2026 Tommi S. Jaakkola: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	100	363	351	198	190	326	51306

Research.com Recognitions

2017 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the fields of machine learning, computational biology and natural language processing.
2002 - Fellow of Alfred P. Sloan Foundation

Overview

Tommi S. Jaakkola is affiliated with MIT in the United States and has contributed extensively to research at the intersection of computer science and molecular biology. Their work spans multiple domains, with a significant number of publications and collaborations across these disciplines.

The primary fields of study for Tommi S. Jaakkola include:

Computer Science
Biochemistry, Genetics and Molecular Biology

Within these broader fields, their subfields of study are:

Artificial Intelligence
Molecular Biology
Materials Chemistry
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

The main research topics addressed by Tommi S. Jaakkola focus on:

Machine Learning in Materials Science
Computational Drug Discovery Methods
Protein Structure and Dynamics
Topic Modeling
Generative Adversarial Networks and Image Synthesis
Adversarial Robustness in Machine Learning
RNA and protein synthesis mechanisms

They have published numerous papers in prominent venues. Notable recent papers include:

A Deep Learning Approach to Antibiotic Discovery, 2020, Cell
De novo design of protein structure and function with RFdiffusion, 2023, Nature
A Deep Learning Approach to Antibiotic Discovery, 2020, Cell
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking, 2022, arXiv (Cornell University)
Generative models for molecular discovery: Recent advances and challenges, 2022, Wiley Interdisciplinary Reviews Computational Molecular Science

The frequent publication venues where Tommi S. Jaakkola has contributed include:

arXiv (Cornell University)
Zenodo (CERN European Organization for Nuclear Research)
PubMed
bioRxiv (Cold Spring Harbor Laboratory)
Cell

Collaboration is a significant aspect of their research. Frequent co-authors include:

Regina Barzilay
Wengong Jin
H. Stärk
Gabriele Corso
Bowen Jing

Tommi S. Jaakkola has been recognized for their contributions by receiving several awards, including:

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2017, for significant contributions to machine learning, computational biology, and natural language processing
Fellow of Alfred P. Sloan Foundation in 2002

Best Publications

An introduction to variational methods for graphical models

Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul

3761
Citations
A Deep Learning Approach to Antibiotic Discovery

Jonathan M. Stokes;Kevin Yang;Kyle Swanson;Wengong Jin

2079
Citations
Exploiting Generative Models in Discriminative Classifiers

Tommi Jaakkola;David Haussler

2072
Citations
Analyzing Learned Molecular Representations for Property Prediction.

Kevin Yang;Kyle Swanson;Wengong Jin;Connor W. Coley

1737
Citations
Maximum-Margin Matrix Factorization

Nathan Srebro;Jason Rennie;Tommi S. Jaakkola

1338
Citations
Convergence of Stochastic Iterative Dynamic Programming Algorithms

Tommi Jaakkola;Michael I. Jordan;Satinder P. Singh

1195
Citations
Weighted low-rank approximations

Nathan Srebro;Tommi Jaakkola

977
Citations
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári

947
Citations
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation

J. Zico Kolter;Tommi S. Jaakkola

916
Citations
Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin;Regina Barzilay;Tommi S. Jaakkola

849
Citations
Computational discovery of gene modules and regulatory networks.

Ziv Bar-Joseph;Georg K Gerber;Tong Ihn Lee;Nicola J Rinaldi

826
Citations
MAP estimation via agreement on trees: message-passing and linear programming

M.J. Wainwright;T.S. Jaakkola;A.S. Willsky

822
Citations
Prediction of Organic Reaction Outcomes Using Machine Learning

Connor W. Coley;Regina Barzilay;Tommi S. Jaakkola;William H. Green

766
Citations
Bayesian parameter estimation via variational methods

Tommi S. Jaakkola;Michael I. Jordan

766
Citations
Towards robust interpretability with self-explaining neural networks

David Alvarez-Melis;Tommi S. Jaakkola

727
Citations
Rationalizing Neural Predictions

Tao Lei;Regina Barzilay;Tommi S. Jaakkola

716
Citations
A discriminative framework for detecting remote protein homologies.

Tommi S. Jaakkola;Mark Diekhans;David Haussler

713
Citations
Partially labeled classification with Markov random walks

Martin Szummer;Tommi Jaakkola

713
Citations
A graph-convolutional neural network model for the prediction of chemical reactivity

Connor W. Coley;Wengong Jin;Luke Rogers;Timothy F. Jamison

696
Citations
Style Transfer from Non-Parallel Text by Cross-Alignment

Tianxiao Shen;Tao Lei;Regina Barzilay;Tommi S. Jaakkola

669
Citations
Using the Fisher Kernel Method to Detect Remote Protein Homologies

Tommi Jaakkola;Mark Diekhans;David Haussler

621
Citations

Frequent Co-Authors

Regina Barzilay MIT

David K. Gifford MIT

Michael I. Jordan University of California, Berkeley

Amir Globerson Tel Aviv University

Alan S. Willsky MIT

Connor W. Coley MIT

Martin J. Wainwright MIT

David Sontag MIT

Klavs F. Jensen MIT

Shiyu Chang University of California, Santa Barbara

External Links

Personal website of Tommi S. Jaakkola Google Scholar page

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