Rahul G. Krishnan
Assistant Professor
Develops deep generative models and causal inference methods for machine learning in healthcare.
Rahul G. Krishnan's lab at Toronto works on probabilistic & causal inference, generative & foundation models and efficient training & inference. 32 papers since 2023, 25 at top venues, mostly NeurIPS and ICML. 10 PhD students and 1 master's student. 30 alumni, most often to Cerebras Systems. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 78%
- PhD students
- 10
- MS students
- 1
- alumni listed
- 30
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 10 · collecting
- 10 papers2025 · T1 8
- 6 papers2024 · T1 3
- 6 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, EMNLP, Findings of ACL
Research focuspaper titles that use each topic's words
- Generative & foundation models7 papers
- Probabilistic & causal inference6 papers
- Efficient training & inference6 papers
- inference methods0 papers
- develops deep0 papers
Paper list
From the lab homepage.
202610 papers
- ICLR (T1)Can we generate portable representations for clinical time series data using LLMs?
- KDD (T1)Causal methods for LLM development and evaluation
- NeurIPS (T1)DynaSub: Adaptive Subgrouping for Scalable Representation Learning
- ICML (T1)Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
- EMNLP (T1)MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Scaling of Diffusion Language Models
- ICLR (T1)Mitigating Privacy Risk via Forget Set-Free Unlearning
- EMNLP (T1)Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
- ICML (T1)SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training
- NeurIPS (T1)SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
- NeurIPS (T1)When to Trust a PFN: Detecting Harmful Shift in Tabular Foundation Models
202510 papers
- Findings of ACLAgentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
- ICML (T1)AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
- NeurIPS (T1)Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
- NeurIPS (T1)CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
- ICML (T1)Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
- CHILExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders
- ICCV (T1)Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models
- NeurIPS (T1)Reliably Detecting Model Failures in Deployment Without Labels
- ICML (T1)Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry
- ICLR (T1)Teaching LLMs How to Learn with Contextual Fine-Tuning
20246 papers
- ICML (T1)A Geometric Explanation of the Likelihood OOD Detection Paradox
- NeurIPS (T1)End-To-End Causal Effect Estimation from Unstructured Natural Language Data
- ICML (T1)InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
- MLHCLong-Term Allograft Survival in Liver Transplant Recipients
- MLHCNeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild
- Neural Information Processing Systems (NeurIPS)Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
20236 papers
- ICLR (T1)A Learning Based Hypothesis Test for Harmful Covariate Shift
- ICLR (T1)Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
- UAI (T1)Copula-Based Deep Survival Models for Dependent Censoring
- MLHCDuETT: Dual Event Time Transformer for Electronic Health Records
- Genes Cells and ChromosomesMachine learning in computational histopathology: Challenges and Opportunities
- NeurIPS (T1)Structured Neural Networks for Density Estimation and Causal Inference
Where alumni went30 listed, 5% to industry
Industry 1Academia 18
Most often to
- Cerebras Systems
Also to
- Carnegie Mellon University
- Princeton University
- University of Toronto
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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