Prof. Tommi S Jaakkola

Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 32-G470


Teresa Coates Cataldo

Areas of Interest and Expertise

Computational and Representational Issues in Machine Learning and its Applications
Statistical Inference
Applications to Molecular Biology (Protein Structure, Gene Identitication and Regulation)
Image Processing
Functional Genomics
Applications to Computational Biology and Information Retrieval
Artificial Intelligence
Computational Biology
Statistical Inference
Big Data

Research Summary

Research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. Our research in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. We design new methods, theory and algorithms so as to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. We apply and develop our algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (e.g., biomedical), design and optimize molecules or reactions for the purpose of drug design, and to model strategic, game theoretic interactions.

Recent Work