CVPR 2019

Accepted Papers

DeepFlux for Skeletons in the Wild
By Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi


Scene Categorization from Contours: Medial Axis Based Salience Measures
By Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B. Walther, Allan Jepson, Sven Dickinson, Kaleem Siddiqi


ICLR 2019

Accepted Papers

Explaining Image Classifiers by Counterfactual Generation
By Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud


Visual Reasoning by Progressive Module Networks
By Seung Wook Kim, Makarand Tapaswi, Sanja Fidler


Three Mechanisms of Weight Decay Regularization
By Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse


LanczosNet: Multi-Scale Deep Graph Convolutional Networks
By Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel


Excessive Invariance Causes Adversarial Vulnerability
By Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge


Neural Graph Evolution: Automatic Robot Design
By Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba


DOM-Q-NET: Grounded RL on Structured Language
By Sheng Jia, Jamie Ryan Kiros, Jimmy Ba


TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
By Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse


Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
By Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel


Aggregated Momentum: Stability Through Passive Damping
By James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse


Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
By Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse


Graph HyperNetworks for Neural Architecture Search
By Chris Zhang, Mengye Ren, Raquel Urtasun


Functional Variational Bayesian Neural Networks
By Shenyang Sun, Guodong Zhang, Jiaxin Xi, Roger Grosse.

Accepted Oral

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
By Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

NIPS 2018

Accepted Papers

Neural Guided Constraint Logic Programming for Program Synthesis
By Lisa Zhang · Gregory Rosenblatt · Ethan Fetaya · Renjie Liao · William Byrd · Matthew Might · Raquel Urtasun · Richard Zemel


Learning Latent Subspaces in Variational Autoencoders
By Jack Klys · Jake Snell · Richard Zemel


Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
By David Madras · Toni Pitassi · Richard Zemel


Reversible Recurrent Neural Networks
By Matthew MacKay · Paul Vicol · Jimmy Ba · Roger Grosse


On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
By Maziar Sanjabi · Jimmy Ba · Meisam Razaviyayn · Jason Lee


Neural Ordinary Differential Equations
By Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud


Isolating Sources of Disentanglement in Variational Autoencoders
By Tian Qi Chen · Xuechen Li · Roger Grosse · David Duvenaud


Iterative Value-Aware Model Learning
By Amir-massoud Farahmand


A Neural Compositional Paradigm for Image Captioning
By Bo Dai · Sanja Fidler · Dahua Lin


Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
By Abdullah Rashwan · Agastya Kalra · Pascal Poupart · Prashant Doshi · Georgios Trimponias · Wei-Shou Hsu


Monte-Carlo Tree Search for Constrained POMDPs
By Jongmin Lee · Geon-hyeong Kim · Pascal Poupart · Kee-Eung Kim


Unsupervised Video Object Segmentation for Deep Reinforcement Learning
By Vikash Goel · Jameson Weng · Pascal Poupart


Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
By Priyank Jaini · Pascal Poupart · Yaoliang Yu


Data-dependent PAC-Bayes priors via differential privacy
By Gintare Karolina Dziugaite · Daniel Roy


Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
By Hassan Ashtiani, Shai Ben-David, Nick Harvey, Chris Liaw, Abbas Mehrabian, Yaniv Plan


Non-convex Optimization with Discretized Diffusions
By Murat A Erdogdu, Lester Mackey, Ohad Shamir


Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
By Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, Timothy Lillicrap


Occam’s razor is insufficient to infer the preferences of irrational agents
By Stuart Armstrong, Sören Mindermann


Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel G Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca

ICML 2018

Accepted Papers and Oral

Neural Relational Inference for Interacting Systems
By Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel


Distilling the Posterior in Bayesian Neural Networks
By Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel


Reviving and Improving Recurrent Back-Propagation
By Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · KiJung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel


Learning Adversarially Fair and Transferable Representations
By David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel


Inference Suboptimality in Variational Autoencoders
By Chris Cremer · Xuechen Li · David Duvenaud


Noisy Natural Gradient as Variational Inference
By Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse


Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
By Yangchen Pan · Amir-massoud Farahmand · Martha White · Saleh Nabi · Piyush Grover · Daniel Nikovski


Differentiable Compositional Kernel Learning for Gaussian Processes
By Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse


Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
By Gintare Karolina Dziugaite · Daniel Roy


Learning to Reweight Examples for Robust Deep Learning
By Mengye Ren · Wenyuan Zeng · Bin Yang · Raquel Urtasun


Nature: International Journal of Science

Published Letter

Observation of topological phenomena in a programmable lattice of 1,800 qubits

By Andrew D. King, Juan Carrasquilla, Jack Raymond, Isil Ozfidan, Evgeny Andriyash, Andrew Berkley, Mauricio Reis,  Trevor Lanting, Richard Harris, Fabio Altomare, Kelly Boothby, Paul I. Bunyk, Colin Enderud, Alexandre Fréchette,  Emile Hoskinson, Nicolas Ladizinsky, Travis Oh, Gabriel Poulin-Lamarre, Christopher Rich, Yuki Sato,  Anatoly Yu. Smirnov, Loren J. Swenson, Mark H. Volkmann1, Jed Whittaker1, Jason Yao1, Eric Ladizinsky, Mark W. Johnson, Jeremy Hilton & Mohammad H. Amin

Published Article

Machine learning for MEG during speech tasks

By Demetres Kostas, Elizabeth W. Pang & Frank Rudzicz

CVPR 2018


Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Spotlight 3330


MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Spotlight 3308

Accepted Posters

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
By David Acuna, Huan Ling, Amlan Kar and Sanja Fidler
Poster 3409


Learning to Act Properly: Predicting and Explaining Affordances from Images
By Ching-Yao Chuang, Jiaman Li, Antonio Torralba and Sanja Fidler
Poster 631


SurfConv: Bridging 3D and 2D Convolution for RGBD Images
By Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun and Sanja Fidler
Poster 711


A Face to Face Neural Conversation Model
By Hang Chu and Sanja Fidler
Poster 710


Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Poster 3330


VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Poster 3399


MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Poster 3308


Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
By Fabien Baradel, Christian Wolf, Julien Mille, and Graham Taylor
Poster 182


VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Oral 3399

ICLR 2018

Accepted Papers

Kronecker-factored Curvature Approximations for Recurrent Neural Networks

By James Martens, Jimmy Ba, Matt Johnson


Quantitatively Evaluating GANs With Divergences Proposed for Training

By Daniel Jiwoong Im, Alllan He Ma, Graham W. Taylor, Kristin Branson


Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

By Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse


Attacking Binarized Neural Networks

By Angus Galloway, Graham W. Taylor, Medhat Moussa


Meta-Learning for Semi-Supervised Few-Shot Classification

By Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, Richard S. Zemel


Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

By Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud


Understanding Short-Horizon Bias in Stochastic Meta-Optimization

By Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse


NerveNet: Learning Structured Policy with Graph Neural Networks

By Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler

Workshop Papers

Predict Responsibly: Increasing Fairness by Learning to Defer

By David Madras, Toniann Pitassi, Richard Zemel


Isolating Sources of Disentanglement in Variational Autoencoders

By Tian Qi Chen, Xuechen Li, Roger Grosse, David Duvenaud


Graph Partition Neural Networks for Semi-Supervised Classification

By Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel


Gradient-based Optimization of Neural Network Architecture

By Will Grathwohl, Elliot Creager, Seyed Ghasemipour, Richard Zemel


Reconstructing evolutionary trajectories of mutations in cancer

By Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit Deshwar, Nil Sahin, Quaid Morris


Inference in probabilistic graphical models by Graph Neural Networks

By KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow


Leveraging Constraint Logic Programming for Neural Guided Program Synthesis

By Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Raquel Urtasun, Richard Zemel


Deep Learning and Reinforcement Learning Summer School 2018

2018- Deep Learning Summer School

Katherine A. Heller, Department of Statistical Science, Duke University
Title: Introduction to Machine Learning

Hugo Larochelle, Google
Title: Neural Networks I

David Duvenaud, Department of Computer Science, University of Toronto
Title: Autodiff

Hugo Larochelle, Google
Title: Neural Networks II

Jonathon Shlens, Google
Title: Introduction to Convolutional Neural Networks (CNNs)

Sanja Fidler, Department of Computer Science, University of Toronto
Title: Advanced Deep Vision

Balázs Kégl, Laboratoire de l Accélérateur Linéaire, University of Paris-Sud 11
Title: RAMP (Practical session)

David Duvenaud, Department of Computer Science, University of Toronto
Title: Generative Models I

Been Kim, Google
Title: Interpretability

Sanjeev Arora, Department of Computer Science, Princeton University
Title: Theory

Jimmy Ba, Department of Computer Science, University of Toronto
Title: Optimization I

Jorge Nocedal, Robert R. McCormick School of Engineering and Applied Science, Northwestern University
Title: Optimization II

Yoshua Bengio, Department of Computer Science and Operations Research, University of Montreal
Title: Recurrent Neural Networks (RNNs)

Graham Neubig,  Language Technologies Institute, Carnegie Mellon University
Title: Language Understanding

Jamie Kiros, Department of Computer Science, University of Toronto
Title: Multimodal Learning

Blake Aaron Richards, Centre for the Neurobiology of Stress (CNS), University of Toronto Scarborough
Title: Computational Neuroscience

Andrew Gordon Wilson, Department of Computer Science, Cornell University
Title: Bayesian Neural Nets

Sageev Oore, Vector Institute
Title: Deep Learning and Music

2018- Reinforcement Learning Summer School

Richard S. Sutton, Department of Computing Science, University of Alberta
Title: Introduction to RL and TD

Sergey Levine, Department of Electrical Engineering and Computer Sciences, UC Berkeley
Title: Policy Search

Amir-massoud Farahmand, Vector Institute
Title: Batch RL and ADP

Martha White, Department of Computing Science, University of Alberta
Title: Off-Policy Learning

Tor Lattimore, DeepMind Technologies Limited
Title: Bandits and Explore/Exploit in RL

Doina Precup, School of Computer Science, McGill University
Title: Temporal Abstraction

Emma Brunskill, Computer Science Department, Carnegie Mellon University
Title: Multi-task and Transfer in RL

Marc G. Bellemare, Google
Title: Deep RL

Hal Daumé III, Department of Computer Science, University of Maryland
Title: Imitation Learning

Mohammad Ghavamzadeh, INRIA Lille – Nord Europe
Title: Safety in RL

Michael Bowling, Department of Computing Science, University of Alberta
Title: Multi-agent RL


Presented at The Fields Institute


Presented at Fields Institute

Barak Pearlmutter, Hamilton Institute, Maynooth
Near-Criticality and Pathology in the Central Nervous System

Suchi Saria, Johns Hopkins University
Individualizing Healthcare with Machine Learning (Video)

Peter Grünwald, Leiden University
Learning the learning rate: how to repair Bayes when the model is wrong (Video)

Emma Brunskill, Stanford University
Reinforcement Learning When Experience is Expensive (Video)

Josh Tenenbaum, Massachusetts Institute of Technology
Building machines that learn and think like humans (Video)

Chris Maddison, University of Oxford
Relaxed Gradient Estimators (Video)

Yair Weiss, Hebrew University
Learning the Statistics of Full Images (Video)

Christopher Manning, Stanford University
Towards a better model for neural network reasoning (Video)

Vlad Mnih, DeepMind
Efficient Multi-Task Deep Reinforcement Learning (Video)

Stephen Friend, Sage Bionetworks
Exploring fundamental unknowns that prevent us from using our devices to navigate between disease and health (Video)

Ilya Sutskever, OpenAI
Meta Learning and Self Play (Video)

Yee Whye Teh, University of Oxford
DisTraL: Distill and Transfer for Deep Multitask Reinforcement Learning (Video)

Guang Wei Yu (Layer 6), Mohammad Islam (Wattpad), Putra anggala (Shopify), and Javier Moreno (Rubikloud)
Recommendation Systems (Video)

Jennifer Listgarten, Microsoft Research
From Genetics to CRISPR Gene Editing with Machine Learning (Video)

Chris Williams, Univeristy of Edinburgh
Artificial Intelligence for Data Analytics (Video)

Jon Shlens, Google Brain
Learning representations of the visual world

Geoffrey Hinton, University of Toronto
What is wrong with convolutional neural nets? (Video)


Presented at Fields Institute

David Sontag, Massachusetts Institute of Technology
How is Machine Learning Going to Change Health Care? (Video)

Alex Graves, DeepMind
Frontiers in Recurrent Neural Network Research

Max Welling, University of Amsterdam
Generalizing convolutions for Deep Learning (Video)

Ernest Earon, Precision Hawk
Airborne Intelligence: Why birds are so good at what they do (Video)

James Bergstra, Kindred
From Teleoperation to AGI (Video)

Ryan Geriepy, ClearPath Robotics
3 Steps To Applying Machine Learning To Fleets Of Self-Driving Vehicles (Video)

Shane Gu, Cambridge University
Sample-Efficient Deep Reinforcement Learning for Robotics (Video)

David Blei, Columbia University
Probabilistic Topic Models and User Behavior (Video)

Fernanda Viegas, Martin Wattenberg and Daniel Smilkov, Google
Visualization for machine learning–and human learning, too (Video)

Roger Melko, University of Waterloo
Machine Learning Quantum Physics (Video)

Pieter Abbeel, University of California, Berkeley
Deep Reinforcement Learning for Robotics (Video)

Roger Grosse, University of Toronto
Optimizing neural networks using structured probabilistic models (Video)

Graham Taylor, University of Guelph
Dataset Augmentation in Feature Space (Video)

Rob Fergus, New York University / Facebook
Memory and Communication in Neural Networks (Video)

Hugo Larochelle, Google Brain

Autoregressive Generative Models with Deep Learning (Video)


Michael Schull, Institute for Clinical Evaluative Sciences
ICES: Linking data and discovery, and building a health data science agenda (Video)

Alexandre Le Bouthillier, Imagia
Imagia: Artificial Intelligence for medical image analysis (Video)

Brendan Frey, Deep Genomics
Deep Genomics: Changing the course of genomic medicine (Video)

Joshua Landy, Figure 1
Figure 1: The computer will see you now (Video)

Ofer Shai, Meta Inc.
Meta: Unlocking the world’s scientific and technical insights (Video)

Roger Grosse, David Duvenaud and Sanja Fidler,, University of Toronto
Meet the New Faculty: Roger Grosse, David Duvenaud & Sanja Fidler (Video)

Andrew McCallum, University of Massachusetts
Universal Schema for Representation and Reasoning from Natural Language (Video)

Richard Sutton, University of Alberta
The Future of Artificial Intelligence Belongs to Search and Learning (Video)

Geoffrey Hinton, University of Toronto
Using Fast Weights to Store Temporary Memories (Video)

Amir Ban, Tel-Aviv University
When Should an Expert Make a Prediction? (Video)



Gillian Hadfield, University of Toronto
AI Alignment and Human Normativity (Video)
Vector Institute

Scott Sanner, University of Toronto
Autoencoders for Collaborative Filtering (Video)
Vector Institute

Peter Wittek, University of Toronto
Machine Learning on Near-Term Quantum Computers (Video)
Vector Institute

Jesse Hoey, University of Waterloo
Affective Dynamics and Control in Group Processes (Video)
Vector Institute

Andreas Moshovos, University of Toronto
Value-Based Deep Learning Hardware Acceleration (Video)
Vector Institute

Micheal Brudno, University of Toronto
Improving Doctor-Patient Interaction with AI-Enabled Medical Note Taking (Video)
Vector Institute



Bartha Knoppers, McGill University
Data Sharing and the Human Right to Benefit From Science and its Applications (Video)
Vector Institute

Yin Aphinyanaphongs, NYU Langone Health
Clinical Predictive Analytics at NYU Langone Health (Video)
Vector Institute



Presented at Centre of Ethics, University of Toronto

Richard Zemel, University of Toronto
Ensuring Fair and Responsible Automated Decisions (Video)
Centre of Ethics, University of Toronto

Frank Rudzicz, University of Toronto, University Health Network, Winterlight Labs
The Future of Automated Healthcare (Video)
Centre of Ethics, University of Toronto


Faculty Affiliates

Yuhong Guo Suzanna Becker Neil Bruce Aleksandar Nikolov
Andreas Moshovos Blake Richards Gennady Pekhimenko Gillian Hadfield
Radford Neal Suzanne Stevenson Yang Xu Dana Kulic
Jimmy Lin Kate Larson Roger Melko Yaoliang Yu
Olga Veksler James Elder Jim Reilly John Connolly
Ranil Sonnadara Reza Samavi Thomas Doyle Konstantinos Derpanis
Alex Mihailidis Angela Schoellig Anne Martel Ashton Anderson
Babak Taati Benjamin Haibe-Kains Gerald Penn Graeme Hirst
Jason Anderson Kyros Kultulakos Michael Brudno Paul Boutros
Scott Sanner Sheila McIlrath Sven Dickinson Toniann Pitassi
Vaughn Betz Michael Hoffman Ali Ghodsi Jesse Hoey
Shai Ben-David Jody Culham Mark Daley Robert Mercer
Marcus Brubaker Yuri Boykov Ajay Agrawal Albert Yoon
Anthony Niblett Avi Goldfarb Benjamin Alarie Peter Wittek
Jonathan Kelly Maura Grossman Hui Jiang Hamid Reza Tizhoosh
Ruth Urner Tom Chau Laura Rosella


Mario Krenn Loic Roch Alina Selega Chendi Wang
Scott lowe Zacharie Naulet Faiza Khan Khattak Bo Zhao
Ethan Fetaya Marc Law Bradly Stadie Ali Ramezani-Kebrya
Joern Jacobson Shalmali Joshi Melissa McCradden Zamyla Chan
Linda Sunderman

Graduate Students and Student Interns

Chunhao Chang Mingjie Mai George Adam Ladislav Rampasek
Shems Saleh Sana Tonekaboni Daniel Hidru Lauren Erdman
Aziz Mezlini Angeline Yashodara Alexander Edmonds Ekansh Sharma
Christopher Srinivasa Haosui Duanmu Alex Gao Jeffrey Negrea
Jun Yang Yangbo Tang Jonathan Lorraine Geoffrey Roeder
Bowen Xu Tian Qi Chen Eric Langlois Chris Cremer
Will Grathwohl Guodong Zhang Jesse Bettencourt Matthew MacKay
Micha Livne Ali Punjani Fartash Faghri Farzaneh Mahdisoltani
Serena Jeblee Bai Li Mohamed Abdalla Akshay Budhkar
Demetres Kostas Daniyal Liaqat Fizza Ahmad Sheikh Stefania Raimondo
Arvid Fydenlund Ekaterina Kudashkina Thorsteinn Jonsson Colin Brennan
Vithursan Thangarasa Bradley Kennedy Alaaeldin Ali Shamak Dutta
Adam Balint Brendan Duke Angus Galloway Eu Wern Teh
Devinder Kumar Terrance Devries Michal Lisicki Boris Knyazev
Harris Chan Kaitlin Laverty Gurnit Atwal Kun Nie
Yulia Rubanova Alexander Sasse Nil Sahin Jeffrey Wintersinger
Simon Eng Amit Deshwar Kevin Ha Andrew Toulis
Ioan Andrei Barsan Chenxue Zhang Renjie Liao Eleni Triantafillou
Marc-Etienne Brunet David Madras Kamyar Seyed Ghasemipour Elliot Creager
Mengye Ren Jake Snell Kuan-Chieh Wang James Lucas
William Saunders Shengyang Sun Yuhuai  Wu Paul Adrian Vicol
Atef Chaudhury Amlan Kar Seung Wook Kim Jiaman  Li
Tingwu Wang Tingke Shen Wenzheng Chen Chaoqi  Wang
Hang Chu Kaustav Kundu Maria Shugrina Ricardo Salmon
Abdullah Rashwan Paulo Pachecho Priyank Jaini Joseph Scott
Jeffrey Rudd Guojun Zhang Ankit Vahdera Pan Pan Cheng
Kira Selby Rong Tian Tse Safwan Hossain John Chen
Haochen Zhang Soren Sabet Sarvestany Sheldon Huang Huan Ling
Gintare Karolina Dziugaite Rachid  Riad Kiarash Jamali Aidan Gomez
Sheng Jia Mufan Li YueLan Qin Ariel Herbert-Voss
Philippe Casgrain Nathan Killoran Oren Kraus Marissa Weis
Michael Leung Hannes Bretschneider Alice Gao
Romina Abachi Amir Hosein Khas Ahmadi Yuhao Zhou  

Postgraduate Affiliates – 2019-20 Cohort

Anastasia Razdaibiedina Anjali Silva Anna Golubeva Buser Say
Kyle Mills Lee Clement Lydia Y. Liu Lina Tran
Marie-Julie Favé Martin Magill Matthew Giamou Matthew J. S. Beach
Md Amirul Islam Michael Ridley Muhammad Raisul Alam Rafid Mahmood
Sayeh Sharifymoghaddam Xingyu Li Zhaleh Safikhani

Postgraduate Affiliates – 2018-19 Cohort

Tristan Aumentado-Armstrong Mahtab Ahmed Shazia Akbar Aryan Arbabi
Nabiha Asghar Ahmed Ashraf Seyed Ershad Banijamali Felix Berkenkamp
Rober Boshra Omar Boursalie Andrew Boutros Alberto  Camacho
Jaskiret    Dhindsa Elham Dolatabadi Mohamed Helwa Kathleen Houlahan
Ethan Jackson Mehran Karimzadeh Reghbati Shehroz Khan Jin Hee Kim
Dmitry Marin Sean Robertson Petr Smirnov Ian Smith
Laleh Soltan Ghoraie Robin Swanson Matthew Tesfaldet Rodrigo Andres  Toro Icarte
Stavros Tsogkas Ga Wu SiQi Zhou Mohammad Hassan Zokaei Ashtiani

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