Rahul G. Krishnan is Assistant Professor of Computer Science and Laboratory Medicine and Pathobiology at the University of Toronto. He is a Canada CIFAR AI Chair and a Faculty Member of the Vector Institute. He completed his PhD in Electrical Engineering and Computer Science in 2020 from the Massachusetts Institute of Technology after which he worked as Senior Researcher at Microsoft Research, New England. Rahul received a BaSc in Computer Engineering from the University of Toronto, and an MS in computer science from New York University. Rahul’s research focuses on building novel algorithms for decision making to automate clinically meaningful problems, and to advance our understanding of human health.
Assistant Professor, Department of Computer Science, University of Toronto
Assistant Professor, Department of Laboratory Medicine and Pathobiology, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Machine Learning
- Machine Learning for Health and Computational Medicine
- Deep Generative Models
- Probabilistic Inference
- Causal Inference
Highlights
- Co-host of the podcast What Now AI! by the University of Toronto
- Tier 2 Canada Research Chair in Computational Medicine.
- Amazon Research Award (2023)
- Disney Research Award at the Advances in Bayesian Inference Workshop
Publications
Copula-based deep survival models for dependent censoring
2026
Machine learning in computational histopathology: Challenges and opportunities
2023
Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help?
2023
Automated screening of computed tomography using weakly supervised anomaly detection
2025
Duett: Dual event time transformer for electronic health records
2023
Structured Neural Networks for Density Estimation and Causal Inference
2024
Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study
2024
Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study
2026