John V. Guttag
Also known as John Guttag
John Guttag is an MIT computer scientist whose research applies machine learning to health care. He studied English and applied mathematics at Brown, earned a computer-science doctorate at Toronto in 1975, joined MIT as an associate professor in 1979, and became a full professor in 1988. From 1999 to 2004, he headed MIT’s EECS department, and he became an ACM Fellow in 2006. In 2015, he co-founded Health at Scale, and in 2016, he and Jen Gong built a machine-learning system for screening children’s speech and language disorders. A 2025 paper he coauthored examines LLM explanation faithfulness; as of October 2026, he is an MIT professor and leads its Clinical and Applied Machine Learning Group.
- Works
- 246
- Citations
- 18,283
- h-index
- 57
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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1971–1975
Degrees at Brown and Toronto
He earns a bachelor’s degree in English from Brown in 1971, a master’s degree in applied mathematics there in 1972, and a computer-science doctorate from Toronto in 1975.
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1975–1979
From USC to MIT
At the University of Southern California, he serves on the faculty from 1975 to 1978; he joins MIT as an associate professor in 1979.
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1979–2004
Professor and department head
He gains tenure in 1983 and becomes a full professor in 1988. From 1993 to 1998, he serves as computer science associate department head; from 1999 to 2004, he heads EECS.
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2006
An ACM Fellowship
In 2006, he becomes an ACM Fellow for contributions to algebraic specifications and abstract data types.
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2015–2016
Machine learning for clinical care
In 2015, he co-founds Health at Scale with Zeeshan Syed, Mohammed Saeed, and David Guttag to apply machine learning to personalized care. In 2016, he and Jen Gong use machine learning to build an automated screening system for children’s speech and language disorders.
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2021
A third-edition Python book
In 2021, he publishes the third edition of Introduction to Computation and Programming Using Python, a text on computational problem solving and data science.
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2025
Measuring LLM explanations
With Katie Matton, Robert Osazuwa Ness, and Emre Kiciman, he coauthors Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations in 2025. The paper tests whether an LLM’s stated reasons match concepts that affect its answer.
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As of October 2026
Leading clinical machine learning
As of October 2026, he is MIT’s Dugald C. Jackson Professor of Computer Science and Electrical Engineering and leads the Clinical and Applied Machine Learning Group, where researchers apply machine learning to clinical problems.
Sources and credits
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- John Guttag – MIT Jameel Clinic
- EECS changes leadership; Guttag appointed head of department | MIT News | Massachusetts Institute of Technology
- ACM names three from MIT for computer science | MIT News | Massachusetts Institute of Technology
- John Guttag
- Making health care more personal | MIT News | Massachusetts Institute of Technology
- Automated screening for childhood communication disorders | MIT CSAIL
- Introduction to Computation and Programming Using Python, third edition by John V. Guttag | Penguin Random House
- When AIs explain themselves, how can we tell if they’re lying? | MIT CSAIL
- Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
- Hi, we're CAML.
- John Guttag | MIT CSAIL