rgraduate Research Conference
DTSTART:20210426T173000Z
DTEND:20210427T000000Z
UID:13
LOCATION:https://ubc.zoom.us/j/64912996304?pwd=MEkyd3dDSDZiTjBqd1kwUzAra1NZ
QT09
DESCRIPTION:COSC Undergraduate Research Conference\nTitle: COSC Undergraduate
Research Conference\nLocation: https://ubc.zoom.us/j/64912996304?pwd=MEkyd
3dDSDZiTjBqd1kwUzAra1NZQT09\nAbstract: https://people.ok.ubc.ca/ylucet/cos
c/seminar/urc2021.pdf
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Automatic Mining and Summarization of Crowd Sourced S
oftware Knowledge from Dr. Uddin
DTSTART:20210416T220000Z
DTEND:20210416T230000Z
UID:14
LOCATION:https://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0
UT09
DESCRIPTION:Dr. Uddin\nTitle: Automatic
Mining and Summarization of Crowd Sourced Software Knowledge\nLocation: h
ttps://ubc.zoom.us/j/64047865030?pwd=SVltbHhpZmF3SWg4em1Kb0FYYXA0UT09\nAbs
tract: Software is ubiquitous. We rely on software to drive the economy an
d to improve quality of life. As such, there is now an increasing need to
educate software developers to produce software quickly but efficiently. U
nfortunately, my surveys of 330 IBM developers found that the official lea
rning resources of software can be often incomplete, obsolete, and incorre
ct. In subsequent surveys of 178 software developers, I found that develop
ers rely on online software forums to compensate for the shortcomings in o
fficial software documentation, but the huge volume and scattered nature o
f crowd-sourced software forums present significant challenges to get quic
k, concise and correct insights. Indeed, among the numerous online crowd-s
ourced platforms for developers, Stack Overflow alone has over 120 million
posts with over 11 million registered users. However, in a study of thous
ands of C++ code examples shared in Stack Overflow, we observed that the c
ode examples can contain critical security vulnerabilities. I have develop
ed Opiner, an online engine to search and summarize reviews (i.e., positiv
e and negative opinions) about APIs (Application Programming Interfaces) f
rom online developer forum. APIs are interfaces to reusable software libra
ries. To support API documentation from crowdsourced data with information
about code quality, Opiner provides summaries of the shared API usage exa
mples. The summaries consist of code examples along with reviews to inform
of their quality attributes. We observe that our proposed domain-specific
summarization algorithms are significantly more useful than off-the-shelf
techniques. In seven user studies, we find that Opiner positively impacts
the productivity of developers to learn and use APIs for diverse developm
ent tasks. Opiner website is available online and is visited by developers
across the world. I conclude by outlining my research vision, around my t
wo long-term goals: 1. Summarization of Big Crowd Sourced Software Knowled
ge. I study the diverse nature of contents shared in crowd-sourced develop
er platforms and design tools and techniques to automatically and efficien
tly aggregate (i.e., collect and summarize) those contents using machine l
earning, text analysis and software analytics. 2. Analyzing Trust of Crowd
-Source Software Knowledge. I study the quality of software usage informat
ion shared in online developer forums and design techniques to recommend t
he best usage practices.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Influencing Healthier Shopping Habits in E-commerce b
y Tailoring Persuasive Strategies from Dr. Ifeoma Adaji
DTSTART:20210319T000000Z
DTEND:20210319T013000Z
UID:15
LOCATION:https://people.ok.ubc.ca/ylucet/cosc/seminars.php?range=past
DESCRIPTION:Dr. Ifeoma Adaji\nTit
le: Influencing Healthier Shopping Habits in E-commerce by Tailoring Persu
asive Strategies\nLocation: https://people.ok.ubc.ca/ylucet/cosc/seminars.
php?range=past\nAbstract: While a lot of effort and research has gone towa
rds influencing people to be more active with the use of apps, games, and
exercise tools not much has been done in influencing people to eat healthy
foods, especially by influencing healthy shopping habits. The use of pers
uasive strategies to change people’s behavior is an active research area
in several domains including e-commerce. Research suggests that persuasiv
e strategies are more effective in bringing about a desired behavior chang
e when they are tailored to individuals or groups of similar individuals.
Unlike in other domains where demographic data such as age, gender, and cu
lture are used to tailor persuasive strategies, these factors are not know
n in ecommerce and thus can not be used. There is therefore a need to iden
tify what factors can be used to tailor persuasive strategies in e-commerc
e to make them more effective in bringing about a desired change in behavi
or\; in this case, shopping for healthier foods. To fill this gap and to t
ailor persuasive strategies to e-commerce shoppers, this study proposes to
use the shopping behaviour of consumers, in particular their shopping mot
ivation. To achieve this, a structural research model was developed using
Partial Least Squares Structural Equation Modeling (PLS-SEM) to identify t
he susceptibility of shoppers to persuasive strategies based on their shop
ping motivation (shopper types: convenience shoppers, variety seekers, sto
re oriented shoppers, and balanced buyers). This model was tested by condu
cting a user study of 244 e-commerce shoppers. The result from the model w
as used to develop ShopRight, a persuasive game to influence behavior chan
ge in e-commerce shoppers and help them develop healthier shopping habits.
To evaluate the game's effectiveness, a study of 305 participants was con
ducted using a data-driven approach to measure the susceptibility of parti
cipants to the persuasive strategies. The findings from this study suggest
that consumer’s online shopping motivation can be used to tailor persua
sive strategies in e-commerce and in particular, influence healthy shoppin
g online. This study's findings also show that tailored persuasive strateg
ies are more likely to bring about a change in attitude or behavior than n
on-tailored strategies. Furthermore, a serious game can be used as a learn
ing tool to influence healthy shopping habits, educate shoppers on the nut
ritional value of foods and show that healthy foods can be purchased on a
budget.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Adaptive MCMC For Everyone from Dr. Jeffrey Rose
nthal
DTSTART:20210114T010000Z
DTEND:20210114T020000Z
UID:16
LOCATION:Zoom recording Passcode: Nzy8PB&\;A
DESCRIPTION:Dr. Jeffrey Rosenthal\nTitle: Adaptive
MCMC For Everyone\nLocation: Zoom recording Passcode: Nzy8PB&\;A\nAbstr
act: Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis Al
gorithm and the Gibbs Sampler, are an extremely useful and popular method
of approximately sampling from complicated probability distributions. Adap
tive MCMC attempts to automatically modify the algorithm while it runs, to
improve its performance on the fly. However, such adaptation often destro
ys the ergodicity properties necessary for the algorithm to be valid. In t
his talk, we first illustrate MCMC algorithms using simple graphical examp
les. We then discuss adaptive MCMC, and present examples and theorems conc
erning its ergodicity and efficiency.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: The UK Biobank: Brain Imaging at the Population
Scale from Dr. Karla Miller
DTSTART:20210217T170000Z
DTEND:20210217T180000Z
UID:17
LOCATION:https://people.ok.ubc.ca/ylucet/symposium/seminars.php?range=past
DESCRIPTION:Dr. Karla
Miller\nTitle: The UK Biobank: Brain Imaging at the Population Scale\
nLocation: https://people.ok.ubc.ca/ylucet/symposium/seminars.php?range=pa
st\nAbstract: The UK Biobank is currently conducting the most ambitious im
aging study ever undertaken, scanning 100,000 subjects, with brain imaging
as a key component. In this talk, I'll overview the Biobank neuroimaging
resource, which includes six separate imaging modalities. I'll then presen
t several studies that highlight how this resource is already revolutionis
ing the neuroimaging field by enabling both discovery science and hypothes
is-driven research that previously would not have been possible.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Bio-inspired Swarm Robotics for Planar Construct
ion from Dr. Andrew Vardy
DTSTART:20210401T190000Z
DTEND:20210401T200000Z
UID:18
LOCATION:https://ubc.zoom.us/j/65764314667?pwd=bkVxSmRsNmxybzNQUHczUG5YM21K
Zz09
DESCRIPTION:Dr. Andrew Vardy\nTitle: Bio-inspired Swarm Robotics for Planar
Construction\nLocation: https://ubc.zoom.us/j/65764314667?pwd=bkVxSmRsNmx
ybzNQUHczUG5YM21KZz09\nAbstract: In this talk I will review my research gr
oup's work on swarm robotics and how we get our robots to move objects int
o desired configurations, a task we call planar construction. We are inspi
red by social insects which use pheromones to help guide the construction
of their nests. In a similar way we define scalar fields that guide the ro
bots as they gather objects together or form them into a desired shape. I
will show some of our results in simulation as well as some limited trials
on physical robots.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Using dynamical geometry tools to study algorith
ms from Dr. Scott B Lindstrom
DTSTART:20210929T230000Z
DTEND:20210930T000000Z
UID:19
LOCATION:https://ubc.zoom.us/j/67371297541?pwd=UkczdHI5dWNBK0FWTCtsaW1qRlJj
dz09
DESCRIPTION:Dr. Scott B Lindstrom\nTitle: Using dynamical ge
ometry tools to study algorithms\nLocation: https://ubc.zoom.us/j/67371297
541?pwd=UkczdHI5dWNBK0FWTCtsaW1qRlJjdz09\nAbstract: I will describe how I
used the free geometry tool Cinderella to discover and describe a class of
algorithms that may be useful for nonlinear optimization. This presentati
on will be conducted entirely within the dynamical interface, so that all
of the concepts are very visible. It should be accessible to, and the tool
s-based approach may be useful for, a wide audience. The presentation is p
rincipally based on the recent article:\n\nLindstrom, Scott B. "Computable
centering methods for spiraling algorithms and their duals, with motivati
ons from the theory of Lyapunov functions." arXiv preprint arXiv:2001.1078
4 (2020).\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Q/A on Using dynamical geometry tools to study a
lgorithms from Dr. Scott B Lindstrom
DTSTART:20210930T000000Z
DTEND:20210930T003000Z
UID:20
LOCATION:https://ubc.zoom.us/https://ubc.zoom.us/j/65321275119?pwd=NUlwaE0z
V2thOVJ4UWlRUDJwcVp1QT09meeting/register/u5Etdu2qrTktEtXr8ka0N7sPgzrpt-byE
ScK
DESCRIPTION:Dr. Scott B Lindstrom\nTitle: Q/A on Using dynam
ical geometry tools to study algorithms\nLocation: https://ubc.zoom.us/htt
ps://ubc.zoom.us/j/65321275119?pwd=NUlwaE0zV2thOVJ4UWlRUDJwcVp1QT09meeting
/register/u5Etdu2qrTktEtXr8ka0N7sPgzrpt-byEScK\nAbstract: I will describe
how I used the free geometry tool Cinderella to discover and describe a cl
ass of algorithms that may be useful for nonlinear optimization. This pres
entation will be conducted entirely within the dynamical interface, so tha
t all of the concepts are very visible. It should be accessible to, and th
e tools-based approach may be useful for, a wide audience. The presentatio
n is principally based on the recent article:\n\nLindstrom, Scott B. "Comp
utable centering methods for spiraling algorithms and their duals, with mo
tivations from the theory of Lyapunov functions." arXiv preprint arXiv:200
1.10784 (2020).\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Code Representation Learning with Prüfer Sequences f
rom Tenzin Jinpa
DTSTART:20211006T180000Z
DTEND:20211006T190000Z
UID:21
LOCATION:https://ubc.zoom.us/j/63061009858?pwd=OFpsSzNuMG04dVpyNzNja0dCMHYw
Zz09
DESCRIPTION:Te
nzin Jinpa\nTitle: Code Representation Learning with Prüfer Sequences
\nLocation: https://ubc.zoom.us/j/63061009858?pwd=OFpsSzNuMG04dVpyNzNja0dC
MHYwZz09\nAbstract: An effective and efficient code representation is crit
ical to the success of sequence-to-sequence deep neural network models for
a variety of tasks in code understanding, such as code summarization and
documentations, improving productivity, and reducing software development
costs. Unlike the natural language, which is unstructured and noisy, progr
amming codes are intrinsically structured, and the learning model can leve
rage this property of the code. A significant challenge is to find a seque
nce representation that captures the structural information in the program
code and facilitates the training of the models.\n\nIn this study, we pro
pose to use the Prüfer sequence of the Abstract Syntax Tree (AST) of a co
mputer program to design a sequential representation scheme that preserves
the structural information in an AST. Our representation makes it possibl
e to develop deep-learning models in which signals carried by lexical toke
ns in the training examples can be exploited automatically and selectively
based on their syntactic role and importance. Unlike other recently propo
sed approaches, our representation is concise and lossless in terms of the
structural information of the AST. To test the efficacy of Prüfer-sequen
ce-based representation, we designed a code summarization using a sequence
-to-sequence learning model on real-world benchmark datasets. The results
from the empirical studies show that Prüfer-sequence-based representation
is indeed highly effective and efficient, outperforming significantly all
the recently-proposed deep-learning models we used as the baseline models
.\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Task-Based Parameter Isolation for Foreground Segment
ation without Catastrophic Forgetting using Multi-Scale Region and Edges F
usion Network from Islam Osman
DTSTART:20211103T180000Z
DTEND:20211103T190000Z
UID:22
LOCATION:https://ubc.zoom.us/j/65964019985?pwd=bnZTaG90OGh0ODVYaklQZGJFbWlE
Zz09
DESCRIPTION:Islam Osman\nTitle: Task-Based Parameter Isolati
on for Foreground Segmentation without Catastrophic Forgetting using Multi
-Scale Region and Edges Fusion Network\nLocation: https://ubc.zoom.us/j/65
964019985?pwd=bnZTaG90OGh0ODVYaklQZGJFbWlEZz09\nAbstract: Foreground segme
ntation of moving objects is widely used in different computer vision appl
ications\; however, existing deep learning-based methods generally suffer
from overall degraded F-measure performance. The two main sources that deg
rade the F-measure are under-segmentation and catastrophic forgetting. Und
er-segmentation is the problem of not capturing objects' fine details. The
catastrophic forgetting problem occurs when training on a large number of
video sequences that leads to forgetting information learned from early v
ideo sequences. This paper proposes a novel multi-scale region and edges f
usion network with task-based parameter isolation (REFNet-TBPI) to overcom
e these two problems. The proposed method consists of a novel multi-scale
region and edges fusion network (REFNet) to capture the moving objects' bo
undary details by extracting regions and boundary edges of each object at
different feature scales and fusing them to produce high-detailed segmente
d objects. REFNet is trained using a continual learning technique called t
ask-based parameter isolation (TBPI) to overcome the catastrophic forgetti
ng problem.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Q/A from Hansi Singh
DTSTART:20211116T200000Z
DTEND:20211116T203000Z
UID:23
LOCATION:https://ubc.zoom.us/j/63425637433?pwd=cStxWWZMeHJoLzFPeGpORnhFcDFW
QT09
DESCRIPTION:Hansi Singh\nTitle:
Q/A\nLocation: https://ubc.zoom.us/j/63425637433?pwd=cStxWWZMeHJoLzFPeGpO
RnhFcDFWQT09\nAbstract: NA
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Modelling Rare Blood in Canada from John Blake
DTSTART:20211025T173000Z
DTEND:20211025T183000Z
UID:24
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:John Blake\nTitle: Modelling Rare Blood in Canad
a\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ
6YTRtdz09\nAbstract: BACKGROUND: Many countries maintain rare blood progr
ams to provide access to blood for patients with complex serology. These
include a process to screen donors and a registry to record information ab
out rare donors. Blood agencies may also freeze rare blood. However, froze
n blood storage is 2-10 times more expensive than liquid blood. \n\nSTUDY
DESIGN AND METHODS: A two-phase approach to analysis was used to evaluate
how rare a blood type must be before a frozen inventory is necessary and
what screening rates are required to support a rare blood program. A simul
ation model was employed to evaluate the impact of inventory on patient ac
cess.\n\nRESULTS: Results suggested that, for 24 of 29 named phenotypes m
anaged by Canadian Blood Services, insufficient donors had been identified
to ensure a stable inventory. Analytic results showed the screening rate
necessary to ensure a stable inventory and the timeframe to build a rare
donor base.\n29 simulation scenarios were executed to evaluate patient acc
ess to rare blood against inventory levels. Simulation results show that s
ome amount of frozen inventory is necessary for phenotypes rarer than 1 in
3,000. However, holding more than 2 units apiece of (O-, O+, A-, A+) did
not improve patient access.\n\nCONCLUSION: While some level of frozen blo
od is needed for rare blood, large inventories do not improve access. Mode
st amounts of frozen inventory, combined with increased door screening, pr
ovides the greatest chance of maximizing patient access.\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Scheduling with communication delays from Sami Davi
es
DTSTART:20211108T183000Z
DTEND:20211108T193000Z
UID:25
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sami Davies\nTitle: Schedu
ling with communication delays\nLocation: https://sfu.zoom.us/j/6632213855
7?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: We study scheduling with
precedence constraints and communication delays. Here, if two dependent j
obs are scheduled on different machines, then c time units must pass betw
een their executions. Previously, the best known approximation ratio was O
(c), though an open problem in the top-10 list by Schuurman and Woeginger
asks whether there exists a constant-factor approximation algorithm. We gi
ve a polynomial-time O(log c * log m)-approximation algorithm when given m
identical machines and delay c for minimizing makespan. Our approach uses
a Sherali-Adams lift of an LP relaxation and a clustering of the semimetr
ic space induced by the lift. We also (1) obtain similar polylogarithmic a
pproximations on related machines with the objective of minimizing the wei
ghted sum of completion times and (2) found that if the delay can vary bet
ween pairs of dependent machines, then a constant factor approximation alg
orithm is not possible (assuming NP complete problems do not admit quasi-p
olynomial time algorithms).
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Artificial Neural Networks with Non-iterative Learnin
g Algorithm: From Single-Layer Network to Hierarchical Networks from Dr. Y
imin Yang
DTSTART:20211117T190000Z
DTEND:20211117T200000Z
UID:26
LOCATION:https://ubc.zoom.us/j/63061009858?pwd=OFpsSzNuMG04dVpyNzNja0dCMHYw
Zz09
DESCRIPTION:Dr. Yimin Yang\nTitle:
Artificial Neural Networks with Non-iterative Learning Algorithm: From Si
ngle-Layer Network to Hierarchical Networks\nLocation: https://ubc.zoom.us
/j/63061009858?pwd=OFpsSzNuMG04dVpyNzNja0dCMHYwZz09\nAbstract: The iterati
ve method of learning has become a paradigm for training artificial neural
networks (ANN). However, utilizing a non-iterative learning strategy can
accelerate the training process of the ANN. This motivates us to introduce
a non-iterative learning strategy that eliminates the purpose of iteratio
n process in an ANN, resulting in lower training time with competitive per
formance. This talk will focus on how to use non-iterative learning strate
gy to train a single layer network, and a hierarchical network, and a deep
network. The detailed experimental results with the proposed methods incl
uding generalization performance as well as learning speed will also be di
scussed. In addition, future research direction will also be presented in
the talk.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Global Climate and the Ocean: Understanding Coup
led Dynamics through a Unified Framework of Energetics and Feedbacks from
Hansi Singh
DTSTART:20211116T190000Z
DTEND:20211116T200000Z
UID:27
LOCATION:https://ubc.zoom.us/j/66623530555?pwd=S1RuWlh3cWIvV0t1Z084RnNuRnk5
dz09
DESCRIPTION:Hansi Singh\nTitle:
Global Climate and the Ocean: Understanding Coupled Dynamics through a Un
ified Framework of Energetics and Feedbacks\nLocation: https://ubc.zoom.us
/j/66623530555?pwd=S1RuWlh3cWIvV0t1Z084RnNuRnk5dz09\nAbstract: The Earth's
climate arises from a complex interplay between its various components: t
he atmosphere, ocean, land surface, and ice. In this talk, I present a ser
ies of studies showing the extent to which coupled dynamics, namely intera
ctions between the ocean and atmosphere, drive the evolution of the climat
e system and its sensitivity to forcings. In these studies, I use state-of
-the-art Earth system models to show that the ocean and its dynamics impac
t climate sensitivity (how much the planet warms with CO2-doubling), preci
pitation sensitivity (how much precipitation changes per unit of warming),
and surface climate variability over a range of time scales. I conclude b
y describing how better understanding of such coupled (ocean-atmosphere) d
ynamics are likely key for designing optimal climate intervention strategi
es, which may help avert some of the irreversible impacts of planetary war
ming as human societies begin the slow transition away from fossil fuels.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Image Reconstruction: Superiorization versus Regul
arization from Warren Hare
DTSTART:20211122T183000Z
DTEND:20211122T193000Z
UID:28
LOCATION:https://ubc.zoom.us/j/69073057434?pwd=WUprZnU3NE9wbWdnVWNRT0Z0SDE4
Zz09
DESCRIPTION:War
ren Hare\nTitle: Image Reconstruction: Superiorization versus Regular
ization\nLocation: https://ubc.zoom.us/j/69073057434?pwd=WUprZnU3NE9wbWdnV
WNRT0Z0SDE4Zz09\nAbstract: Image reconstruction plays an important role in
a number of modern activities, such as experimental verification of radio
therapy. \nMathematically, image reconstruction can be viewed as a least s
quares problem, but with the caveat that the `optimal' solution is not nec
essarily the cleanest image. Several approaches have been suggested to de
al with this issue. In this talk, we review some of these approaches and
present the results of comprehensive numerical testing comparing different
approaches.\n\n\nbased on Joint work with M. Guenter, S. Collins, A. Ogil
vy, A. Jirasek\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: MATH 590: graduate presentations from MATH 590
DTSTART:20211123T190000Z
DTEND:20211123T210000Z
UID:30
LOCATION:https://ubc.zoom.us/j/69780744256?pwd=TStzZDJlWW9TYnZtajJrSjlKUlQ1
Zz09
DESCRIPTION:MATH 590\nTitle: MATH 590: graduate presentation
s\nLocation: https://ubc.zoom.us/j/69780744256?pwd=TStzZDJlWW9TYnZtajJrSjl
KUlQ1Zz09\nAbstract: 11:00-11:30 Ziyuan Wang\nError bounds, quadratic grow
th, and linear convergence of prox-linear algorithm\n\n11:30-12:00 Alex Ia
nnantuono\nBenson's Algorithm: An Outer Approximation Algorithm for Genera
ting All Efficient Extreme Points in the Outcome Set of a Multiple Objecti
ve Linear Programming Problem\n\n12:00-12:30 Tareque Hossian\nImpact of Va
rying Community Networks on Disease Invasion\n\n12:30-13:00 Stephanie Hami
lton\nA Review of Improved filtering for the bin-packing with cardinality
constraint
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: MATH 590: graduate presentations from MATH 590
DTSTART:20211130T190000Z
DTEND:20211130T203000Z
UID:31
LOCATION:https://ubc.zoom.us/j/69780744256?pwd=TStzZDJlWW9TYnZtajJrSjlKUlQ1
Zz09
DESCRIPTION:MATH 590\nTitle: MATH 590: graduate presentation
s\nLocation: https://ubc.zoom.us/j/69780744256?pwd=TStzZDJlWW9TYnZtajJrSjl
KUlQ1Zz09\nAbstract: 11:00-11:30 Sarah Wyse\nThe story of structural sensi
tivity\n\n11:30-12:00 Neha Bansal\nUnderstanding Invariant Function and Me
asure for Discrete Time Markov Process via Renewal Approach\n\n12:00-12:30
Alex (Xiaoyu) Mao\nOptimization of Inf-Convolution Regularized Nonconvex
Composite Problems\n
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: SGD in the Large: Average-case Analysis, Asymptotic
s, and Stepsize Criticality from Courtney Paquette
DTSTART:20211206T183000Z
DTEND:20211206T193000Z
UID:32
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Courtney Paquette\n
Title: SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize
Criticality\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNU
GU5aGs3TUZ6YTRtdz09\nAbstract: In this talk, I will present a framework, i
nspired by random matrix theory, for analyzing the dynamics of stochastic
gradient descent (SGD) when both the number of samples and dimensions are
large. Using this new framework, we show that the dynamics of SGD on a lea
st squares problem with random data becomes deterministic in the large sam
ple and dimensional limit. Furthermore, the limiting dynamics are governed
by a Volterra integral equation. This model predicts that SGD undergoes a
phase transition at an explicitly given critical stepsize that ultimately
affects its convergence rate, which we also verify experimentally. Finall
y, when input data is isotropic, we provide explicit expressions for the d
ynamics and average-case convergence rates. These rates show significant i
mprovement over the worst-case complexities.
DTSTAMP:20211205T081730Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Improving Stroke Routing Protocol from Dr. Amir Ard
estani-Jaafari
DTSTART:20220124T183000Z
DTEND:20220124T193000Z
UID:29
LOCATION:https://ubc.zoom.us/j/66022642862?pwd=dU4wMkkvSmNheWYwQVpUekVBN0l3
Zz09
DESCRIPTION:Dr. Amir Ardestani-Jaa
fari\nTitle: Improving Stroke Routing Protocol\nLocation: https://ubc.
zoom.us/j/66022642862?pwd=dU4wMkkvSmNheWYwQVpUekVBN0l3Zz09\nAbstract: Curr
ently, stroke patients are transported to the nearest stroke center. Many
factors, including the spatial variation in population density, the stroke
's severity, the time since stroke onset, and the congestion level at the
receiving stroke center are not considered in the current protocol. We dev
elop an analytical framework that enriches the stroke transport decision-m
aking process by incorporating these factors. Applying our framework to th
e city of Montreal Stroke Network, we show that adopting a triage strategy
leads to significantly improved health outcomes.\n\nKeywords: Stroke, Que
uing Optimization, Congestion, Facility Location\n
DTSTAMP:20220117T190038Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Personalized, Expandable Learning Modules for CS1 Pro
gramming from Abdallah Mohamed, Thomas, Van De Crommenacker
DTSTART:20220129T000000Z
DTEND:20220129T010000Z
UID:37
LOCATION:https://ubc.zoom.us/j/7562761523?pwd=TXRCM3AyRVBZZ2c2OW90N3dtS2E4d
z09
DESCRIPTION:Abdallah Moha
med, Thomas, Van De Crommenacker\nTitle: Personalized, Expandable Lear
ning Modules for CS1 Programming\nLocation: https://ubc.zoom.us/j/75627615
23?pwd=TXRCM3AyRVBZZ2c2OW90N3dtS2E4dz09\nAbstract: Computer programming is
a crucial, cross-disciplinary skill that has become important to audience
s beyond merely computer scientists. Records from the past few years at UB
CO show that students from BSc, BA, BASc, BFA, BHK, BMgt, BMS, BSN, MA, an
d MEng were registered in one or more first-year programming courses. Furt
her, several researchers seek to learn the fundamentals of computer-progra
mming to help them implement their research ideas and algorithms. Such mix
of students have different abilities and needs.\n\nIn our presentation, w
e will introduce a new learning hub that has several modules to cover the
fundamentals of computer programming with two key innovations: \n a) Per
sonalized Learning: Learners have the flexibility to customize the learnin
g modules to meet their needs. For each module, learners can choose: (a) p
rogramming language (Java, Python), (b) application domain for coding exer
cises (math, stats, and physics), and (c) website language (English, Frenc
h), and (d) depth of learning material (Basics or Advanced), \n b) Modu
les Expansion and Reusability: instructors can \n i. create new lea
rning modules \n ii. expand existing modules by adding more options
under the four customization attributes listed above. For example, instruc
tors can add C++ option under the “programming language” attribute, th
en they only need to modify the parts in the modules that are related to t
he new customization option, e.g. append C++ code to replace Java or Pytho
n code.\n\nDigital badges are used to track and document student progress
and professional growth at different levels of competencies.\n\nThe system
has been used in COSC 111 and can be reached at learncoding.ok.ubc.ca (th
rough UBC VPN).\n
DTSTAMP:20220119T205223Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Generating New Software Artifacts Automatically from
Existing Data from Fuxiang Chen
DTSTART:20220212T000000Z
DTEND:20220212T010000Z
UID:35
LOCATION:https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBuNHFVNHZvYk1FVnNBT0w3
UT09
DESCRIPTION:Fu
xiang Chen\nTitle: Generating New Software Artifacts Automatically fro
m Existing Data\nLocation: https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBuN
HFVNHZvYk1FVnNBT0w3UT09\nAbstract: In many software projects, the two most
created and modified artifacts are code (semi-structured) and natural lan
guage text (unstructured) – developers are spending a lot of time on cre
ating and modifying them. One of the common unstructured artifacts is code
comments. Code comments are short descriptions about code and they are im
portant for software comprehension. External to the software projects, a l
ot of unstructured artifacts are also created during the software lifecycl
e. A common avenue is through Stack Overflow, a forum where developers can
ask technical questions (in natural language text) about their problems a
nd have them answered by experts (also in natural language text). \n\nTo i
mprove the developers' productivity and effectiveness, in this seminar, we
will discuss how we leveraged the existing abundance of data to generate
new artifacts for developers automatically. Specifically, we will introduc
e two of our recent works that attempt to generate 1) code from natural la
nguage text, and 2) natural language text from code. Finally, we will shar
e some of the current work.\n
DTSTAMP:20220129T024605Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: I have data and a business problem\; now what? Automa
ted Machine Learning and Explainability (AutoMLx) at Oracle Labs from Yash
a Pushak
DTSTART:20220226T000000Z
DTEND:20220226T010000Z
UID:36
LOCATION:https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBuNHFVNHZvYk1FVnNBT0w3
UT09
DESCRIPTION:Yasha Pushak\nTitle: I have data and a business
problem\; now what? Automated Machine Learning and Explainability (AutoMLx
) at Oracle Labs\nLocation: https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBu
NHFVNHZvYk1FVnNBT0w3UT09\nAbstract: In the last few decades, machine learn
ing has made many great leaps and bounds, thereby substantially improving
the state of the art in a diverse range of industry applications. However,
for a given dataset and a business use case, non-technical users are face
d by many questions that limit the adoption of a machine learning solution
. For example:\n• "Which machine learning model should I use?"\n• "How
should I set its hyper-parameters?"\n• "Can I trust what my model learn
ed?"\n• "Does my model discriminate against a marginalized, protected gr
oup?"\nEven for seasoned data scientists, answering these questions can be
tedious and time consuming. To address these barriers, the AutoMLx team a
t Oracle Labs has developed an automated machine learning (AutoML) pipelin
e that performs automated feature engineering, preprocessing and selection
, and then selects a suitable machine learning model and hyper-parameter c
onfiguration. To help users understand and trust their "magic" and opaque
machine learning models, the AutoMLx package supports a variety of methods
that can help explain what the model has learned.\n\nIn this talk, we wil
l provide an overview of our current AutoMLx methods\; we will comment on
open questions and our active areas of research\; and we will briefly revi
ew the projects of our sister teams at Oracle Labs. Finally, in this talk
we will briefly reflect on some of the key differences between research in
a cutting-edge industry lab compared with research in an academic setting
.\n
DTSTAMP:20220129T025112Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Formulation and solution of stochastic inverse p
roblems for science and engineering models from Dr Donald Estep
DTSTART:20220215T000000Z
DTEND:20220215T010000Z
UID:44
LOCATION:https://ubc.zoom.us/j/62574960025?pwd=M0RPYTZ3SlZLaEl5b1RTR0p0alNk
Zz09
DESCRIPTION:Dr Donald Estep\nTitle
: Formulation and solution of stochastic inverse problems for science and
engineering models\nLocation: https://ubc.zoom.us/j/62574960025?pwd=M0RPYT
Z3SlZLaEl5b1RTR0p0alNkZz09 \nAbstract: Determining information about the s
tate of a complex physical system from observations of its behavior is a f
undamental problem in scientific inference and engineering design. Often,
this can be formulated as the stochastic inverse problem of determining pr
obability structures on parameters for a physics model corresponding to a
probability structure on the output of the model. We describe the formulat
ion and solution of stochastic inverse problems. Our approach yields a com
putationally tractable problem while avoiding alterations of the model lik
e regularization and ad hoc assumptions about the probability structures.
We present several examples, including a high-dimensional application to d
etermination of parameter fields in storm surge models. We describe severa
l extensions and on-going research.
DTSTAMP:20220203T014218Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Stable, accurate and efficient deep neural networks
for reconstruction of gradient-sparse images from Maksym Neyra-Nesterenko
DTSTART:20220214T183000Z
DTEND:20220214T193000Z
UID:39
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Maksym Neyra-Nesterenko \nT
itle: Stable, accurate and efficient deep neural networks for reconstructi
on of gradient-sparse images\nLocation: https://sfu.zoom.us/j/66322138557?
pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Deep learning has recently
shown substantial potential to outperform standard methods in compressive
imaging, an inverse problem where one reconstructs an image from highly u
ndersampled measurements. Given compressive imaging arises in many importa
nt applications, such as medical imaging, the potential impact of deep lea
rning is significant. Empirical results indicate deep learning achieves su
perior accuracy on data for such tasks. However, a theoretical treatment e
nsuring the stability of deep learning for compressive imaging is mostly a
bsent in the current literature. In fact, many existing deep neural networ
ks designed for these tasks are unstable and fail to generalize.\n\nIn thi
s talk, we present a novel construction of accurate, stable and efficient
neural networks to reconstruct images under a gradient sparsity model. Thi
s is based on recent work by Adcock et al. (2021) and Antun et al. (2021).
We construct the network by unrolling an optimization algorithm similar t
o NESTA. We utilize a compressed sensing analysis to prove accuracy and st
ability of the network. This shows that deep neural networks are capable o
f performing as well as state-of-the-art compressive imaging techniques fo
r gradient-sparse images. A restart scheme enables exponential decay of th
e required network depth, yielding a shallower network. In turn this reduc
es computational costs, making the network feasible for fast image reconst
ruction.\n\nThis is joint work with Ben Adcock.\n
DTSTAMP:20220208T155947Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: Estimating fire spread from micro-fire experiment data
using anisotropic smoothing techniques from Dr John Thompson
DTSTART:20220214T170000Z
DTEND:20220214T180000Z
UID:43
LOCATION:https://ubc.zoom.us/j/69248291814?pwd=NnhCMkZSOXRUcGluVGFIdk56YXNN
Zz09
DESCRIPTION:Dr John Thompson\nTitle: Estimating fire spread
from micro-fire experiment data using anisotropic smoothing techniques\nLo
cation: https://ubc.zoom.us/j/69248291814?pwd=NnhCMkZSOXRUcGluVGFIdk56YXNN
Zz09\nAbstract: Wildfires in North America have been a hot topic since 201
8 due to the extreme fire seasons in California,\nBritish Columbia, and Al
berta. Each fire was unique, with environmental conditions that vary withi
n and\nbetween fires, and so with different variabilities in the fire spre
ad rates. The ability to estimate the distribution\nof fire spread rates f
rom imagery is crucial to understanding the scenarios of spread rates over
a\nregion. Research will be presented on statistical methods used to esti
mate fire spread rate and spread rate\nvariability from fire spread image
data. Measured results obtained in micro-fire experiments will be compared
\nto estimates from nonparametric statistical methods. More specifically,
anisotropic smoothing was applied to\nimages to remove noise while preserv
ing the fire boundaries. Results demonstrate the effectiveness of the\nest
imation methodology, and also, that the mean and variability of fire sprea
d rate is affected by temporal and\nenvironmental factors.
DTSTAMP:20220211T225504Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: Number systems from Dr Kevin Hare
DTSTART:20220314T160000Z
DTEND:20220314T170000Z
UID:45
LOCATION:https://ubc.zoom.us/j/69248291814?pwd=NnhCMkZSOXRUcGluVGFIdk56YXNN
Zz09
DESCRIPTION:Dr Kevin Hare\nTitle: Number systems\nLocation:
https://ubc.zoom.us/j/69248291814?pwd=NnhCMkZSOXRUcGluVGFIdk56YXNNZz09\nAb
stract: Typically when we think of a number system, we think of the base 1
0 number system with digits 0, 1, …, 9. For example, 1729 = 1 * 10^3 +
7 * 10^2 + 2 * 10^1 + 9 * 10^0 and \n Pi = 3 * 10^0 + 1 * 10^{-1} + 4
* 10^{-2} + 1 * 10^{-3} … .\nThis is a special case of a very general c
onstruction. Here d need not be an integer greater than 1. It could be a
real number, or negative, or complex, or a matrix. The set of digits cou
ld be any subset of real or complex numbers, vectors from R^n, etc.\n\nIn
this talk we will survey a number of results of number systems, as well as
connections to various areas as number theory, fractal geometry and quasi
-crystals.\n
DTSTAMP:20220216T192233Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Feasibility-based structured nonconvex optimization
(Manish Krishan-Lal) / Positive bases with maximal cosine measure (Gabrie
l Jarry-Bolduc) from Manish Krishan-Lal /Gabriel Jarry-Bolduc
DTSTART:20220228T183000Z
DTEND:20220228T193000Z
UID:41
LOCATION:https://ubc.zoom.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJx
UT09
DESCRIPTION:Manish Krishan-
Lal /Gabriel Jarry-Bolduc\nTitle: Feasibility-based structured nonconv
ex optimization (Manish Krishan-Lal) / Positive bases with maximal cosine
measure (Gabriel Jarry-Bolduc)\nLocation: https://ubc.zoom.us/j/6521069192
2?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJxUT09\nAbstract: Feasibility-based struct
ured nonconvex optimization: We present the onset of the theory to find cl
osed-form projection onto conics and quadrics in Hilbert spaces. These set
s are crucial in many applications. We will focus on a feasibility-based f
ramework that utilizes these sets, to train a neural network with projecti
on methods.\n\nPositive bases with maximal cosine measure: The properties
of positive bases make them a useful tool in derivative-free optimization
and an interesting concept in mathematics. The notion of cosine measure h
elps to quantify the quality of a positive basis. It provides information
on how well the vectors in the positive basis uniformly cover the space co
nsidered. In this talk, we present recent progress on how to compute the c
osine measure of a given positive basis and the structure of a positive ba
sis with maximal cosine measure.
DTSTAMP:20220224T155826Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Non-realizability of polytopes via linear programmi
ng from Amy Wiebe
DTSTART:20220314T173000Z
DTEND:20220314T183000Z
UID:46
LOCATION:https://ubc.zoom.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJx
UT09
DESCRIPTION:Amy Wiebe\nTitle: Non-re
alizability of polytopes via linear programming\nLocation: https://ubc.zoo
m.us/j/65210691922?pwd=OVBkOWU5VDloR0FNVDdrbVB5TWJxUT09\nAbstract: A class
ical question in polytope theory is whether an abstract polytope can be re
alized as a concrete convex object. Beyond dimension 3, there seems to be
no concise answer to this question in general. In specific instances, answ
ering the question in the negative is often done via “final polynomials
introduced by Bokowski and Sturmfels. This method involves finding a po
lynomial which, based on the structure of a polytope if realizable, must b
e simultaneously zero and positive, a clear contradiction. The search spac
e for these polynomials is ideal of Grassmann-Plücker relations, which qu
ickly becomes too large to efficiently search, and in most instances where
this technique is used, additional assumptions on the structure of the de
sired polynomial are necessary. \n\nIn this talk, I will describe how by c
hanging the search space, we are able to use linear programming to exhaust
ively search for similar polynomial certificates of non-realizability with
out any assumed structure. We will see that, perhaps surprisingly, this el
ementary strategy yields results that are competitive with more elaborate
alternatives and allows us to prove non-realizability of several interesti
ng polytopes. \n
DTSTAMP:20220301T002819Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: ᐁ ᐃᓯ ᒫᒥᑐᓀᔨᐦᑕᒫᐣ ᓂᒥᑭᓯ
ᑕᐦᐃᑫᐏᐣ ᒫᒥᑐᓀᔨᐦᐃᒋᑲᓂᐦᑳᓂᕽ ê-isi-mâm
itonêyihtamân nimikisistahikêwin mâmitonêyihicikanihkânihk Indigenou
s Cultures of Programming: Decolonizing computer programming through langu
age and ceremony from Jon M. R. Corbett
DTSTART:20220312T000000Z
DTEND:20220312T010000Z
UID:34
LOCATION:https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBuNHFVNHZvYk1FVnNBT0w3
UT09
DESCRIPTION:Jon M. R. Corbett\nTitle: ᐁ ᐃᓯ ᒫᒥᑐᓀᔨᐦᑕᒫᐣ ᓂᒥᑭᓯᐢᑕᐦᐃ
ᑫᐏᐣ ᒫᒥᑐᓀᔨᐦᐃᒋᑲᓂᐦᑳᓂᕽ ê-isi-mâmitonêyiht
amân nimikisistahikêwin mâmitonêyihicikanihkânihk Indigenous Cultures
of Programming: Decolonizing computer programming through language and ce
remony\nLocation: https://ubc.zoom.us/j/64145809474?pwd=MFBpZVBuNHFVNHZvYk
1FVnNBT0w3UT09\nAbstract: Computing practices have a long history of refle
cting western science paradigms and have become indirectly responsible for
the continued erosion of Indigenous languages and cultures. For example,
high-level programming languages in modern computing extensively use the R
oman alphabet, the Hindu-Arabic numeral system, and so-called “global”
Englishes as the prevailing standards for computer language development (
Nasser 2018). Conversely, Indigenous worldviews and practices often exist
at the opposite end of Western science and technology, rooted in harmonies
based on knowledge developed through land, spirit, and ceremony. Due to t
he dominance of western philosophies in computing practices, I assert a he
terodox view of computing for Indigenous people that requires a significan
t shift in existing computing philosophies to accommodate Indigenous cultu
ral representations better. To that end, I subscribe to the ideology that
computer programming should not be viewed as a purely functional practice
but as ceremonial. Many Indigenous peoples view language (their language)
as medicine, and therefore, computer programming by extension is medicine,
allowing culturally specific and significant meaning to be embedded in th
e system.\n\nThis presentation introduces my approach to indigenizing comp
uter programming using nehiyawewin, the language of the Plains Cree, with
an Indigenous Storywork methodology (Archibald 2008) as a base for a new c
omputing framework and programming language – that I collectively refer
to as “Ancestral Code.”\n\nI will discuss my Indigenous Computing Fram
ework and associated Indigenous Digital Media Toolkit that I aim at unitin
g four specific academic domains: Indigenous Studies (cultural revitalizat
ion), Computer Science, Digital Humanities including Linguistics (language
documentation and resurgence), and Fine Art. Through this exploration, I
consciously entwine Indigenous cultural practices and meanings with comput
er coding practices creating a cultural-digital-connection between the use
r and the machine.\n\nThe Ancestral Code computing framework is a wholisti
c system I propose as a computational solution to capture, transcribe, par
se, process, and transform Indigenous histories/stories (both oral and wri
tten) into programmatic code that can create generative digital artwork. I
t contains a programming language with a specialized user interface that u
ses nehiyaw language, syllabic orthography, storytelling, and cultural and
ceremonial practices as programmatic inputs to produce digital artworks.
This platform, therefore, provides a digital template for the archiving, m
aintenance, and revitalization of Indigenous cultures and languages by not
only collecting Indigenous language and knowledge contained within storyw
ork, but promotes the continued use and development of Indigenous language
and facilitates the digital encoding and storage of audio, video, and tex
t for cultural survivance. \n\nReferences\n- Archibald, J.A. 2008. Indigen
ous Storywork: Educating the Heart, Mind, Body, and Spirit. Vancouver, BC:
UBC Press.\n- Nasser, Ramsey. 2018. “A Personal Computer for Children o
f All Cultures.” In Decolonising the Digital: Technology As Cultural Pra
ctice, 21–36. Sydney: Tactical Space Lab. http://ojs.decolonising.digita
l/index.php/decolonising_digital/article/view/PersonalComputer. \n\nThis t
itle was inspired by Tomas Petricek’s unpublished paper “Cultures of P
rogramming: Understanding the history of programming through controversies
and technical artifacts.” (2019)\n
DTSTAMP:20220301T173850Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: A Colorful Steinitz Lemma Applied to Block Integer
Programs from Joseph Paat
DTSTART:20220328T173000Z
DTEND:20220328T183000Z
UID:40
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Joseph P
aat \nTitle: A Colorful Steinitz Lemma Applied to Block Integer Progra
ms\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TU
Z6YTRtdz09\nAbstract: Block integer programs (IPs) model a wide range of p
roblems including those in social choice, scheduling, and stochastic optim
ization. Recently, algorithms for block IPs have been improved by using th
e so-called Steinitz Lemma, which is a statement about the rearrangement o
f a set of vectors. In this work, we develop a variation of the Steinitz L
emma that rearranges multiple sets simultaneously. We then demonstrate how
our variation can be used to derive new results for block IPs.\n\nThis is
joint work with Timm Oertel and Robert Weismantel.
DTSTAMP:20220318T211104Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Predicting and Explaining Semantic Segmentation Neura
l Network Architecture Performance and Diversity from Brandon Graham-Knigh
t
DTSTART:20220401T230000Z
DTEND:20220402T000000Z
UID:33
LOCATION:https://ubc.zoom.us/j/68109679570?pwd=alZlN2pNSGxDVHZsUG4reTdua1ZP
UT09
DESCRIPTION:Brandon Graham-Knight\nTitle: Predicting and Exp
laining Semantic Segmentation Neural Network Architecture Performance and
Diversity\nLocation: https://ubc.zoom.us/j/68109679570?pwd=alZlN2pNSGxDVHZ
sUG4reTdua1ZPUT09\nAbstract: We explain prediction performance and diversi
ty of various network sizes and activation functions applied to semantic s
egmentation of the CityScapes dataset. We show that both performance and
diversity can be predicted from network architecture using explainable boo
sting machines. Tukey's test shows that many of the models exhibit no sig
nificant difference in mean performance within a 95% confidence interval,
and that many of the best performing models have different network archite
ctures. The explanations for performance largely agree with the accepted
wisdom of the machine learning community, which shows that the method is e
xtracting information of value. We find that diversity between models is
best achieved with varied network size. Homogeneous network size paramete
rs generally show positive correlation, and larger models tend to converge
to similar solutions These explanations provide better understanding o
f the effects of network parameters to deep learning practitioners\; they
could also be used in place of naïve search methods or a model pool to in
form growing an ensemble.
DTSTAMP:20220328T202919Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Geometry of All Pivot Rules for the Simplex Me
thod from Jesus de Loera
DTSTART:20220404T173000Z
DTEND:20220404T183000Z
UID:42
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jesus de Loera
\nTitle: The Geometry of All Pivot Rules for the Simplex Method\nLoc
ation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtd
z09\nAbstract: The simplex method is one of the most famous and popular al
gorithms in optimization. Purely geometrically, a linear program (LP) is a
polyhedron together with an orientation of its graph. The famous simplex
method selects a path from an initial vertex to the sink (optimum) and th
us determines an arborescence (of shortest monotone paths). The engine of
any version of the simplex method is a pivot rule that selects the outgoin
g arc for a current vertex. Pivot rules come in many forms and types, but
after 80 years we are still ignorant whether there is one that can make th
e simplex method run in polynomial time. Motivated by this question we tri
ed to understand the structure of all pivot rules for a linear program.\n\
nClassic result in parametric linear optimization explain the changes of o
bjective function, eg the orientation and Sink and for a pivot rule its ar
borescence. I present a type of parametric analysis for all pivot rules be
longing to a certain class, memoryless pívot rules, we associate polytope
s that parametrize memoryless pívot rules of a given LP, an attempt to ge
t a geometric/topological picture of a “space of all pivot rules of an
LP”. This story is related to performance of pivot rules, parametric lin
ear programs, shadow-vertex-rules, and several classic polyhedral geometry
and is joint work with Alex Black, Niklas Lu ̈tjeharms, and Raman Sanya
l. \n
DTSTAMP:20220330T200239Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:URC seminar: Undergraduate Research Conference Data, Math, Physics,
and Stats from Students
DTSTART:20220412T170000Z
DTEND:20220412T193200Z
UID:47
LOCATION:https://ubc.zoom.us/j/64180515358?pwd=RU9oZDh3azRpMXJqWjFONkI5VVdV
QT09
DESCRIPTION:Students\nTitle: Undergraduate Research Conferen
ce Data, Math, Physics, and Stats\nLocation: https://ubc.zoom.us/j/6418051
5358?pwd=RU9oZDh3azRpMXJqWjFONkI5VVdVQT09\nAbstract: Time Speaker
Supervisor Discipline\n10:00 – 10:20 S
am Ruttiman John Hopkinson Physics\n10:20 – 10:2
2\n10:22 – 10:42 Maya Patel John Hopkinson/ Hiroko Nakahara Phy
sics\n10:42 – 10:44\n10:44 – 11:04 Emily Mellors Peter Simpson
Physics\n11:04 – 11:06\n11:06 – 11:26 Ian Kennedy
Jake Bobowski Physics\n11:26 – 11:28\n11:28 – 1
1:48 Amaan Ali Khan Eric Foxall Math\n11:48 – 1
1:50\n11:50 – 12:10 Aanchal Kuckian Paramjit Gill/ Rasika Rajapakshe
Data\n12:10 – 12:12\n12:12 – 12:32 Yue (Cassie) Zhang Xiaoping Shi
Statistics
DTSTAMP:20220413T154157Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:URC seminar: Computer Science Honours thesis presentations from URC
students
DTSTART:20220427T164500Z
DTEND:20220427T223000Z
UID:48
LOCATION:https://ubc.zoom.us/j/61698995381?pwd=MHFlSndxSkEzUmVUYnFUbkRheGNG
dz09
DESCRIPTION:URC students\nTitle: Computer Science Honours th
esis presentations\nLocation: https://ubc.zoom.us/j/61698995381?pwd=MHFlSn
dxSkEzUmVUYnFUbkRheGNGdz09\nAbstract: Department Undergraduate Research Co
nference\nDay 2: Computer Science Honours Thesis Presentations\nWednesday,
April 27, 2022, 9:45am – 3:30pm\nLocation: SCI 247 or https://ubc.zoom.
us/j/61698995381?pwd=MHFlSndxSkEzUmVUYnFUbkRheGNGdz09\nWelcome: 9:45am –
10:00am\n\nSession 1: 10:00am – 11:00am Chaired by: Dr. Ramon Lawrence\
n10:00am – 10:15am Shawn Zhao: Machine Learning in Medical Physics (Supe
rvisor: Dr. Jeff Andrews and Dr. Apurva Natayan)\n10:15am – 10:30am Abhi
neeth Adiraju: Adaptive Educational Video Recommendation System (Superviso
r: Dr. Bowen Hui)\n10:30am – 10:45am Devina Jaiswal: QCQP Model for Opti
mising Vertical Alignment for Multi-Material\nRoads (Supervisor: Dr. Yves
Lucet)\n10:45am – 11:00am Adam Fox: Augmented Reality Applications for C
onstruction Environment (Supervisor: Dr. Khalad Hasan)\n\nSession 2: 11:15
am – 12:15pm Chaired by: Dr. Khalad Hasan\n11:15am – 11:30am Ravi Bull
ock: Computing the Conjugate for Piecewise Linear-Quadratic Functions (Sup
ervisor: Dr. Yves Lucet)\n11:30am – 11:45am Yohen Thounaojam: Adversaria
l Robustness of Patch Attacks (Supervisor: Dr. Apurva Natayan)\n11:45am
12:00pm Hexuan Zhang: Developing a Language Learning Game (Supervisor: D
r. Bowen Hui)\n12:00pm – 12:15pm Tatiana Urazova: Automatic Generation a
nd Marking of UML Database Design Questions (Supervisor: Dr. Ramon Lawrenc
e)\n\nLunch Break (Sandwiches and Drinks Provided): 12:15pm – 1:00pm\nPr
ize Draw #1\n\nSession 3: 1:00pm – 2:00pm\nChaired by: Dr. Jeff Andrews\
n1:00pm – 1:15pm Carson Ricca: Using Bayesian Networks to Model Student
Mastery (Supervisor: Dr. Bowen Hui)\n1:15pm – 1:30pm Rick Feng: Deep-Lea
rning Models for Structured Data (Supervisor: Dr. Yong Gao)\n1:30pm – 1:
45pm Emily Medema: Bovine Event Detection: Data Analysis of Cow Data and C
ycles (Supervisor: Dr. Ramon Lawrence)\n1:45pm – 2:00pm Aashish Raizada:
TA Scheduler (Supervisor: Dr. Abdallah Mohammed)\n\nSession 4: 2:15pm –
3:15pm\nChaired by: Dr. Abdallah Mohammed\n2:15pm – 2:30pm Opey Adeyemi
: An Analysis of Mastery Learning Approaches to Improve Computer Science E
ducation Learning Outcomes (Supervisor: Dr. Bowen Hui)\n2:30pm – 2:45pm
Kathryn Ng: A Game Design Framework for Middle School Mathematics (Supervi
sor: Dr. Bowen Hui)\n2:45pm – 3:00pm Athena An: Improving the Performanc
e of Object Tracking Using ViT (Supervisor: Dr. Mohammed Shehata)\n3:00pm
– 3:15pm Ivan Carvalho: Towards Parallel Learned Sorting (Supervisor: Dr
. Ramon Lawrence)\n\nClosing: 3:15pm – 3:30pm\nPrize Draw #2
DTSTAMP:20220421T225950Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: High-Density Scintillating Glass Detector Use in
Proton Computed Tomography from Adam Zeiser
DTSTART:20220920T223000Z
DTEND:20220921T000000Z
UID:52
LOCATION:https://goo.gl/maps/XYQ7RgmwhowMYpUcA
DESCRIPTION:Adam Zei
ser\nTitle: High-Density Scintillating Glass Detector Use in Proton Co
mputed Tomography\nLocation: https://goo.gl/maps/XYQ7RgmwhowMYpUcA\nAbstra
ct: The high cost and low image quality traditionally associated with prot
on computed tomography (pCT) have prevented it from seeing significant use
in clinical settings. A cheap, compact, high-density scintillating glass
detector capable of being attached to existing proton therapy gantries may
help address these concerns. The design of the detector allows for use in
conjunction with single-proton counting reconstruction algorithms, as wel
l as beam-based algorithms that do not resolve individual protons within a
n accelerator bunch. This study presents quantitative reconstructed images
of proton stopping power from Monte Carlo generated pCT scans using the r
adiation transport code MCNP6, demonstrating the feasibility of proton ima
ging using this detector design. Relative error and contrast have been exa
mined and compared for images reconstructed using two reconstruction algor
ithms: a standard filtered back projection algorithm to act as a benchmark
, and a variant of a pCT algorithm which utilizes the concept of distance-
driven binning.
DTSTAMP:20220902T203621Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Collision-Free Multi-Axis Tool-Path for Additive Man
ufacturing from Rahnuma Nishat
DTSTART:20220920T170000Z
DTEND:20220920T174500Z
UID:49
LOCATION:https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Z
z09
DESCRIPTION:Rahnuma Nishat\nTitle: Collision-Free Multi-Axi
s Tool-Path for Additive Manufacturing\nLocation: https://ubc.zoom.us/j/33
80769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Zz09\nAbstract: Additive manufact
uring (AM) is a process by which complex components or assemblies are fabr
icated in layers. As materials are deposited over time, the possibility of
collision increases, inflicting damage to the components being printed or
the machine parts. Therefore, special care needs to be taken while design
ing the tool-path (the path that the deposition head follows during the ma
nufacturing process).\n \nIn this talk, I describe an algorithm we gave to
modify a given tool-path to avoid collision between the printing surface
and the tool holder using a geometric approach. In the algorithm, for each
point on the tool-path, we generate multiple tool vectors that are collis
ion-free. A tool vector at a point of the tool-path shows the direction of
the deposit head at that point. Using these collision-free tool-vectors w
e build an edge-weighted graph, where an edge is added between two tool-ve
ctors if they are associated with adjacent points on the tool-path. The we
ight of an edge denotes the change in angle between two tool-vectors. Then
using shortest path algorithm, we generate a collision-free tool-path suc
h that the difference in tilting angle is minimized between adjacent tool-
path points. I also present experimental results to show the performance o
f our algorithm.\n\nPlease join us on Zoom at https://ubc.zoom.us/j/338076
9070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Zz09\n(Meeting ID: 338 076 9070, Cod
e: 799902)
DTSTAMP:20220912T163721Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Cells in the Box and a Hyperplane from Imre Bárán
y
DTSTART:20220929T223000Z
DTEND:20220929T233000Z
UID:53
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Imre Bárány\nTitle: Cells in the Box and a Hyp
erplane\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5a
Gs3TUZ6YTRtdz09\nAbstract: It is well known that a line can intersect at m
ost 2n-1 cells of the n x n chessboard. What happens in higher dimensions:
how many cells of the d-dimensional [0,n]^d box can a hyperplane intersec
t? We also prove the integer analogue of the following fact. If K, L are c
onvex bodies in R^d and K is contained in L, then the surface area K is sm
aller than that of L. Joint work with Peter Frankl.
DTSTAMP:20220928T214148Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: What do we know about current NLP techniques for prog
ramming languages? from Fatemeh H. Fard
DTSTART:20221018T170000Z
DTEND:20221018T174500Z
UID:50
LOCATION:https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Z
z09
DESCRIPTION:Fatemeh H. Fard\nTitle: What do we know about current NLP tech
niques for programming languages?\nLocation: https://ubc.zoom.us/j/3380769
070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Zz09\nAbstract: Natural Language Proce
ssing (NLP) has gained a lot of advances in recent years, thanks to deep l
earning (DL) approaches, large language models, and Transformers. In the p
ast two years, there has been exponential growth in applying NLP and DL fo
r source code to detect bugs, fix bugs, generate code, recommend method na
mes, etc. However, source code is different from natural language. There a
re many repetitive tokens that are unique in code, and the developers can
also create infinite identifier names. Though a lot of NLP techniques are
applied to code, fewer studies try to understand the differences, and what
works or not in the context of programming languages and source code. In
this talk, we will go over some of our findings and other interesting work
s we have done for code representation learning.\n\nPlease join us on Zoom
at\nhttps://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Zz09
\n(Meeting ID: 338 076 9070, Code: 799902)
DTSTAMP:20220929T183506Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Maximum Entropy on the Mean Method for Linear I
nverse Problems (and beyond) from Tim Hoheisel
DTSTART:20221013T223000Z
DTEND:20221013T233000Z
UID:54
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Tim Hohei
sel\nTitle: The Maximum Entropy on the Mean Method for Linear Inverse
Problems (and beyond)\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGY
xeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The principle of ‘maximum entro
py’ states that the probability distribution which best represents the c
urrent state of knowledge about a system is the one with largest entropy w
ith respect to a given prior (data) distribution. It was first formulated
in the context of statistical physics in two seminal papers by E. T. Jayne
s (Physical Review, Series II. 1957), and thus constitutes an information
theoretic manifestation of Occam’s razor. We bring the idea of maximum e
ntropy to bear in the context of linear inverse problems in that we solve
for the probability measure which is close to the (learned or chosen) prio
r and whose expectation has small residual with respect to the observation
. Duality leads to tractable, finite-dimensional (dual) problems. A core t
ool, which we then show to be useful beyond the linear inverse problem set
ting, is the ‘MEMM functional’: it is an infimal projection of the Kul
lback-Leibler divergence and a linear equation, which coincides with Cram
r’s function (ubiquitous in the theory of large deviations) in most cas
es, and is paired in duality with the cumulant generating function of the
prior measure. Numerical examples underline the efficacy of the presented
framework.\n
DTSTAMP:20221004T205347Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Review of modern smoothing methods for density a
nd distribution functions dealing with circular data from Dr. Yogen Chaube
y
DTSTART:20221124T213000Z
DTEND:20221124T223000Z
UID:56
LOCATION:https://goo.gl/maps/jdt4CQE4Nb62XXyN7
DESCRIPTION:Dr. Yogen Chaubey\nTitle: Review of modern sm
oothing methods for density and distribution functions dealing with circul
ar data\nLocation: https://goo.gl/maps/jdt4CQE4Nb62XXyN7\nAbstract: In thi
s talk, I will provide a short review of modern smoothing methods for dens
ity and distribution functions dealing with circular data. The usefulness
of circular kernels for smooth density estimation in this context is highl
ighted and contrasted with smooth density estimation based on orthogonal s
eries. It is seen that the wrapped Cauchy kernel as a choice of circular k
ernel appears as a natural candidate as it has a close connection to ortho
gonal series density estimation on a unit circle. In the literature, the u
se of the von Mises circular kernel is investigated, which requires the nu
merical computation of the Bessel function. On the other hand, the wrapped
Cauchy kernel is much simpler to use. This adds further weight to the con
siderable role of the wrapped Cauchy distribution in circular statistics.
We also investigate some transformation-based methods in adapting the line
ar kernel density estimator to the circular data. This is very useful in p
ractice as widely available software on linear kernel density estimation c
an easily be adapted to the circular case. These simpler methods are illus
trated using some real data.
DTSTAMP:20221020T170121Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Improving Radiotherapy Treatment Logistics from Nad
ia Lahrichi
DTSTART:20221027T223000Z
DTEND:20221027T233000Z
UID:55
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Nadia Lahrichi\nTitle: Improving Radiotherapy Tr
eatment Logistics\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeld
zZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The main cancer treatments are surger
y, radiation therapy and chemotherapy. The complexity of the logistical pr
ocess of scheduling treatment appointments stems from the fact that it inv
olves extremely costly resources, sometimes synchronously. Several due dat
es (i.e., appointments already scheduled, maximum wait times) and unexpect
ed events such as the arrival of patients requiring urgent palliative care
add to the difficulty. This talk will investigate how can simulation and
optimization models help improve the efficiency of cancer treatment center
s and share experiences on patient booking, physician scheduling, and capa
city assessment. All projects are conducted in close partnership with a ho
spital and rely on real data.
DTSTAMP:20221024T175059Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Masked-Autoencoders for Few-Shot Learning from Reece
Walsh
DTSTART:20221122T180000Z
DTEND:20221122T184500Z
UID:59
LOCATION:https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Z
z09
DESCRIPTION:Reece Walsh\nTitle: Masked-Autoencoders for Few-
Shot Learning\nLocation: https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBt
ZDdSWkd5eWNjNFZ1Zz09\nAbstract: In few-shot classification, performing wel
l on a testing dataset is a challenging task due to the restricted amount
of labelled data available and the unknown distribution. Deep learning mod
els have struggled with generalization in this field, especially when tran
sitioning to out-of-domain datasets. Recent progress in self-supervised le
arning, however, has been shown to address the issue of few-shot testing o
n unseen classes (in-domain and out-of-domain) through use of robust weigh
ts learned from a pretext task. Moreover, masked autoencoding has also bee
n shown to encode a deeper understanding of a given class from a smaller a
mount of data. In this work, we propose Masked Autoencoders for Few-Shot L
earning (MAE-FS), a self-supervised, generative technique that reinforces
few-shot classification performance for a prototypical backbone model. MAE
-FS leverages the data completion capabilities of a masked autoencoder to
expand a given prototypical support set. We show that prototypes generated
by MAE-FS improve backbone performance by up to 17%. We also show that ap
plying MAE-FS to an inductive classifier achieves state-of-the-art perform
ance on mini-imagenet and the CVPR L2ID Classification Challenge. The prop
osed MAE-FS can be integrated with almost any existing prototype-based few
-shot learning technique.
DTSTAMP:20221027T215003Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Case Studies in Data Science and Analytics from a U
K Business School from Jing Lu
DTSTART:20221117T233000Z
DTEND:20221118T003000Z
UID:60
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jing Lu\nTitle: Case Studies in Data Science and
Analytics from a UK Business School\nLocation: https://sfu.zoom.us/j/6632
2138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Data science invol
ves the collection, management, processing, analysis, visualisation and in
terpretation of huge amounts of data. It is a multi-disciplinary field tha
t integrates systematic thinking, methodology, process and technology to d
evelop intelligence with respect to real-world problems. This presentation
focuses on the business environment and identifies the components of data
science forming a conceptual architecture before proposing a composite da
ta-driven process model. Representative tools and techniques are applied t
o relevant case studies demonstrating innovation in undergraduate programm
e design, customer analytics and the marketing of insurance for example.
DTSTAMP:20221107T224249Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Computing the nearest structured rank deficient mat
rix from Diego Cifuentes
DTSTART:20221110T223000Z
DTEND:20221110T233000Z
UID:57
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Diego Cifuentes\nTitle: Computing the nearest structured rank defic
ient matrix\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNU
GU5aGs3TUZ6YTRtdz09\nAbstract: Given an affine space of matrices L and a m
atrix Θ ∈ L, consider the problem of computing the closest rank deficie
nt matrix to Θ on L with respect to the Frobenius norm. This is a nonconv
ex problem with several applications in control theory, computer algebra,
and computer vision. We introduce a novel semidefinite programming (SDP) r
elaxation, and prove that it always gives the global minimizer of the nonc
onvex problem in the low noise regime, i.e., when Θ is close to be rank d
eficient. Our SDP is the first convex relaxation for this problem with pro
vable guarantees. We evaluate the performance of our SDP relaxation in exa
mples from system identification, approximate GCD, triangulation, and came
ra resectioning. Our relaxation reliably obtains the global minimizer unde
r non-adversarial noise, and its noise tolerance is significantly better t
han state of the art methods.
DTSTAMP:20221108T000132Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Individualized Dynamic Patient Monitoring Under Ala
rm Fatigue from Hossein Piri
DTSTART:20230105T233000Z
DTEND:20230106T003000Z
UID:63
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Hossein Piri\nTitle: Indi
vidualized Dynamic Patient Monitoring Under Alarm Fatigue\nLocation: https
://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstrac
t: Hospitals are rife with alarms, many of which are false. This leads to
alarm fatigue, in which clinicians become desensitized and may inadvertent
ly ignore real threats. We develop a partially observable Markov decision
process model for recommending dynamic, patient-specific alarms in which w
e incorporate a cry-wolf feedback loop of repeated false alarms. Our model
takes into account patient heterogeneity in safety limits for vital signs
and learns a patient’s safety limits by performing Bayesian updates dur
ing a patient’s hospital stay. We develop structural results of the opti
mal policy and perform a numerical case study based on clinical data from
an intensive care unit. We find that compared with current approaches of s
etting patients’ alarms, our dynamic patient-centered model significantl
y reduces the risk of patient harm.\n\nhttps://researchseminars.org/semina
r/SFUOR
DTSTAMP:20230103T202524Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Tensor Optimization and Applications from Tanmaya K
armarkar
DTSTART:20230119T233000Z
DTEND:20230120T003000Z
UID:58
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Tanmaya Karmarkar\nTitle: Tensor Optimization an
d Applications\nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudU
l0ODhCdEZ5MFlhVnAxdz09\nAbstract: First example of applying tensor optimiz
ation to combinatorial problems was shown in IPCO 1992: pages 406-420. We
improve and strengthen those results in several ways and obtain computatio
nal results on three problems - graph partitioning, satisfiability and ana
lysis of counterexamples related to Hilbert’s 17th problem. \n\nFor this
we created a mixed symbolic-numeric model formulation package which facil
itates definition of objective function, equality and inequality constrain
ts and definition of new dependent variables.\n\nFor discrete problems cer
tain inequalities valid at candidate solutions are dynamically incorporate
d in the iterations of the continuous optimization algorithm based on unde
rlying non-Newtonian geometry of the interior-point space.\n\nFor graph pa
rtitioning we obtain optimal solutions including proof of optimality. For
satisfiability problem we either find the satisfiable assignment or constr
uct and output proof of unsatisfiability.\n\nFor Hilbert’s 17th problem
we analyse concrete examples whose non-negativity has been stablished to b
e not provable using sums of the squares expressions valid in RN. However,
for these counterexamples, we construct non-negativity proofs by computat
ionally constructing sums of squares expressions valid on certain sub-vari
eties of RN The same modeling package mentioned above is used to post proc
ess the solver output into symbolic proofs of optimality or infeasibility\
n
DTSTAMP:20230118T185318Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Restarts Subject to Approximate Sharpness: a Parame
ter-free and Optimal Scheme for Accelerating First-order Methods from Ben
Adcock
DTSTART:20230126T233000Z
DTEND:20230127T003000Z
UID:67
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Ben Adcock\nTitle: Restarts Subject to Approxima
te Sharpness: a Parameter-free and Optimal Scheme for Accelerating First-o
rder Methods\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdN
UGU5aGs3TUZ6YTRtdz09\nAbstract: Sharpness is a generic assumption in conti
nuous optimization that bounds the distance to the set of minimizers in te
rms of the suboptimality in the objective function. It leads to the accele
ration of first-order optimization methods via so-called restarts. However
, sharpness involves problem-specific constants that are typically unknown
, and previous restart schemes often result in reduced convergence rates.
Such schemes are also challenging to apply in the presence of noise or app
roximate model classes (e.g., in compressed sensing or machine learning pr
oblems). In this talk, we introduce the notion of approximate sharpness, a
generalization of sharpness that incorporates an unknown constant perturb
ation to the objective function error. By employing a new type of search o
ver the unknown constants, we then describe a restart scheme that applies
to general first-order methods. Our scheme maintains the same convergence
rate as when assuming knowledge of the constants. Moreover, for broad clas
ses of problems, it gives rates of convergence which either match known op
timal rates or improve on previously established rates. Finally, we demons
trate the practical efficacy of this scheme on applications including spar
se recovery, compressive imaging and feature selection in machine learning
.
DTSTAMP:20230118T185550Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Number of String C-groups of High Rank from Dim
itri Leemans
DTSTART:20230202T233000Z
DTEND:20230203T003000Z
UID:65
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Dimitri Leemans\nTitle: The Number of String C-g
roups of High Rank\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxel
dzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Abstract polytopes are a combinatori
al generalisation of classical objects that were already studied by the gr
eeks. They consist in posets satisfying some extra axioms. Their rank is r
oughly speaking the number of layers the poset has. When they have the hig
hest level of symmetry (namely the automorphism group has one orbit on the
set of maximal chains), they are called regular. One can then use string
C-groups to study them.\n\nIndeed, string C-groups are in one-to-one corre
spondence with abstract regular polytopes. They are also smooth quotients
of Coxeter groups.\n\nThey consist in a pair (G,S) where G is a group and
S is a set of generating involutions satisfying a string property and an i
ntersection property. The cardinality of the set S is the rank of the stri
ng C-group. It corresponds to the rank of the associated polytope.\n\nIn t
his talk, we will give the latest developments on the study of string C-gr
oups of high rank. In particular, if G is a transitive group of degree $n$
having a string C-group of rank r >= (n+3)/2, work over the last twelve y
ears permitted us to show that G is necessarily the symmetric group S_n.\n
\nWe have just proven in the last months that if n is large enough, up to
isomorphism and duality, the number of string C-groups of rank r for S_n (
with r \geq (n+3)/2) is the same as the number of string C-groups of rank
r+1 for S_{n+1}. \n\nThis result and the tools used in its proof, in part
icular the rank and degree extension, imply that if one knows the string C
-groups of rank (n+3)/2 for S_n with n odd, one can construct from them al
l string C-groups of rank (n+3)/2+k for S_{n+k} for any positive integer k
. \n\nThe classification of the string C-groups of rank r >= (n+3)/2 for
S_n is thus reduced to classifying string C-groups of rank r for S_{2r-3}.
\n\nA consequence of this result is the complete classification of all str
ing C-groups of S_n with rank n-kappa for kappa in {1,...,6}, when n >= 2
kappa+3, which extends previous known results.\n\nThe number of string C-
groups of rank $n-\kappa$, with n >= 2kappa+3, of this classification giv
es the following sequence of integers indexed by kappa and starting at kap
pa = 1: (1,1,7,9,35,48).\n\nThis sequence of integers is new according to
the On-Line Encyclopedia of Integer Sequences.\n\nJoint work with Peter J.
Cameron (University of St Andrews) and Maria Elisa Fernandes (University
of Aveiro)\n\n \n
DTSTAMP:20230118T185808Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: A computational approach toward characterizing the st
ructure of phylogenetic tree powers from James Nastos and Dakota Joiner
DTSTART:20230131T230000Z
DTEND:20230131T234500Z
UID:51
LOCATION:ART 374, UBC Okanagan
DESCRIPTION:James Nastos a
nd Dakota Joiner\nTitle: A computational approach toward characterizin
g the structure of phylogenetic tree powers\nLocation: ART 374, UBC Okanag
an\nAbstract: The study of phylogeny (i.e. phylogenetics) concerns the evo
lutionary history group of organisms depicted in the form of phylogenetic/
evolutionary trees such that similar species appear close to each other in
the tree diagram. The process of reconstructing an evolutionary tree is o
ften heuristic and based on pairwise comparisons of species which could in
clude inconsistencies or impure data. One approach to building these trees
is through a graph-theoretic representation of the interspecies closeness
(whether it be through common attributes or genetic analysis or other). \
n\nA k-leaf power graph G is a graph of species where two species are adja
cent in G if and only if they are 'close' (within distance k of each other
) in their phylogenetic tree. Such leaf-power graphs, once constructed fro
m data, can be used to obtain the corresponding phylogenetic tree. Our int
erest here is to obtain structural characterizations of these k-leaf power
graphs corresponding to entirely consistent data. With such a characteriz
ation, in the presence of impure or inconsistent data, one can find a mini
mum alteration to the data to obtain a closest possible set of consistent
pairwise comparisons. The work presented here summarizes the computational
efforts conducted towards this goal of characterizing k-leaf power graphs
, undertaken by two undergraduate students in a Directed Studies course.
DTSTAMP:20230123T225538Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On Combinatorial Algorithms and Circuit Augmentatio
n for Pseudoflows from Angela Morrison
DTSTART:20230216T233000Z
DTEND:20230217T003000Z
UID:68
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Angela Morrison\nTitle: On Combinatorial Algorithms and
Circuit Augmentation for Pseudoflows\nLocation: https://ubc.zoom.us/j/692
41125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: There is a wealth
of combinatorial algorithms for classical min-cost flow problems and thei
r simpler variants like max flow or shortest-path problems. It is well-kno
wn that several of these algorithms are intimately related to the Simplex
method and the more general circuit augmentation schemes. Prime examples a
re the network Simplex method, a refinement of the primal Simplex method,
and (min-mean) cycle canceling, which corresponds to a (steepest-descent)
circuit augmentation scheme over the underlying polyhedron.\n\nWe are inte
rested in deepening and expanding the understanding of the close relations
hip between circuit augmentation and combinatorial network flows algorithm
s. To this end, we generalize from the consideration of primal or dual fea
sible flows to the so-called pseudoflows, which allow for a violation of f
low balance. We introduce what are called ‘pseudoflow polyhedra’, in w
hich slack variables are used to quantify this violation, and characterize
their circuits. This enables us to study various network flows algorithms
in view of the walks that they trace in these polyhedra, and in view of t
he pivot rules used to choose the steps.\n\nIn particular, we show that th
e Successive Shortest Path Algorithm and the Shortest/Generic Augmenting P
ath Algorithm form general, non-edge circuit walks. We also provide a proo
f outline showing that the aforementioned algorithms correspond to a Dantz
ig Rule and Steepest-ascent circuit augmentation scheme respectively.
DTSTAMP:20230125T185140Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Making Sense of Information Anywhere from Dr Barrett
Ens
DTSTART:20230216T213000Z
DTEND:20230216T223000Z
UID:70
LOCATION:COM 107 (VEMS), Kelowna
DESCRIPTION:Dr Barrett Ens\nTitle: Making Sense of Informati
on Anywhere\nLocation: COM 107 (VEMS), Kelowna\nAbstract: The miniaturisat
ion of sensing, networking, and processing technologies has increasingly m
ade information readily available. Taking this further, emerging Augmented
Reality (AR) technologies and near-future holographic displays (such as l
ight field and laser plasma displays) will soon allow rich visual informat
ion to be displayed anywhere, beyond the confines of small 2D screens. On
one hand, these advances will allow relevant information to be more direct
ly integrated with the activities, places or objects to which it is relate
d. However, they will also bring significant challenges in designing usefu
l and productive interfaces for visualising information and interacting wi
th it. \n\nGiven these coming developments, how can we leverage spatial i
nteraction and situated information spaces to improve the way we perceive,
interact with, and understand information? In this talk I will present my
work on spatial interface design, and recent applications for data explor
ation and sensemaking. I will discuss what we have learned about the benef
its of managing information in the space around us and some of the challen
ges that lie ahead.
DTSTAMP:20230215T194234Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Machine Learning in 3D Face Modeling from Peizhi Yan
DTSTART:20230221T230000Z
DTEND:20230221T234500Z
UID:62
LOCATION:https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Z
z09
DESCRIPTION:Peizhi Yan\nTitle: Machine Learning in 3D Face M
odeling\nLocation: https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWk
d5eWNjNFZ1Zz09\nAbstract: Applications of 3D digital humans (and avatars)
are no longer constrained to the film-making and gaming industries only. R
ecently, we started seeing their applications in Virtual Reality, such as
virtual conferences, virtual assistants, and virtual clothing try-on. The
rapid progress of 3D digital humans is credited to advances in 3D modeling
software, gaming engines, and computer graphics hardware. However, the va
st majority of industries still heavily rely on time-consuming manual work
for 3D digital human creation. It is necessary to transform the creation
of 3D digital humans from a labor-intensive process to a data-driven proce
ss to enable its wider application.\n\nMy research is mainly focusing on t
he creation of a 3D face. In this talk, I will start by introducing the ex
isting 3D face modeling methods, including the traditional 3D Morphable Fa
ce Model (3DMM) and the most recent neural network-based 3D face models. N
ext, I will present our work, Novel Editing-Oriented 3D Face (NEO-3DF). NE
O-3DF is built on 3DMM and deep learning methods that allow the user to cr
eate a 3D face from a 2D face image and further edit it. I will conclude b
y discussing some future research directions - audio-driven 3D face animat
ion and 3D face cartoon stylization.
DTSTAMP:20230216T205715Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: The Park-and-loop Technician Routing Problem from J
ean-François Cordeau
DTSTART:20230310T233000Z
DTEND:20230311T003000Z
UID:72
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Jean-François Cordeau\nTitle: The Park-and-loop
Technician Routing Problem\nLocation: https://sfu.zoom.us/j/66322138557?p
wd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Motivated by an application
in the routing of technicians at a French public utility, we introduce a
highly efficient heuristic together with a branch-price-and-cut algorithm
for the doubly open park-and-loop routing problem. This problem is an exte
nsion of the classical vehicle routing problem in which routes may involve
a main tour performed by driving a vehicle as well as a set of subtours t
hat are carried out on foot after parking the vehicle. In addition, routes
do not start and end at a central depot, but rather at customer locations
. We first describe a matheuristic based on a split procedure that generat
es high quality solutions fast. We present computational experiments on a
set of real instances with up to 3,800 customers. We also apply the matheu
ristic to a related problem called the vehicle routing problem with transp
ortable resources, where the method found new best solutions on 32 out of
40 benchmark instances from the literature. We then present an exact algor
ithm, based on a set-covering formulation of the problem with columns repr
esenting complete routes, which is capable of solving to optimality instan
ces with up to 50 customers.
DTSTAMP:20230222T173809Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Simulation Modelling of the BC Critical Care System
for Pandemic Response from Sandy Rutherford
DTSTART:20230302T233000Z
DTEND:20230303T003000Z
UID:71
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sandy Rutherford\nTitle: Simulation Modelling of
the BC Critical Care System for Pandemic Response\nLocation: https://sfu.
zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: The
pandemic placed considerable stress on the critical care system in British
Columbia. In this talk, I will present simulation modelling analysis done
to support the response to the pandemic and ongoing work to improve the a
bility of the critical care system to respond to future public health cris
es. The first project that I will discuss is a queuing model to inform ven
tilator capacity planning during the first wave of the COVID-19 pandemic.
I will then describe ongoing development of a discrete event simulation mo
del for the network of major intensive care units (ICUs) in BC. Currently,
our model contains admissions and transfers for ICUs and high acuity unit
s at eight hospitals in BC. This model will help to improve patient access
to critical care, and inform planning for seasonal influenza and COVID-19
.
DTSTAMP:20230227T181454Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:Symposium seminar: Model-Based Clustering of Three-Way Data from Dr
. Paul McNicholas
DTSTART:20230328T220000Z
DTEND:20230328T233000Z
UID:73
LOCATION:EME 1151
DESCRIPTION:Dr. Paul McNicholas\nTitle: Model-Based Clusteri
ng of Three-Way Data\nLocation: EME 1151\nAbstract: Some model-based clust
ering approaches for three-way data are discussed. These include approache
s for multivariate longitudinal data, approaches that do not assume normal
ity, and an approach for three-way data that are high dimensional. Several
examples are discussed, including a motivating example on data from a lar
ge Canadian autism study. Finally, some future directions are discussed.
DTSTAMP:20230309T215328Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: RESCHEDULED TO LATER DATE A Qualitative Study about t
he Benefits of Time-bounded Collaborative Events for Startup Founders from
Cleidson R. B. de Souza
DTSTART:20230314T220000Z
DTEND:20230314T224500Z
UID:61
LOCATION:https://ubc.zoom.us/j/3380769070?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Z
z09
DESCRIPTION:Cleidson R. B. de Souza\nTitle: RESCHEDULED TO L
ATER DATE A Qualitative Study about the Benefits of Time-bounded Collabora
tive Events for Startup Founders\nLocation: https://ubc.zoom.us/j/33807690
70?pwd=NDNoUTFxOTBtZDdSWkd5eWNjNFZ1Zz09\nAbstract: Software development or
ganizations, like any other organizations, need to innovate to remain comp
etitive. One way of doing this is by adopting open innovation approaches l
ike software platform ecosystems, software crowdsourcing, and time-bounded
collaborative events (e.g., hackathons and game jams). In particular, the
se events attract people with different backgrounds to work in small teams
on a project that addresses a particular problem in a very short amount o
f time. Previous research acknowledges that event participants have the op
portunity to learn new things, meet people, and gain recognition, among ot
her benefits. However, there are few studies exploring the relationship be
tween these events and startups. \n\nThis talk will explore how startup fo
unders benefit from time-bounded events based on a qualitative study using
data from semi-structured interviews with 20 startup founders. The benefi
ts cited by the interviewees include developing new products, raising mone
y, gaining visibility, and identifying opportunities for action, learning
and networking. Our results suggest that participation in time-bounded col
laborative events helps startup founders to satisfy some of the needs of t
heir startups. We conclude by presenting recommendations for event organiz
ers.
DTSTAMP:20230314T171320Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Data-driven Approach to Optimal Ordering batching P
roblem in Warehouse Management from Gohram Baloch
DTSTART:20230316T223000Z
DTEND:20230316T233000Z
UID:66
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Gohram Ba
loch\nTitle: Data-driven Approach to Optimal Ordering batching Problem
in Warehouse Management\nLocation: https://sfu.zoom.us/j/66322138557?pwd=
ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: In this work, we focus on the
picking process in warehouse management and study it from a data perspecti
ve. Using historical data from an industrial partner, we introduce, model,
and study the robust order batching problem (ROBP) that groups orders int
o batches to minimize total order processing time accounting for uncertain
ty caused by system congestion and human behavior. We provide a generaliza
ble, data-driven approach that overcomes warehouse-specific assumptions ch
aracterizing most of the work in the literature. We analyze historical dat
a to understand the processes in the warehouse, to predict processing time
s, and to improve order processing. We introduce the ROBP and develop an e
fficient learning-based branch-and-price algorithm based on simultaneous c
olumn and row generation, embedded with alternative prediction models such
as linear regression and random forest that predict processing time of a
batch. We conduct extensive computational experiments to test the performa
nce of the proposed approach and to derive managerial insights based on re
al data.\n\n
DTSTAMP:20230315T185937Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On the Packing and Hitting Numbers of Axis-parallel
Segments from Marco Caoduro
DTSTART:20230406T223000Z
DTEND:20230406T233000Z
UID:74
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Marco Caoduro\nTitle: On the Packing and Hitting
Numbers of Axis-parallel Segments\nLocation: https://sfu.zoom.us/j/663221
38557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Given a family R of
rectangles in the plane, the packing number of R, denoted by $\nu$(R), is
the maximum size of a set of pairwise disjoint rectangles in R, and the hi
tting number, denoted by $\tau$(R), is the minimum size of a set of points
having a non-empty intersection with each rectangle in R. Clearly, $\tau
\ge \nu$.\n\nWegner (1965), and independently, Gyárfás and Lehel (1985),
asked whether the hitting number $\tau$ could be bounded by a linear func
tion of the packing number $\nu$. In addition, Wegner proposed a multiplic
ative constant of 2. This problem is still wide open and even if linear bo
unds are known for several particular cases, almost none of them are paire
d with lower bound examples showing their optimality.\n\nFor a family of a
xis-parallel line segments, it is easy to show that $\tau \le 2\nu$, as su
ggested by Wegner. During the talk, we will consider families of axis-para
llel segments with the additional property that no three of them meet at a
point (that is, the intersection graph is triangle-free). We show that, i
n this restricted setting, the packing number of a family is at least $n/4
+C_1\sqrt{n}$ where $n$ is the size of the considered family and $C_1$ is
a fixed positive constant. In addition, we construct examples with packing
number at most $n/4 + C_2\sqrt{n}$ for a different constant $C_2 > C_1$ s
howing that the previous bound is essentially optimal.\nAs a consequence o
f these results, we settle the Wegner-Gyárfás-Lehel’s problem for axis
-parallel segments showing that the multiplicative constant of 2 is optima
l and deduce that $\tau \le 2\nu − C_3 \sqrt{\nu}$ for triangle-free axi
s-parallel segments. This bound cannot be achieved for triangle-free axis-
parallel rectangles, marking a substantial difference in the behavior of s
egments and rectangles.\n\nAt the end of the talk, we will present several
open problems, in particular, linking our work with the recent developmen
ts on the computation of the packing number for axis-parallel rectangles o
f Mitchell (2021) and Gálvez, Khan, Mari, Mömke, Pittu, and Wiese (2022)
. This is joint work with Jana Cslovjecsek, Michał Pilipczuk, and Karol W
ęgrzycki.\n\n \n
DTSTAMP:20230319T041506Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Effects of Usage-Based Auto Insurance: A Dynamic Me
chanism-Design Approach from Mona Imanpoor Yourdshahy
DTSTART:20230323T223000Z
DTEND:20230323T233000Z
UID:69
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Mona Imanpoor Yourdshahy\nTitle: Effects of Usag
e-Based Auto Insurance: A Dynamic Mechanism-Design Approach\nLocation: htt
ps://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstr
act: Usage-Based Insurance (UBI) is one of the most recent innovations by
auto insurance companies that links the premium rates of customers to thei
r actual driving performance. In this program, drivers’ behaviours are m
onitored directly while they drive. Then, the insurance company uses this
data to offer discounts on the insurance premium to their customers. This
paper provides a theoretical model to capture the effects of this monitori
ng technology on the auto insurance market. We formulate the underlying in
surance problem as a dynamic principal-agent model with hidden information
and hidden action. An agent (customer) privately knows his type that summ
arizes his ability as a driver and can exert an unobservable effort in eac
h period, which affects his subsequent type. The principal (insurer) offer
s a long-term contract to the agent despite the fact that she observes nei
ther the type of the agent nor the actions he takes. We characterize the f
ull history-dependent optimal contract for this dynamic adverse selection
and moral hazard problem. To compute the optimal contract, we develop a ge
neral recursive formulation. The underlying system is a Markov decision pr
ocess, where the evolution of the state of the system (type of the custome
r) is endogenous, as it depends on his hidden action in the previous perio
d. We develop a dynamic programming algorithm to examine the model analyti
cally and explore structural results about the optimal contract. The model
results lead to important and interesting managerial insights for firms w
ho may consider offering UBI programs. The study sheds light on how to des
ign the contract to manage a UBI program, the extent to which a UBI policy
can outperform a traditional policy, and how the potential gains depend o
n the demographics of the target market.
DTSTAMP:20230321T224604Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: COSC 590 presentations from Multiple
DTSTART:20230328T210000Z
DTEND:20230329T000000Z
UID:76
LOCATION:ART 374
DESCRIPTION:Multiple\nTitle: COSC 590 presentations\nLocatio
n: ART 374\nAbstract: Date and Times March 21 March 28 April 4 April
11\n2:00 PM - 2:30 PM Omar Abdelaziz Satabdi Das Ruochen Deng Eranga De
Saa\n2:30 PM - 3:00 PM Islam Abdelfattah Pragya Bhandari Samar Sallam\
n3:00 PM - 3:30 PM Not available Nelusha Nugegoda Not available Victor O
kpanachi\n3:30 PM - 4:00 PM Not available Tim Mammadov Not available Sha
wn Zhao\n4:00 PM - 4:30 PM Namrata Kundu Wei-Hsiang Hung Md Jumar Alam T
anmaya Karmarkar\n4:30 PM - 5:00 PM Sayan Sadhukhan Iman Saberi Erfan Rao
ofian Jonathan Gresl\n
DTSTAMP:20230328T204743Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: COSC 590 presentations from Multiple
DTSTART:20230321T210000Z
DTEND:20230322T000000Z
UID:75
LOCATION:ART 374
DESCRIPTION:Multiple\nTitle: COSC 590 presentations\nLocatio
n: ART 374\nAbstract: Date and Times March 21 March 28 April 4 April
11\n2:00 PM - 2:30 PM Omar Abdelaziz Satabdi Das Ruochen Deng Eranga De
Saa\n2:30 PM - 3:00 PM Islam Abdelfattah Pragya Bhandari Samar Sallam\
n3:00 PM - 3:30 PM Not available Nelusha Nugegoda Not available Victor O
kpanachi\n3:30 PM - 4:00 PM Not available Tim Mammadov Not available Sha
wn Zhao\n4:00 PM - 4:30 PM Namrata Kundu Wei-Hsiang Hung Md Jumar Alam T
anmaya Karmarkar\n4:30 PM - 5:00 PM Sayan Sadhukhan Iman Saberi Erfan Rao
ofian Jonathan Gresl\n
DTSTAMP:20230328T204810Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: COSC 590 presentations from Multiple
DTSTART:20230404T210000Z
DTEND:20230405T000000Z
UID:77
LOCATION:ART 374
DESCRIPTION:Multiple\nTitle: COSC 590 presentations\nLocatio
n: ART 374\nAbstract: Date and Times March 21 March 28 April 4 April
11\n2:00 PM - 2:30 PM Omar Abdelaziz Satabdi Das Ruochen Deng Eranga De
Saa\n2:30 PM - 3:00 PM Islam Abdelfattah Pragya Bhandari Samar
Sallam\n3:00 PM - 3:30 PM Not available Nelusha Nugegoda Not available
Victor Okpanachi\n3:30 PM - 4:00 PM Not available Tim Mammadov No
t available Shawn Zhao\n4:00 PM - 4:30 PM Namrata Kundu Wei-Hsiang Hung
Md Jumar Alam Tanmaya Karmarkar\n4:30 PM - 5:00 PM Sayan Sadhukha
n Iman Saberi Erfan Raoofian Jonathan Gresl\n
DTSTAMP:20230328T205020Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: COSC 590 presentations from Multiple
DTSTART:20230411T210000Z
DTEND:20230412T000000Z
UID:78
LOCATION:ART 374
DESCRIPTION:Multiple\nTitle: COSC 590 presentations\nLocatio
n: ART 374\nAbstract: Date and Times March 21 March 28 April 4 April
11\n2:00 PM - 2:30 PM Omar Abdelaziz Satabdi Das Ruochen Deng Eranga De
Saa\n2:30 PM - 3:00 PM Islam Abdelfattah Pragya Bhandari Sa
mar Sallam\n3:00 PM - 3:30 PM Not available Nelusha Nugegoda Not availabl
e Victor Okpanachi\n3:30 PM - 4:00 PM Not available Tim Mammadov
Not available Shawn Zhao\n4:00 PM - 4:30 PM Namrata Kundu Wei-Hsiang Hu
ng Md Jumar Alam Tanmaya Karmarkar\n4:30 PM - 5:00 PM Sayan Sadhu
khan Iman Saberi Erfan Raoofian Jonathan Gresl\n
DTSTAMP:20230328T205147Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: MATH 590 presentations from Multiple
DTSTART:20230328T210000Z
DTEND:20230328T223000Z
UID:79
LOCATION:SCI 236
DESCRIPTION:Multiple\nTitle: MATH 590 presentations\nLocatio
n: SCI 236\nAbstract: Sanuri Thudallage 14:00pm–14:30pm\nTitle: On the m
echanistic underpinning of discrete-time population models Abstract: Many
natural populations are modelled using discrete-time models. But no popula
tion has a completely discrete life cycle, and so we ask what mechanisms u
nderpin these classical discrete-time models? Geritz and Kisdi (2004) demo
nstrated how a large class of classical discrete-time models can be derive
d from continuous-time consumer-resource models with discrete birth events
(semi-discrete system). Eskola and Parvinen (2006) extended this work to
show how models with Allee effects can be derived using the Geritz and Kis
di (2004) framework. Together, these papers describe the mechanisms underl
ying classical discrete-time models.\n\nSarah Wyse 14:30pm–15:00pm\nTitl
e: Experimental evidence for tipping points in social convention\nAbstract
: I present a summary of Centola et al. (2018) and extend upon their work
to further study the model. In this paper, the authors study an agent-base
d opinion dynamics model where agents have a memory of length M and can ho
ld opinion A or opinion B depending on which opinion shows up most in thei
r memory. The authors introduce a committed minority to the model to study
the resulting dynamics. In this theoretical model, a committed minority c
an overturn an established social convention as long as it is large enough
. They find that this critical committed minority size is a function of th
e memory length. The authors collect data to support their claim that, whe
n M = 12, the committed minority must be equal to or larger than 25% to ov
erturn an established convention. I discuss my critiques of their work, in
particular, their data collection methods. I reformulate the agent-based
model as a system of ODEs for the M = 1 and M = 2 cases and find that if t
he committed minority is non-zero, then it overturns the established socia
l convention for all initial conditions. I make an assumption to simplify
the M = 2 model and find a tipping point that agrees with the results from
Centola et al. (2018)\n\nZiyuan Wang 15:00pm–15:30pm\nTitle: Finding co
njugate duality of relative smoothness\nAbstract: A classic result in conv
ex analysis states that a function is Lipschitz smooth, that is, different
iable with Lipschitz gradient, if and only if its Fenchel conjugate is str
ongly convex. Relative smoothness generalizes Lipschitz smoothness. Howeve
r, the conjugate duality of relative smoothness remained an open question
in the field, as posed by Lu, Freund and Nesterov [SIAM J. Optim., 28 (201
8), pp. 333–354]. In this talk, we present an answer by Laude, Themelis,
Patrinos, based on two recent preprints by Laude, Themelis and Patrinos,
and by Laude and Patrinos.
DTSTAMP:20230328T205836Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:590 seminar: MATH 590 presentations from Multiple
DTSTART:20230404T210000Z
DTEND:20230404T233000Z
UID:80
LOCATION:SCI 236
DESCRIPTION:Multiple\nTitle: MATH 590 presentations\nLocatio
n: SCI 236\nAbstract: Cassie Zhang 14:00pm–14:30pm\nTitle: On the limiti
ng distributions of multivariate depth-based rank sum statistics and relat
ed tests\nAbstract: The depth-based rank sum statistic, an extension of th
e univariate Wilcoxon rank sum statistic, was introduced by Liu and Singh
for use in multivariate rank tests. It employs a depth function to transfo
rm multivariate data points into a center-outward ordering, providing a ro
bust, distribution-free method for analyzing multivariate data. While the
Liu-Singh depth-based rank sum statistic (Q-statistic) is asymptotically d
istribution-free under the null hypothesis, its limiting distribution has
only been conjectured by Liu and Singh. An attempt to prove this conjectur
e by Rousson was not entirely successful. In this paper, the authors provi
de a rigorous validation of the conjectured limiting distribution for the
Qstatistic, addressing the mathematical gap left by previous attempts. Thi
s validation confirms the robustness and reliability of the depth-based ra
nk sum statistic in various applications and its advantages over other mul
tivariate rank tests, such as Hotelling's T^2 test, in detecting location-
scale changes in multivariate distributions.\n\nYiwen Chen 14:30pm–15:00
pm\nTitle: What color is your Hessian?\nAbstract: Jacobian and Hessian app
roximations are important in optimization algorithms. When the Jacobian or
Hessian is sparse, the sparsity structure can be exploited to improve the
computation efficiency, which is measured by the number of function evalu
ations required. In this talk, we introduce a graph-theoretic framework de
veloped by Gebremedhin et al. (SIAM review,\n2005) that can help reduce th
e number of function evaluations. In particular, we show how they formulat
e the computation problem as a matrix partitioning problem and apply the g
raph-coloring technique to solve it. We illustrate their technique on spec
ific examples where Hessian matrices have classical sparse structures. We
also generalize their technique to the case where the Hessian is approxima
ted without using gradient information.\n\nMahammad Mosaffa 15:00pm–15:3
0pm\nTitle: Accurate emergency department wait time prediction\nAbstract:
I will present a new method called Q-Lasso for predicting wait times in em
ergency departments which was proposed by Ang et al. (2015) and published
in M&SOM Informs journal. Q-Lasso uses a combination of statistical learni
ng and fluid model estimators, and outperforms current methods used by hos
pitals, such as rolling averages and quantile regression. Q-Lasso is bette
r at correcting underestimations and predicting longer wait times for low-
acuity patients. When implemented at the San\nMateo Medical Center, Q-Lass
o achieved over 30% lower prediction error than the best rolling average m
ethod. I will discuss the challenges and lessons learned from implementing
Q-Lasso at the medical center \n\n\nNilma Eslami 15:30pm–16:00pm\nTitle
: Enhanced computation speed and improved bounds through duality in two-st
age adaptive linear optimization.\nAbstract: Duality is a powerful concept
in optimization that allows one to transform a primal optimization proble
m into an equivalent dual problem. This dual problem can often be easier t
o solve or analyze than the original primal problem. In the context of two
-stage adaptive linear optimization models, it will be shown how duality c
an be used to derive an equivalent dualized formulation. This dualized for
mulation is also a two-stage adaptive linear optimization model, but it ha
s a different dimension and uses a different description of the uncertaint
y set than the primal formulation. It will be demonstrated that\nthe optim
al primal affine policy can be directly obtained from the optimal affine p
olicy in the dual formulation. Furthermore, empirical evidence is presente
d that the dualized formulation can be solved at an order of magnitude fas
ter than the primal formulation with affine policies in two-stage lot-sizi
ng on a network and two-stage facility location problems.\n\nAmirhossein S
alamiriad 16:00pm–16:30pm\nTitle: Solving two-stage robust optimization
problems using a column-and-constraint generation method\nAbstract: Two-st
age robust optimization (RO) has become increasingly more famous among the
scholars during the recent years. However, two-stage RO models are very d
ifficult to compute and even a simple twostage RO could be NP-hard. In thi
s paper, the column-and-constraint generation (C&CG) algorithm is proposed
to find the exact solution of two-stage RO in a timely manner. In my talk
, I will provide a quick introduction on two-stage optimization and robust
optimization and then, I will introduce the C&CG thoroughly. Finally, I w
ill present the numerical results of applying the C&CG on two different tw
o-stage RO problems.
DTSTAMP:20230330T201437Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:URC seminar: Computer Science Honours thesis presentations from URC
students
DTSTART:20230421T194500Z
DTEND:20230422T000000Z
UID:81
LOCATION:https://cmps-people.ok.ubc.ca/ylucet/urc/urc2023.pdf
DESCRIPTION:URC students\nTitle: Computer Science Honours th
esis presentations\nLocation: https://cmps-people.ok.ubc.ca/ylucet/urc/urc
2023.pdf\nAbstract: https://cmps-people.ok.ubc.ca/ylucet/urc/urc2023.pdf\n
click link for speaker list, times, titles, and supervisors
DTSTAMP:20230412T023909Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Decoding electroencephalographic (EEG) signals for Br
ain-Computer Interfaces from Dr Javier M. Antelis
DTSTART:20230524T220000Z
DTEND:20230524T230000Z
UID:82
LOCATION:SCI 396
DESCRIPTION:Dr Javier M. Antelis \nTitle: Decoding electroen
cephalographic (EEG) signals for Brain-Computer Interfaces\nLocation: SCI
396\nAbstract: In this talk, I will give an overview of EEG-based brain-co
mputer interfaces and the role of the machine learning algorithms to decod
e brain signals. I will present various applications developed in our lab
which are focused to control hand orthosis, mobile robots, and a virtual r
eality environment. I will show two of our BCI systems that are working in
a clinical environment with people with amyotrophic lateral sclerosis and
spinal cord injury. Finally, I will present several studies focused on th
e decoding of brain signals in different daily-live situations using machi
ne learning models.
DTSTAMP:20230523T190606Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Central Curve in Semidefinite Programming from Isab
elle Shankar
DTSTART:20230525T223000Z
DTEND:20230525T233000Z
UID:64
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:
Isabelle Shankar\nTitle: Central Curve in Semidefinite Programming\nLo
cation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09\nAbstract: The Zariski closure of the central path (which interior po
int algorithms track in convex optimization problems such as linear and se
midefinite programs) is an algebraic curve, called the central curve. Its
degree has been studied in relation to the complexity of these interior po
int algorithms. We show that the degree of the central curve for generic
semidefinite programs is equal to the maximum likelihood degree of linear
concentration models. This is joint work with Serkan Hoşten and Angélic
a Torres.
DTSTAMP:20230523T191304Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: First-order Methods for Bilevel Optimization from Z
haosong Lu
DTSTART:20230921T210000Z
DTEND:20230921T220000Z
UID:85
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Zhaosong Lu\nTitle: First-order Methods for Bile
vel Optimization\nLocation: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldz
ZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Bilevel optimization has been widely u
sed in a variety of areas such as adversarial training, hyperparameter tun
ing, image reconstruction meta-learning, neural architecture search, and r
einforcement learning. In this talk, I will present first-order methods fo
r solving a class of bilevel optimization through the use of single or seq
uential minimax optimization. The first-order operation complexity of the
proposed methods will be discussed.
DTSTAMP:20230919T215037Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: On the composition of two linear projections from H
einz Bauschke
DTSTART:20231005T210000Z
DTEND:20231005T220000Z
UID:88
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Heinz Bauschke\nTitle: On the composition of two
linear projections\nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2O
TdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: Projection operators are fundamenta
l algorithmic operators in Analysis and Optimization. It is well known tha
t these operators are ﬁrmly nonexpansive\; however, their composition is
generally only averaged and no longer ﬁrmly nonexpansive. We will intro
duce the modulus of averagedness and provide an exact result for the compo
sition of two linear projection operators. As a consequence, we deduce tha
t the Ogura-Yamada bound for the modulus of the composition is sharp. Base
d on joint work with Theo Bendit and Walaa Moursi.
DTSTAMP:20230926T001357Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Understanding the Data Needs for Developing a Computa
tional Model of Team Dynamics from Dr. Bowen Hui
DTSTART:20231016T170000Z
DTEND:20231016T180000Z
UID:83
LOCATION:FIP 140
DESCRIPTION:Dr. Bowen Hui
\nTitle: Understanding the Data Needs for Developing a Computational M
odel of Team Dynamics\nLocation: FIP 140\nAbstract: Understanding team eff
ectiveness is crucial to improving performance in many workplace and educa
tional contexts. Many theories have postulated a variety of factors that i
nfluence team success. However, they are limited to a descriptive framewor
k or involve small empirical studies. In contexts involving many teams, we
would ideally like to monitor ongoing team behaviors to alert problematic
behaviors and reward positive actions. To accomplish this goal, we propos
e to develop a computational model of team concepts to facilitate the dete
ction, prediction, and proper management of team behaviors. In this work,
we synthesize the literature on team models and present six overarching te
am concepts. We select two specific concepts and model them as a dynamic B
ayesian network (DBN). We demonstrate the utility of the DBN models in sim
ulation and discuss the gap between the behaviors prescribed by team theor
ies and the data needs in computational models. Lastly, we discuss possibl
e data sources that serve as starting points for developing empirically ac
curate models.
DTSTAMP:20231011T160852Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Exploiting Problem Structure for Efficient Optimiza
tion in Machine Learning from Sharan Vaswani
DTSTART:20231019T210000Z
DTEND:20231019T220000Z
UID:86
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Sharan Vaswani\nTitle: Exploiting Problem Struct
ure for Efficient Optimization in Machine Learning\nLocation: https://sfu.
zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstract: Stoc
hastic gradient descent (SGD) is the standard optimization method for trai
ning machine learning (ML) models. SGD requires a step-size that depends o
n unknown problem-dependent quantities, and the choice of this step-size h
eavily influences the algorithm's practical performance. By exploiting the
interpolation property satisfied by over-parameterized ML models, we desi
gn a stochastic line-search procedure that can automatically set the SGD s
tep-size. The resulting algorithm exhibits improved theoretical and empiri
cal convergence, without requiring the knowledge of any problem-dependent
constants. Next, we consider efficient optimization for imitation learning
(IL) and reinforcement learning. These settings involve optimizing functi
ons for which it is expensive to compute the gradient. We propose an optim
ization framework that uses the expensive gradient computation to construc
t surrogate functions that can then be minimized efficiently. This allows
for multiple model updates, thus amortizing the cost of the gradient compu
tation. The resulting majorization-minimization algorithm is equipped with
strong theoretical guarantees and exhibits fast convergence on standard I
L problems.\n\n
DTSTAMP:20231011T201710Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: Quantum computing with superconducting qubits from Dr
. Joshua Y. Mutus
DTSTART:20231101T211500Z
DTEND:20231101T220000Z
UID:91
LOCATION:FIP 139
DESCRIPTION:Dr. Joshua Y. Mutus\
nTitle: Quantum computing with superconducting qubits\nLocation: FIP 139\n
Abstract: What is a quantum computer and what might it be useful for? I'll
describe how a quantum computer, based on superconducting qubits, works.
I'll also describe fault tolerant quantum computing (FTQC), and the applic
ations where a quantum computer might vastly outperform even the largest h
igh performance computing facility. What might such "utility-scale" quantu
m computer look like and how big would it have to be to solve problems int
ractable on today's machines?
DTSTAMP:20231023T171345Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: The Inclusive Developer: Perspectives and Considerat
ions for Building Inclusive Software from Daniela Damian
DTSTART:20231027T220000Z
DTEND:20231027T230000Z
UID:89
LOCATION:EME 4116
DESCRIPTION:Daniela Damian\nTit
le: The Inclusive Developer: Perspectives and Considerations for Building
Inclusive Software\nLocation: EME 4116\nAbstract: As software has become
ubiquitous and influences our society in unprecedented ways, it is becomin
g imperative to understand how it can be inclusive of diverse end-user nee
ds. The software industry is, however, in a diversity crisis as software
teams lack the breadth of knowledge, skills and perspectives afforded by d
iverse membership. As the software products that teams build reflect the d
iversity of understanding and experiences of the teams, research needs to
carefully consider the relationship between the team, its development proc
esses and the software it develops -- rightly so, existing literature has
focused on aspects of diversity in software teams as drivers for more incl
usive software products. In this talk, I take the next step and argue that
our focus should be on inclusivity in software teams, and what design pro
cesses, tools and education environments can support diverse teams become
inclusive in order to develop more inclusive software.
DTSTAMP:20231023T191749Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Pricing Shared Rides from Julia Yan
DTSTART:20231102T210000Z
DTEND:20231102T220000Z
UID:87
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Julia Yan\nTitle: Pricing Shared Rides\nLocation
: https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\n
Abstract: Shared rides, which pool individual riders into a single vehicle
, are essential for mitigating congestion and promoting more sustainable u
rban transportation. However, major ridesharing platforms have long strugg
led to maintain a healthy and profitable shared rides product. To understa
nd why shared rides have struggled, we analyze procedures commonly used in
practice to set static prices for shared rides, and discuss their pitfall
s. We then propose a pricing policy that is adaptive to matching outcomes,
dubbed match-based pricing, which varies prices depending on whether a ri
der is dispatched alone or to what extent she is matched with another ride
r. Analysis on a single origin-destination setting reveals that match-base
d pricing is both profit-maximizing and altruistic, simultaneously improvi
ng cost efficiency (i.e., the fraction of cost saved by shared rides relat
ive to individual rides) and reducing rider payments relative to the optim
al static pricing policy. These theoretical results are validated on a lar
ge-scale simulation with hundreds of origin-destinations from Chicago ride
sharing data. The improvements in efficiency and reductions in payments ar
e especially noticeable when costs are high and demand density is low, ena
bling healthy operations where they have historically been most challengin
g.\n\n
DTSTAMP:20231027T185909Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Path to Energy Sovereignty: Clean and Affordable So
lutions for Remote Communities from Feyza Sahinyazan
DTSTART:20231116T220000Z
DTEND:20231116T230000Z
UID:90
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Feyza Sahinyazan\nTitle: Path to Energy Sovereig
nty: Clean and Affordable Solutions for Remote Communities\nLocation: http
s://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbstra
ct: Remote communities around the globe rely on off-grid installations of
stand-alone diesel generators to cover their energy needs, which can be co
stly, harmful to the environment and subject to disruptions. Policymakers
seek sustainable solutions for these communities to meet the Sustainable D
evelopment Goals regarding clean energy and reduced inequalities. Even wit
h the best intentions, ignoring community perspectives can hamper the clea
n energy transition and energy accessibility goals of remote communities.
Our objective in this research is to identify the optimal generation capac
ity investment decisions from a remote community’s perspective and inves
tigate how common policy mechanisms interact with these decisions.\n\nI am
also planning to dedicate some portion of my talk to give a brief overvie
w of my other ongoing projects to see if there is any interest in collabor
ation.
DTSTAMP:20231103T180125Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Searching for Optimal Per-Coordinate Step-sizes wit
h Multidimensional Backtracking from Frederik Kunstner
DTSTART:20231130T220000Z
DTEND:20231130T230000Z
UID:93
LOCATION:https://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRt
dz09
DESCRIPTION:Frederik Kunstner\nTitle: Searching for Optimal
Per-Coordinate Step-sizes with Multidimensional Backtracking\nLocation: ht
tps://sfu.zoom.us/j/66322138557?pwd=ZGYxeldzZjdNUGU5aGs3TUZ6YTRtdz09\nAbst
ract: The backtracking line-search is an effective technique to automatica
lly tune the step-size in smooth optimization. It guarantees similar perfo
rmance to using the theoretically optimal step-size. Many approaches have
been developed to instead tune per-coordinate step-sizes, also known as di
agonal preconditioners, but none of the existing methods are provably comp
etitive with the optimal per-coordinate stepsizes. We propose multidimensi
onal backtracking, an extension of the backtracking line-search to find go
od diagonal preconditioners for smooth convex problems. Our key insight is
that the gradient with respect to the step-sizes, also known as hypergrad
ients, yields separating hyperplanes that let us search for good precondit
ioners using cutting-plane methods. As black-box cutting-plane approaches
like the ellipsoid method are computationally prohibitive, we develop an e
fficient algorithm tailored to our setting. Multidimensional backtracking
is provably competitive with the best diagonal preconditioner and requires
no manual tuning.
DTSTAMP:20231103T180246Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COSC seminar: A Solver Framework Powered by Graph Neural Networks f
or Optimization Problems on Graphs from Congsong Zhang
DTSTART:20231120T180000Z
DTEND:20231120T190000Z
UID:84
LOCATION:ASC 301
DESCRIPTION:Congsong Zhang\nTitle: A Solver Framework Powere
d by Graph Neural Networks for Optimization Problems on Graphs\nLocation:
ASC 301\nAbstract: Backtracking with branching heuristics is a common stra
tegy for constraint satisfaction problems and combinatorial optimization p
roblems. Tailored branching heuristics, designed specifically for individu
al problems, can achieve high theoretical efficiency but often at the cost
of added complexity and practical implementation challenges. On the other
hand, general branching heuristics, benefited for their versatility and b
road applicability, may not consistently deliver optimal solutions for spe
cific problems. Our solver framework is designed in the hope of bridging t
hese two approaches. It incorporates Shannon entropy into the design of br
anching heuristics, guiding backtracking algorithms along the least uncert
ainty path, informed by probability distributions that align with the uniq
ue constraints of each problem. This is achieved by graph neural network m
odels, equipped with loss functions inspired by the probabilistic method,
enabling the learning of suitable probability distributions tailored to th
e characteristics of individual problems.
DTSTAMP:20231115T233944Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: TBA from Warren Hare
DTSTART:20240111T220000Z
DTEND:20240111T230000Z
UID:94
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Warren Hare\nTitle: TBA\nLocation: https://ubc.z
oom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: TBA
DTSTAMP:20231120T200957Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: TBA from Charles Audet
DTSTART:20240314T210000Z
DTEND:20240314T220000Z
UID:95
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Charles Audet\nTitle: TBA\nLocation: https://ubc
.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: TBA
DTSTAMP:20231208T191501Z
END:VEVENT
BEGIN:VEVENT
SUMMARY:COCANA seminar: Any-dimensional convex sets from Eitan Levin
DTSTART:20240125T220000Z
DTEND:20240125T230000Z
UID:92
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Eitan Levin\nTitle: Any-dimensional convex sets\
nLocation: https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhV
nAxdz09\nAbstract: Classical algorithms are defined on inputs of different
sizes. In contrast\, data-driven algorithms\, that is\, algorithms learne
d from some data\, may only be defined on inputs of the same size as the d
ata. What does it mean for an algorithm to be defined on infinitely-many
input sizes? How do we describe such algorithms\, and how do we parametriz
e and search over them?\nIn this talk\, we tackle these questions for conv
ex optimization-based algorithms. Describing such algorithms reduces to de
scribing convex sets. These\, in turn\, are often "freely" described\, mea
ning that their description makes instantiation in every dimension obvious
. Examples include unit balls of standard norms defined on vectors of any
size\, graph parameters defined for graphs of any size\, and (quantum) inf
ormation theoretic quantities defined for distributions on any number of (
qu)bits.\nWe show that such free descriptions of convex sets arise from tw
o ingredients. First\, group invariance and the recently-identified pheno
menon of representation stability. Second\, embeddings and projections re
lating different-sized problem instances. We combine these ingredients to
obtain parametrized families of infinitely instantiable convex sets. To
extend a set learned from data in a fixed dimension to higher ones\, we id
entify consistency conditions relating sets in different dimensions that a
re satisfied in a variety of applications\, and obtain parametrizations re
specting these conditions. Our parametrizations can be obtained computatio
nally.\n
DTSTAMP:20231208T210905Z
END:VEVENT
END:VCALENDAR