Tommi Jaakkola

About

Tommi Jaakkola is the Thomas M. Siebel Distinguished Professor at MIT, specializing in Artificial Intelligence and Decision-making within the Department of Electrical Engineering and Computer Science. His research areas include AI for Healthcare and Life Sciences, Artificial Intelligence and Machine Learning, and Natural Language and Speech Processing. He focuses on developing techniques for the analysis and synthesis of systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. His work involves leveraging computational, theoretical, and experimental tools to advance groundbreaking sensors, energy transducers, physical substrates for computation, and systems addressing shared human challenges.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computational biology
• Biology
• Computer Security
• Genetics
• Data Mining
• Chemistry
• Data science

Selected publications

• Expanded encyclopaedias of DNA elements in the human and mouse genomes

  Nature · 2020 · 2538 citations

  data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the…

  DOI

• A Deep Learning Approach to Antibiotic Discovery

  Cell · 2020 · 2127 citations

  DOI

• De novo design of protein structure and function with RFdiffusion

  Nature · 2023 · 1810 citations

  have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, prot…

  DOI

• Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

  Journal of Medicinal Chemistry · 2020 · 241 citations

  synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS…

  DOI

• Perspectives on ENCODE

  Nature · 2020 · 222 citations

  . The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.

  DOI

Frequent coauthors

• Regina Barzilay

  174 shared

• Wengong Jin

  64 shared

• David K. Gifford

  Massachusetts Institute of Technology

  52 shared

• Amir Globerson

  Tel Aviv University

  34 shared

• David Alvarez-Melis

  30 shared

• Klavs F. Jensen

  Massachusetts Institute of Technology

  25 shared

• Karthik Narasimhan

  24 shared

• Connor W. Coley

  Massachusetts Institute of Technology

  24 shared

Labs

• MIT EECS Artificial Intelligence + Decision-making LabPI

Similar researchers at Massachusetts Institute of Technology

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• Antonio Torralbah-133
• Daniela Rush-129