Invited Talks & Media

Media

November — Deep learning and causality – bridging the gap, Samsung Research
October — Reliable Decision Making in Medicine: Survival Analysis and Causality, Helmholtz Munich
August — Reliable Decision Making in Medicine: Survival Analysis and Causality, Oxford University (Tutorial, MLxHealth Summer School)
July — Reliable Decision Making in Medicine, University College London
July — Reliable Decision Making in Medicine, Ludwig Maximilian University, Munich (Keynote, Causal Inference Workshop)
July — Reliable Decision Making with Structured and Unstructured Data, SAP
May — Deep Learning and Causal Inference, Statistical Society of Canada
April — Towards Neural Networks that Reason Causally, AutoDesk Research
April — From associational to causal predictions with deep learning, Google Research
March — From associational to causal predictions with deep learning, ETH Zurich, Zurich, Switzerland
February — From associational to causal predictions with deep learning, Schwartz Reisman Institute, Toronto

November — AI Agents in Healthcare, Association for AI in Medicine (Virtual)
September — AI in Pathology, Laboratory Medicine and Pathobiology Residents, Toronto
July — Deep Learning and Causality – Bridging the Gap, UCLA Computational Medicine Summer School, Los Angeles, USA
July — The case for causality in medicine, Oxford MLxHealth Summer School, Oxford, UK
June — Causality, CIFAR Summer School, Toronto
June — Maximizing the Impact of ML in Healthcare, University of Toronto Research Ethics Board, Toronto
May — Causality and Deep Generative Models, University of Calgary, Calgary
April — Causality and Deep Generative Models, American Causal Inference Conference, Seattle
March — MultiResFormer – Adaptive Time Series Modeling, Vector FastLane Startup Series, Toronto
March — ML and healthcare – models, algorithms and applications, Institute of Public Health, Toronto
February — Modeling functional (In)dependence with Neural Networks, SIAM Uncertainty Quantification, Trieste, Italy
February — Guardrails for detecting model failures in medicine, Kaiser Permanente Medical Center

December — ML in healthcare, LG AI Discovery Forum
November — Deep learning for waitlist prioritization, UHN Transplant Symposium
October — ML in Healthcare – Models, Algorithms and Applications, Krembil Computational Neuroscience Symposium
August — Dependent censorship in survival analysis, UCLA
June — Trustworthy ML in Healthcare, MILA (Virtual)
May — Technion (Israel) Machine Learning Meeting
April — Five things to know about Machine Learning, AI and the Eye Conference
April — Machine Learning for Healthcare, UHN Allo-BMT Group Meeting
March — Auditing predictive models in healthcare, Vector-RIKEN Workshop
March — Partial Identification with Implicit Generative Models, Brock University Data Science Talk
February — From data to decision making in medicine, Vector Endless Summer School (Guest Speaker)

November — Learning representations of histopathological images with self-supervised learning, Takeda AI/ML Days
September — Bias in Healthcare: Clustering Interval Censored Time Series Data, University of Toronto Undergraduate AI Ethics Hackathon
May — SubLign: A Deep Generative Model for Clustering Censored Time Series Data, Takeda Corporation
May — Machine Learning for Healthcare, CIFAR Board Meeting
May — Probabilistic Modeling for Disease Progression, Worlds AI Summit
April — SubLign – A Deep Generative Model for Clustering Interval Censored Time-Series Data, Laboratory Medicine and Pathobiology Research Conference
March — Machine Learning for Healthcare, Canadian Society for Clinical Chemists
January — SubLign – A Deep Generative Model for Clustering Interval Censored Time-Series Data, Fields-Vector Machine Learning Seminar

November — Machine Learning for Chronic Disease Management, Toronto Machine Learning Summit
September — Machine Learning for Chronic Disease Management, Temerty Centre for AI Research in Medicine
April — AI in healthcare for COVID-19, Canada-India Healthcare Summit (Panelist)

April — Deep Markov Models for clinical data, Google Research
March — Deep Markov Models for clinical data, Broad Institute – ML for Cardiovascular Disease
March — Deep Markov Models for clinical data, University of Toronto – Department of Computer Science and Medicine
March — Deep Markov Models for clinical data, New York University – Department of Computer Science and Medicine
February — Deep Markov Models for clinical data, Columbia University – Department of Biomedical Informatics
January — Deep Markov Models for clinical data, Microsoft Research

January — Structured Inference Networks for Nonlinear State Space Models, Verily Inc.

Rahul G. Krishnan

Department of Computer Science

Department of Laboratory Medicine and Pathobiology

University of Toronto

Office:

Toronto, Ontario

M5S 3G4

Canada

Email: ‘rahulgk‘ @ ‘cs.toronto.edu‘