Rahul G. Krishnan

Faculty Member

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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

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.

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

Ali Hossein Foomani Gharari, Michael Cooper, Russell Greiner, Rahul G Krishnan

2023

Machine learning in computational histopathology: Challenges and opportunities

Michael Cooper, Zongliang Ji, Rahul G Krishnan

2023

Characterizing the Progression of Pulmonary Edema Severity: Can Pairwise Comparisons in Radiology Reports Help?

Stephanie M Hu, Steven Horng, Seth J Berkowitz, Ruizhi Liao, Rahul G Krishnan, H Lehman Li-wei, Roger G Mark

2023

Automated screening of computed tomography using weakly supervised anomaly detection

Atsuhiro Hibi, Michael D Cusimano, Alexander Bilbily, Rahul G Krishnan, Pascal N Tyrrell

2023

Duett: Dual event time transformer for electronic health records

Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, Rahul G Krishnan

2023

Structured Neural Networks for Density Estimation and Causal Inference

Asic Chen, Ruian Ian Shi, Xiang Gao, Ricardo Baptista, Rahul G Krishnan

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

Atsuhiro Hibi, Michael D Cusimano, Alexander Bilbily, Rahul G Krishnan, Pascal N Tyrrell, CENTER-TBI and CINTER-TBI Participants and Investigators

2024

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study

Atsuhiro Hibi, Michael D Cusimano, Alexander Bilbily, Rahul G Krishnan, Pascal N Tyrrell

2024

Performance of Large Language Models on Medical Oncology Examination Questions

Jack B Longwell and Ian Hirsch and Fernando Binder and Galileo Arturo Gonzalez Conchas and Daniel Mau and Raymond Jang and Rahul G Krishnan and Robert C Grant

2024

Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

Vahid Balazadeh and Keertana Chidambaram and Viet Nguyen and Rahul G Krishnan and Vasilis Syrgkanis

2024

The association between total social exposure and incident multimorbidity: A population-based cohort study

Ingrid Giesinger and Emmalin Buajitti and Arjumand Siddiqi and Peter M Smith and Rahul G Krishnan and Laura C Rosella

2025

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