David Sontag

About

David Sontag is an Associate Professor at MIT in the EECS department, specializing in Artificial Intelligence and Decision-making. His research areas include AI for Healthcare and Life Sciences, Natural Language and Speech Processing, and developing techniques for systems that interact with the external world through perception, communication, and action. His work combines intellectual traditions from computer science and electrical engineering to analyze and synthesize systems that learn, make decisions, and adapt to changing environments. As a faculty member, he is involved in advancing the understanding and application of AI technologies, contributing to the department's focus on innovative research in these fields.

Research topics

• Artificial Intelligence
• Data Mining
• Computer Science
• Machine Learning
• Biology
• Bioinformatics

Selected publications

• Large language models are few-shot clinical information extractors

  2022-01-01 · 292 citations

  articleOpen accessSenior author

  A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classifi…

  PublisherOA PDFDOI

• A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection

  Science Translational Medicine · 2020-11-04 · 92 citations

  articleOpen accessSenior authorCorresponding

  Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using e…

  PublisherOA PDFDOI

• Predicting human health from biofluid-based metabolomics using machine learning

  Scientific Reports · 2020 · 38 citations

  Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, w…

  PublisherOA PDFDOI

• Deeper evaluation of a single-cell foundation model

  Nature Machine Intelligence · 2024-12-12 · 18 citations

  articleSenior authorCorresponding
  PublisherDOI

• Need Help? Designing Proactive AI Assistants for Programming

  2025-04-24 · 14 citations

  articleOpen access
  PublisherDOI

Recent grants

• CAREER: Exact Algorithms for Learning Latent Structure

  NSF · $500k · 2014–2017

• Collaborative Research: SCH: Machine Learning Driven User Interfaces for Information Gathering and Synthesis from Medical Records

  NSF · $600k · 2022–2026

• CAREER: Exact Algorithms for Learning Latent Structure

  NSF · $351k · 2017–2020

Frequent coauthors

• Fredrik Johansson

  30 shared

• Michael Oberst

  28 shared

• Monica Agrawal

  Massachusetts Institute of Technology

  26 shared

• Steven Horng

  Beth Israel Deaconess Medical Center

  25 shared

• Yoni Halpern

  Google (United States)

  22 shared

• Hunter Lang

  21 shared

• Larry Nathanson

  Beth Israel Deaconess Medical Center

  19 shared

• Irene Y. Chen

  University of Rochester Medical Center

  18 shared

Labs

• MIT EECS - David Sontag LabPI

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Daniela Rush-129