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DTSTART:20200308T030000
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DTSTART:20201101T010000
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DTSTART:20211107T010000
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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: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: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: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: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: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: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: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: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:COCANA seminar: TBA from Richard Brewster
DTSTART:20230119T233000Z
DTEND:20230120T003000Z
UID:58
LOCATION:https://ubc.zoom.us/j/69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAx
dz09
DESCRIPTION:Richard Brewster\nTitle: TBA\nLocation: https://ubc.zoom.us/j/
69241125133?pwd=QUw2OTdudUl0ODhCdEZ5MFlhVnAxdz09\nAbstract: TBA
DTSTAMP:20221020T185305Z
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: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 defici
ent matrix to Θ on L with respect to the Frobenius norm. This is a noncon
vex problem with several applications in control theory\, computer algebra
\, and computer vision. We introduce a novel semidefinite programming (SDP
) relaxation\, and prove that it always gives the global minimizer of the
nonconvex problem in the low noise regime\, i.e.\, when Θ is close to be
rank deficient. Our SDP is the first convex relaxation for this problem wi
th provable guarantees. We evaluate the performance of our SDP relaxation
in examples from system identification\, approximate GCD\, triangulation\,
and camera resectioning. Our relaxation reliably obtains the global minim
izer under non-adversarial noise\, and its noise tolerance is significantl
y better than state of the art methods.
DTSTAMP:20221108T000132Z
END:VEVENT
END:VCALENDAR