Agenda and Zoom Links

Biography: Stefanie Jegelka is an X-Consortium Career Development Associate Professor in the Department of EECS at MIT. She is a member of the Computer Science and AI Lab (CSAIL), the Center for Statistics and an affiliate of IDSS and ORC.

Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems.

Professor Jegelka has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, a Google research award, a Two Sigma faculty research award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). Her research interests span the theory and practice of algorithmic machine learning.

11:00-11:30 Kimberly Skead
Ontario Institute for Cancer Research, University of Toronto, Toronto, Vector Institute for Artificial Intelligence
Opposing evolutionary pressures drive clonal evolution and health outcomes in the aging blood system

11:30-12:00 Morgan Craig
Université de Montréal
Clonal hematopoiesis is accelerated by atherosclerosis

12:00-12:30 Thomas Stiehl
RWTH Aachen University
How cancer stem cell properties shape clonal evolution and disease dynamics in acute myeloid leukemia - insights from mathematical modeling

12:30-1:00 Mia Brunetti
Université de Montréal, Sainte-Justine University Hospital Research Centre
Mathematical modelling of the pre-leukemic phase of AML to evaluate clonal reduction therapeutic strategies

11:00-11:30 Stephanie Waterman
University of British Columbia
Filling in the map: understanding Arctic Ocean mixing space-time geography and its implications

11:30-12:00 Ruth Musgrave
Dalhousie University
Characterizing internal waves and mesoscale eddies at four deep ocean sites

12:00-12:30 Susan Allen
University of British Columbia
Waves over topography in a rotating world: upwelling induced by coastal trapped waves over a submarine canyon

12:30-1:00 Peter Bartello
McGill University
Balance and imbalance in rotating stratified turbulence

11:00-11:30 Aslak Tveito
Simula Research Laboratory
Numerical simulation of cardiac electrophysiology based on representation of individual cells in the EMI model

11:30-12:00 Joyce Reimer
University of Saskatchewan
The Simulation of Long QT Syndrome using the Extracellular- Membrane-Intracellular Model

12:00-12:30 Siqi Wei
University of Saskatchewan
Comparing new second-order operator-splitting methods with Strang

12:30-1:00 Kevin Green
University of Saskatchewan
Towards generic temporal operator splitting methods in deal.ii

11:00-11:30 Haggai Maron
NVIDIA Research
From local structures to size generalization in graph neural networks

11:20-12:00 Renjie Liao
Google Brain and Vector Institute
On the generalization of graph neural networks and their applications in probabilistic inference

12:00-12:30 Kimon Fountoulakis
University of Waterloo
Graph convolution for semi-supervised classification: improved linear separability and out-of-distribution generalization

12:30-1:00 Petar Veličković
DeepMind
Persistent message passing

11:00-11:30 A. Li
Western University
Re-examination of the impact of non-pharmaceutical interventions and media coverage on the COVID- 19 outbreak in Wuhan City

11:30-12:00 R. Fields
University of Guelph
An Age-stratified transmission model of COVID- 19 in Ontario with Google mobility

12:00-12:30 K. R. Fair
University of Guelph
Estimating COVID-19 cases and deaths prevented by non-pharmaceutical interventions in 2020-2021, and the impact of individual actions: a retrospective model-based analysis 

12:30-1:00 D. Flynn-Primrose 
University of Guelph
Towards an agent based model of a child care facility and the problem of validating simulated data

11:00-11:30 Lina von Sydow
Uppsala Universitet
A high order method for pricing of financial derivatives using radial basis function generated finite differences

11:30-12:00 Karel In’t Hout
University of Antwerpen
Operator splitting schemes for option valuation under the two-asset Merton jump-diffusion model

12:00-12:30 Rock Stephane Koffi
University of Cape Town
A fitted multi-point flux approximation method for pricing two-asset options  

12:30-1:00 Yuwei Chen
University of Toronto
Comparison of numerical PDE and asymptotic approaches for stochastic default intensity in bilateral XVA pricing

11:00-11:15 Nazanin Zaker
University of Ottawa
The effect of movement behavior on population density in fragmented landscapes

11:15-11:30 Frithjof Lutscher
University of Ottawa
Competition dynamics of seasonal breeders

11:30-11:45 Peter Harrington
University of Alberta
A framework for studying transients in marine metapopulations

11:45-12:00 Jane Shaw MacDonald
University of Ottawa
Moving Habitat Models: A Numerical Approach

12:00-12:15 Laurence Ketchemen Tchouaga
University of Ottawa
Population density in fragmented landscapes under monostable and bistable dynamics

12:15-12:30 Wayne Enright
University of Toronto
Accurate Approximation of Models of the Spread of Infectious Diseases

12:30-12:45 Prince Harvim
University of Ottawa
Cigarette smoking on college campuses: an epidemical modelling approach

3:00-3:30 Xiaoying Wang
Trent University
Studying social awareness of physical distancing in mitigating COVID-19 transmission

3:30-4:00 Lauren Childs
Virginia Tech
Modeling mitigation strategies to contain COVID-19

4:00-4:30 Matt Betti
Mount Allison University
Combining data forecasting with scenario-based modeling for insights into a rapidly changing outbreak situation

3:00-3:15 Alexander Melnikov
University of Alberta 
On Mathematics of Financial Markets and Contracts

3:15-3:30 Geoffrey McGregor
University of Northern British Columbia 
Conservative Hamiltonian Monte Carlo

3:30-3:45 Jiuda Wu  
Concordia University 
On the dynamics of coupled Mathieu equations

3:45-4:00 William McCann
New Jersey Institute of Technology 
Where Models Fail: Stationary Probability Distributions of Stochastic Gradient Descent and the Success and Failure of the Diffusion Approximation

4:00-4:15 Amjad Khan
Dalhousie University
The role of temperate bacteriophages in the maintenance and distribution of Antibiotic Resistance Genes (ARGs)

4:15-4:30 Michael Lindstrom
University of California, Los Angeles
Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

3:00-3:30 Mark Schmidt
University of British Columbia
Homeomorphic-invariance of EM: non-asymptotic convergence in KL divergence for exponential families via mirror descent

3:30-4:00 Ruth Urner
York University
On the (un-)avoidability of adversarial examples

4:00-4:30 Martha White
University of Alberta
A generalized objective for off-policy value estimation in reinforcement learning

4:30-5:00 Xinhua Zhang
University of Illinois at Chicago
Certified robustness of graph convolution networks for graph classification under topological attacks

3:00-3:30 I. Papst
Cornell University
Mathematical modelling to inform Ontario’s COVID- 19 response: successes, challenges, and lessons learned

3:30-4:00 Z. Mohammadi
University of Guelph
Mobility during the pandemic and its markers of disease spread

4:00-4:30 N. Yu
Ryerson University
Mathematical Modeling and Analysis of Human Gait 

4:30-5:00 M. A. L. Croix
Ryerson University
Cancer: 5-year survival vs. Network Statistics - From Correlation to Application

3:00-3:30 Sebastian Dominguez
University of Saskatchewan
From skeletal to cardiac muscle modelling and vice versa

3:30-4:00 Nilima Nigam
Simon Fraser University
Skeletal muscle: modeling and simulation of isometric contractions

4:00-4:30 Stephanie Ross
Simon Fraser University
An interdisciplinary approach to understanding the contractile con- sequences of muscle tissue mass

4:30-5:00 Ryan Konno
Simon Fraser University
Modelling the multiscale phenomena of diseased skeletal muscle tissue

3:00-3:30 Alexander Bihlo
Memorial University
Physics-informed neural networks for the shallow-water equations on the sphere

3:30-4:00 Yves Bourgault
University of Ottawa
Mass-conservative and positivity preserving second-order methods for high order parabolic equations

4:00-4:30 Martina Bukač
University of Notre Dame
Variable time-stepping methods for fluid-structure interaction problems

4:30-5:00 Fengyan Li
Rensselaer Polytechnic Institute
Asymptotic-preserving IMEX-DG methods for a linear kinetic transport model: different reformulations and IMEX strategies

Biography: Professor Kees Oosterlee has been working on computational problems in finance for 20 years now. Since 2007 he has been a part-time professor in Delft and also a scientist at the Centrum Wisknude & Informatica (CWI) in Amsterdam, where he was also a member of the management team since 2012. From this year on, he holds a chair position at Utrecht University, Mathematical Institute. His main research interests include Fourier expansions, Monte Carlo methods and, recently, also, machine learning in finance.

Professor Oosterlee is co-author of two textbooks (Multigrid, 2001 and Mathematical Modeling and Computation in Finance, 2019), and many scientific publications. Methods he co-developed include the COS method, SWIFT (Shannon Wavelet Inverse Fourier Transform method), SGBM (Stochastic Grid Bundling Method), SCMC (Stochastic Collocation Monte Carlo Method), and the Seven-League scheme (7L).

He was the editor-in-chief of the Journal of Computational Finance, 2013–2018, and has been teaching guest lectures at Oxford University, Hitotsubashi University, Japan, University of A Coruña Spain, among others.

10:30-11:00 Stevan Dubljevic
University of Alberta 
Linear Distributed Parameter Systems (DPS) Moving Horizon Estimation Design

11:00-11:30 Guchuan Zhu
Polytechnique Montreal
Approximations of Lyapunov functionals and input-to-state stability of nonlinear parabolic PDEs

11:30-12:00 Michel Delfour
Universite de Montreal
Quadratic ODE and PDE models of drug release kinetics from biodegradable polymers

12:00-12:30 Roberto Guglielmi
University of Waterloo
Optimal control of the Richards’ equation and optimal irrigation planning

10:30-11:00 Yaowen Lu
University of Queensland
An epsilon-monotone Fourier method for Guaranteed Minimum Withdrawal Benefit (GMWB) as a continuous impulse control problem

11:00-11:30 Alvaro Cartea
University of Oxford
Optimal execution with stochastic delay

11:30-12:00 Christoph Reisinger
University of Oxford
Neural-SDE market models without static arbitrage

12:00-12:30 Dena Firoozi
HEC Montreal
Equilibrium pricing in solar renewable energy certificate (SREC) markets: a mean field game approach

10:30-11:00 Shiyu He
University of Waterloo
Statistical challenges in the analysis of sequence and structure data for the COVID-19 spike protein

11:00-11:30 Michael Lindstrom
University of California Los Angeles
Networks of necessity: simulating strategies for COVID-19 mitigation among disabled people and their caregivers

11:30-12:00 Zongjun Liu
Queen’s University
Conformational variability of loops in the SARS-CoV-2 spike protein

12:00-12:30 Geoffrey McGregor
University of Northern British Columbia
Assessing the effectiveness of regional physical distancing measures of COVID-19 in rural regions of British Columbia

10:30-10:45 Eric Foxall
University of British Columbia Okanagan
Limit processes and bifurcation theory of quasi-diffusive perturbations

10:45-11:00 Matthew Hennessy
University of Oxford 
The electric double layer at the interface between a polyelectrolyte gel and a salt bath

11:00-11:15 Thomasina Ball 
University of British Columbia 
How stable are sliding viscoplastic films?

11:15-11:30 Amine Hanini
University of Ottawa 
Well-balanced schemes for modelling multi-component transport in two-dimensional shallow water flow

11:30-11:45 Hasan Karjoun
Mohammed VI Polytechnic University 
A positivity-preserving numerical scheme satisfying the discrete maximum-minimum principle for surface water flow and solute transport

11:45-12:00 Ariel Dufresne
University of British Columbia 
Interfacial instabilities in a Hele-Shaw cell

12:00-12:15 Sperydon Koumarianos
York University 
Theory and Experiment of Preferential Loop Fractions of Polymers Adsorbed to Silica Nanoparticles

12:15-12:30 Alexey Smirnov
University of Waterloo
NONLINEAR SAR IMAGING VIA CONVEXIFICATION INVERSION METHOD

10:30-11:00 Shuangquan Xie
Tohoku University
Oscillating spots in reaction-diffusion systems

11:00-11:30 Pau Capera-Aragones
University of British Columbia Okanagan
Differential equation model for central-place foragers with memory: implications for bumble bee crop pollination

11:30-12:00 Juncheng Wei
University of British Columbia
Non-hexagonal lattice minimizers in interacting systems

12:00-12:30 Rebecca Tyson
University of British Columbia Okanagan
Investigating the impact of the mycorrhizal inoculum on the resident fungal community and on plant growth

Moderator: Dhavide Aruliah (OpenTeams)

Panelists

Angela Bassa, Senior Director of the Data Science and Analytics Center of Excellence at iRobot

Jessica Cervi, Consultant at EMERITUS

Debaleena Das, Founder of STEM-Away

Jules Kouatchou, Computational Scientist at Science Systems & Applications, Inc. (SSAI)

Erin LeDell, Chief Machine Learning Scientist at H2O.ai

Travis Oliphant, CEO/Founder of OpenTeams & Quansight

Nargol Rezvani, Senior Manager, Machine Learning at Adobe

Melissa Weber Mendonça, Software Engineer at Quansight

The closest point method for solving partial differential equations on surfaces

The numerical approximation of partial differential equations (PDEs) on surfaces poses interesting challenges not seen on flat spaces. The discretization of these PDEs typically proceeds by either parametrizing the surface, triangulating the surface, or embedding the surface in a higher dimensional flat space.   Here we consider an embedding method, the closest point method, which is designed to solve a variety of PDEs on smooth surfaces using a closest point representation of the surface and standard Cartesian grid methods in the embedding space.   An attractive property of the method, in its explicit form, is that it frequently leads to evolution equations that are simply the equations of the corresponding flow in the embedding space.   This advantage means that with the insertion of a simple interpolation step, highly effective 3D numerical PDE codes can be reused to approximate the evolution of PDEs on surfaces.       In this talk, we review the closest point method and present recent results for solving PDEs on moving surfaces, as well as recent domain decomposition methods and software suitable for parallel computing.    

Biography: Hansi Singh is an Assistant Professor in the School of Earth and Ocean Sciences at the University of Victoria. Her research is focused on the physical climate system, particularly the myriad of interactions between atmosphere, ocean, and ice that give rise to the Earth’s climate. She is especially interested in the ever-evolving climate of the polar regions, both Antarctic and Arctic, as well as the large-scale transport of atmospheric water from equator to pole. Her tools are primarily models — from global climate models executed on supercomputers to simple heuristic models that can be analyzed with pencil and paper. Her publications range over a broad set of climate and paleo-climate related topics with several high impact recent contributions in Geophysical Research Letters.

Dr. Singh is a co-chair of the Polar Climate Working Group of the Community Earth System Model, whose development hub is at the National Center for Atmospheric Research in Boulder, Colorado, USA. She received her PhD from the University of Washington in 2016, where she held a US Department of Energy Computational Science Graduate Fellowship. Prior to joining the University of Victoria, she was a Linus Pauling Distinguished Postdoctoral Fellow at Pacific Northwest National Laboratory, sponsored by US Department of Energy Office of Science.

Formal Methods for Control of Dynamical Systems

Motivated by safety-critical control of cyber-physical engineering systems, formal methods for control aims to synthesize controllers for continuous dynamical systems to meet high-level specifications. Finite abstractions, also known as symbolic models, have provided useful means for algorithmically synthesizing hybrid controllers with respect to rigorous specifications (e.g., safety, reachability, or more generally a temporal logic formula). A central theoretical question surrounding abstraction-based control is whether one can decide, through finite abstractions and discrete synthesis, the existence of a controller for a nonlinear system to satisfy a given specification. This question may also have practical implications towards addressing the inherent scalability issues of abstraction-based approaches.

In this talk, we discuss some recent results towards answering this question. We first introduce a method to synthesize robust controllers for temporal logic formulas using finite abstractions. We then use this notion of robustness to show that, if a system robustly satisfies a given specification, then it is possible to use discrete abstractions to synthesize a robust controller. Following this, we present a specification-guided framework to improve the computational performance of abstraction-based methods, while providing the same theoretical guarantees. We conclude by arguing that the intrinsic robustness and controllability of the underlying dynamical system can and should be exploited to address the scalability issues caused by discretization of continuous dynamics and to mitigate the combinatorial explosion imposed by logic specifications.

Moderator: Sue Ann Campbell (University of Waterloo)

Panelists

Dhavide Aruliah (OpenTeams)

Juliette Bruce (University of California, Berkeley)

Amenda Chow (York University)

Edward Doolittle (First Nations University)

Sarafa Iyaniwura (University of British Columbia)

1:30-2:00 Mo Chen
Simon Fraser University
FaSTrack and LR-CAM: two novel applications of pursuit-evasion games

2:00-2:30 Maryam Kamgarpour
University of British Columbia
Learning nash equilibria with bandit feedback

2:30-3:00 Lacra Pavel
University of Toronto
On system theoretic principles for nash equilibrium seeking dynamics

3:00-3:30 Kexue Zhang
University of Calgary
Event-triggered control for nonlinear systems with time delay

1:30-2:00 Felicia Magpantay
Queen's University
Challenges in modeling the transition period of childhood diseases from the pre-vaccine to vaccine era

2:00-2:30 Laurent Potvin-Trottier
Concordia University
Long-term protein-based memory through prion inheritance in single cells

2:30-3:00 Andreas Hilfiger
University of Toronto
Can we infer gene regulation dynamics from static snapshots of gene 
expression variability?

3:00-3:30 Jane Heffernan 
York University
A Model of COVID-19 Vaccination and Waning Immunity in Canada

1:30-2:00 Matt Davison
Western University
Oil market games, nonlinear ODEs, and singularities

2:00-2:30 Sebastian Jaimungal
University of Toronto
Portfolio optimisation within a Wasserstein ball

2:30-3:00 Tony Ware
University of Calgary
Generalized multi-level Monte Carlo method

3:00-3:30 Pieter van Staden
University of Waterloo
A data-driven neural network approach to dynamic factor investing with transaction costs

1:30-2:00 Ryan Thiessen
University of Alberta
Analysis of scaling limits of the kinetic chemotaxis equations

2:00-2:30 Daniel Gomez
University of Pennsylvania
Boundary layer solutions in the singularly perturbed Gierer-Meinhardt model

2:30-3:00 Sean Lawley
University of Utah
Reaction-subdiffusion equations with linear reactions

3:00-3:30 Jason Gilbert
University of Saskatchewan
Narrow escape problems in the elliptic domain and global optimization of locations of traps of different size

1:30-2:00 Mutti Ur Rehman
Massachusetts Institute of Technology
Computing structured singular values

2:00-2:30 Jitendar Singh
Trent University
Influence of geodemographic factors on power consumption

2:30-3:00 Xing Tan
York University
Stochastic sampling on Bayesian network-based models for document ranking

3:00-3:15 Sebastian Moraga
Simon Fraser University 
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data

3:15-3:30 Maksym Neyra-Nesterenko
Simon Fraser University 
Provably Accurate and Stable Deep Neural Networks for Imaging

1:30-2:00 Adriana-Stefania Ciupeanu
University of Manitoba
Bayesian Inference and COVID-19

2:00-2:30 Arma Khan
York University
Numerically modelling respiratory aerosol transport in indoor spaces, considering the implications of HVAC systems

2:30-3:00 Maria M. Martignoni
Memorial University
Optimal public health responses to COVID-19 differ in high and low importation regions

3:00-3:30 Yi Tan
York University
Measuring the effect of behavior change during COVID-19 outbreak: Canada a case study
 

1:30-1:45 Seth Keenan
Thompson Rivers University
Unsteady Ideal Gas Flow Through Porous Media

1:45-2:00 Abhishek Baral
Thompson Rivers University
Modeling Turbulence in Landfill Gas Flow: Ingress into a Horizontal Well

2:00-2:15 Avleen Kaur
University of Manitoba 
A space-time spectral method for the Stokes problem

2:15-2:30 Mohamed Boujoudar
Mohammed VI Polytechnic University 
Space-time localized radial basis function method for modelling water flow in porous media

2:30-2:45 Michael Lamoureux
University of Calgary 
Local factorization of multidimensional discrete differential operators

2:45-3:00 Jonathan Tessier
University of Waterloo 
Quasi-Geostrophic Magnetohydrodynamics

Biography: Peter E. Caines received the BA in mathematics from Oxford University in 1967 and the PhD in systems and control theory in 1970 from Imperial College, University of London, supervised by David Q. Mayne, FRS. In 1980, he joined McGill University, Montreal, where he is Distinguished James McGill Professor and Macdonald Chair in the Department of Electrical and Computer Engineering.

In 2000, his paper on adaptive control with G. C. Goodwin and P. J. Ramadge (IEEE TAC, 1980) was recognized by the IEEE Control Systems Society as one of the 25 seminal control theory papers of the 20th century. He received the IEEE Control Systems Society Bode Lecture Prize in 2009, is a Fellow of  IFAC, CIFAR, SIAM, IEEE, the IMA (UK) and the Royal Society of Canada (2003), and is a member of Professional Engineers Ontario. Peter Caines is the author of Linear Stochastic Systems (Wiley, 1988), which was republished as a SIAM Classic in June 2018, and is a Senior Editor of Nonlinear Analysis — Hybrid Systems. His research interests include stochastic systems, mean field games and control theory, systems on complex networks and hybrid systems theory, together with their applications to natural and artificial systems.

Our muscles aren't one-dimensional fibres

Skeletal muscles possess rather amazing mechanical properties. They are comprised of several tissues, possess an intricate structure, and behave nonlinearly in response to mechanical stresses.  In the 1910s,  A.V. Hill observed muscles heat when they contract, but not when they relax.  Based on experiments on frogs he posited a mathematical description of skeletal muscles which approximated muscle as a 1-dimensional nonlinear and massless spring. This has been a remarkably successful model, and remains in wide use. Yet skeletal muscle is three dimensional, has mass, and a fairly complicated structure. Are these features important? What insights are gained if we include some of this complexity in our models? Complex models can suffer from 'over-fitting' of parameters - how can this be addressed? In this talk, we'll see (partial) answers to some of these questions.

This work was done as part of a long-standing collaboration with James Wakeling's lab at SFU.

Infectious Disease Dynamics from the Black Death to COVID-19

Historical records allow us to reconstruct patterns of disease spread in the past, in some cases going back hundreds of years.  Mathematical models can help us reveal the mechanisms that shaped these epidemics.  I will discuss analyses that identify how demographic and behavioural processes changed the structure of recurrent epidemics of childhood infections, such as measles and whooping cough, in the 20th century.  I will also describe recent work that illuminates epidemic patterns as far back as the Black Death in the 14th century, and how COVID-19 presents some of the same, and some very different, challenges.

11:00-11:30 Hans De Sterck
University of Waterloo
Convergence acceleration for nonlinear fixed-point methods

11:30-12:00 Lars Ruthotto
Emory University
A machine learning framework for mean field games and optimal control

12:00-12:30 Gitta Kutyniok Cancelled
Ludwig-Maximilians-Universität München
Deep learning meets shearlets: towards interpretable image reconstruction

12:30-1:00 Carola-Bibiane Schönlieb
University of Cambridge
Data-driven solutions to inverse problems

11:00-11:30 Qi Feng 
University of Michigan
Cubature Method for Volterra SDEs and Rough Volatility Model

11:30-12:00 Ethan Johnson-Skinner
Ryerson University
A Novel Algorithmic Trading Strategy using Hidden Markov Model for Kalman Filtering Innovations

12:00-12:30 Weijie Pang
McMaster University
Systemic Risk in Repurchase Agreement Markets

12:30-1:00 Xuekui Zhang 
University of Victoria
Novel Modelling Strategies for High-frequency Stock Trading Data

11:00-11:30 Smita Sahu
University of Portsmouth
DandeLiion: A ultra-fast solver for Doyle-Fuller-Newman models of lithium-ion battery discharge

11:30-12:00 Jose Morales-Escalante
McMaster University
Discerning Models of Phase Transformation in Porous Graphite Electrodes: Insights from Inverse Modelling Based on Mri Measurements

12:00-12:30 Maricela McKay
University of British Columbia
Deep Neural Network Approximation of Li-ion Battery Models 

11:00-11:30 Sherry Zhu
York University
Graph neural network approach to cross lingual entity alignment and its applications

11:30-12:00 Ryan Zhou
Queen’s University
Graph LSTM: learning graph relationships in spatiotemporal data for time series forecasting

12:00-12:30 Jiashu (Jessie) Zhao
Wilfrid Laurier University
Modeling biases in learning to rank systems

12:30-12:45 Juan Cardenas
Simon Fraser University
Adaptive sampling and domain learning strategies for multivariate function approximation on known and unknown domains

11:00-11:15 Sana Keita
University of Ottawa 
Implicit and semi-implicit second-order time stepping methods for the Richards equation

11:15-11:30 Cristina Anton
Grant MacEwan University 
Stochastic Runge-Kutta pseudo-symplectic methods

11:30-11:45 Paul Muir
Saint Mary's University 
Accurate defect estimation for continuous numerical solutions of ordinary differential equations

11:45-12:00 Krishna Dutt
University of Waterloo 
A high-order moment limiter for the discontinuous Galerkin method on triangular meshes

12:00-12:15 Zewen Shen
University of Toronto 
Numerical Computation of the Tracy-Widom Distribution

12:15-12:30 Sarah Nataj
University of Manitoba 
Superlinear convergence of  BFGS method by using Kantorovich type assumptions

12:30-12:45 Diane Fokoue
University of Ottawa 
Various Numerical Methods for the Microscopic Cardiac Electrophysiology Model

Panelists

Kori Czuy (Spark Science Centre)

Edward Doolittle (First Nations University)

Petra Menz (Simon Fraser University)

Nathan Rowbottom (Six Nations Polytechnic)

Multi-marginal optimal transport: the good, the bad and the ugly

Optimal transport is the general problem of trying to pair two distributions of mass (the marginals) with maximal efficiency, relative to a given cost function (think, for example, of using a pile of dirt to fill a hole of the same volume, so as to minimize the average distance the dirt moves). This is a vibrant area of modern mathematics, touching on analysis, partial differential equations, geometry and probability, with far reaching and diverse applications in areas such as meteorology, operations research, fluid mechanics, finance and biology, to name only a few. Solutions to this problem are by now fairly well understood; in particular, under reasonable conditions, they concentrate on graphs over the first variable.

Over the past decade, largely driven by its own diverse collection of applications (including, for example, matching agents in multi-sided markets in economics, interpolating among distributions in data science, and minimizing interaction energies between electrons in quantum physics) interest has grown rapidly in multi-marginal optimal transport, in which there are several, rather than two, distributions to be matched. Solutions to this problem exhibit an intricate dependence on the cost function. For some costs, solutions concentrate on graphs over the first variable as in the more classical, two marginal case (I think of this structure as “good”), whereas for others, solutions can be much more exotic (I think of this structure as “bad,” or in the worst cases, “ugly”). In this talk, I will survey the state of the art, and in particular attempt to develop some intuition for the dichotomy between these two types of costs, illustrating the discussion with examples arising in physics, economics, and data science. If time permits, I will briefly discuss some ongoing work and point to some open problems.

Biography: Ben Adcock is an Associate Professor in the Department of Mathematics at Simon Fraser University. He received the CAIMS/PIMS Early Career Award (2017), an Alfred P. Sloan Research Fellowship (2015) and a Leslie Fox Prize in Numerical Analysis (2011).

Professor Adcock’s work has been published in venues such SIAM Review, Proceedings of the National Academy of Sciences, Foundations of Computational Mathematics and featured on the cover of SIAM News. His research interests include numerical analysis, mathematics of data science, approximation theory and computational harmonic analysis. He received the CAIMS/PIMS Early Career Award in 2017, an Alfred P. Sloan Research Fellowship in 2015, and a Leslie Fox Prize in Numerical Analysis in 2011. He has published over 40 peer-reviewed journal articles, 15 conference proceedings, and two book chapters. 

Professor Adcock is currently PIMS-SFU Site Director and a member of the CAIMS board. He is also a member of the Editorial Board for the SIAM Journal on Scientific Computing, and the SIAM Computational Science and Engineering book series. From 2015–2017 he was secretary of the SIAM Pacific Northwest Section. He is the primary organizer for the 2020 Foundations of Computational Mathematics conference.