Regina Barzilay
School of Engineering Distinguished Professor for AI and Health
Develops machine learning for medicine, such as AI models that predict breast cancer risk from mammograms, along with natural language processing.
Regina Barzilay's lab at MIT works on medical imaging & healthcare AI. 37 papers since 2023, 13 at top venues, mostly TMLR and ICLR. 22 PhD students and 2 master's students. 12 alumni, most often to Amazon and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 35%
- PhD students
- 22
- MS students
- 2
- alumni listed
- 12
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 8 papers2025 · T1 5
- 9 papers2024 · T1 3
- 20 papers2023 · T1 5
T1 venuesOtherVenues: TMLR, ICLR, ICML, Science, Journal of Chemical Information and Modeling
Research focuspaper titles that use each topic's words
- cancer5 papers
- risk4 papers
- predict1 paper
- Medical imaging & healthcare AI0 papers
- medicine0 papers
Paper list
From the lab homepage.
20258 papers
- Nature Communications (T1)AI-driven discovery of synergistic drug combinations against pancreatic cancer (opens in a new tab)
- Proceedings of the Thirteenth International Conference on Learning Representations (ICLRComposing Unbalanced Flows for Flexible Docking and Relaxation (opens in a new tab)
- Nature Machine Intelligence (T1)Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens (opens in a new tab)
- Nature MedicineInfluenza vaccine strain selection with an AI-based evolutionary and antigenicity model (opens in a new tab)
- TMLR (T1)MiniFold: Simple, Fast, and Accurate Protein Structure Prediction (opens in a new tab)
- TMLR (T1)Predicting sub-population specific viral evolution (opens in a new tab)
- Science (T1)Protein codes promote selective subcellular compartmentalization (opens in a new tab)
- Transactions on Machine Learning Research (TMLR) Sample, estimate, aggregate: A recipe for causal discovery foundation models (opens in a new tab)
20249 papers
- ICML (T1)CLIPZyme: reaction-conditioned virtual screening of enzymes (opens in a new tab)
- An MIT Exploration of Generative AI MIT.Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards (opens in a new tab)
- Proceedings of the Twelfth International Conference on Learning Representations (ICLRDeep Confident Steps to New Pockets: Strategies for Docking Generalization (opens in a new tab)
- 14(2):e1711Diffusion models in protein structure and docking (opens in a new tab)
- Nature Reviews Methods Primers 4Graph neural networks (opens in a new tab)
- ICLR (T1)Graph-Based Forward Synthesis Prediction of Biocatalyzed Reactions (opens in a new tab)
- TMLR (T1)Improved motif-scaffolding with SE(3) flow matching (opens in a new tab)
- Journal of Chemical Information and Modeling 64 (14), 5521-5534OpenChemIE: An information extraction toolkit for chemistry literature (opens in a new tab)
- Transactions on Machine Learning Research (TMLR) Risk-Controlling Model Selection via Guided Bayesian Optimization (opens in a new tab)
202320 papers
- The Clinics: Internal Medicine, 43, 3, 405-417Artificial intelligence and machine learning in lung cancer screening (opens in a new tab)
- Nat Chem Biol 19, 1561Author Correction: Distinct chemical environments in biomolecular condensate (opens in a new tab)
- Science (T1)Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back (opens in a new tab)
- Biophysical Journal, 121, 10, - Blind protein–ligand docking with diffusion-based deep generative models (opens in a new tab)
- Nature (T1)De novo design of protein structure and function with RFdiffusion (opens in a new tab)
- —Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii (opens in a new tab)
- ICLR (T1)DiffDock-Pocket: Diffusion for pocket-level docking with sidechain flexibility (opens in a new tab)
- —Diffdock: Diffusion steps, twists, and turns for molecular docking (opens in a new tab)
- Proceedings of the Eleventh International Conference on Learning Representations (ICLRDiffusion Probabilistic Modeling of Protein Backbones in 3D for the Motif-Scaffolding Problem (opens in a new tab)
- Systematic Reviews, 12Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews (opens in a new tab)
- NeurIPS (T1)Harmonic Prior Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design (opens in a new tab)
- 1-5, doi: 10.1109/ICASSP49357. .10095632InfoShape: Task-Based Neural Data Shaping via Mutual Information (opens in a new tab)
Where alumni went12 listed, 50% to industry
Industry 4Academia 4
Most often to
- Amazon
- Google DeepMind
- Meta
Also to
- Postdoctoral Associate at Broad Institute
- Tel Aviv University
- UC Berkeley
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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