Tom Hartvigsen

Thomas (Tom) Hartvigsen

Postdoctoral Associate


Pronouns: He/Him


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I am on the Faculty Market for CS and Data Science!


I am a Postdoctoral Associate at MIT in the Computer Science and Artificial Intelligence Laboratory (CSAIL) with Prof. Marzyeh Ghassemi.


I work on making data mining and machine learning algorithms safe, robust, and actionable enough for deployment in complex, time-varying environments, especially those found in healthcare. I focus on time series and NLP data. The projects that excite me the most: (1) robustly model complex time-varying systems through time series, (2) prevent perpetuating bias via machine learning and/or (3) have impact through real-world deployment.

Some recent highlights:

  • GRACE: Repairing pre-trained models thousands of times in a row during deployment (preprint)

  • ToxiGen: Constructing large, diverse hate speech datasets with large language models to train better hate speech classifiers (ACL'22 + dataset)

  • Timely and Actionable Time Series Models (see KDD'19; KDD'20; CIKM'22)

  • Robustness to uncertain/incomplete labels (see AAAI'23; AAAI'22; SDM'22; CIKM'22; AAAI'21)

  • Explainability for time series and NLP models ( see ACL'20; CIKM'21; ICDM'22)


  • Organizer:

    • NeurIPS 2022 Workshop on Learning from Time Series for Health

    • CHIL 2023

  • Led tutorial on Deep Learning with PyTorch for undergrads

  • Program Committee: AAAI, WSDM, CVPR, ICCV, ACL, EMNLP, NAACL, KDD, NeurIPS Datasets & Benchmarks Track

In the News


Before MIT, I received my PhD in Data Science from Worcester Polytechnic Institute in 2021, where I was a GAANN Fellow advised by Professors Elke Rundensteiner and Xiangnan Kong. I also interned at Microsoft and UMass Medical School. I received a B.A. in Applied Math from SUNY Geneseo in 2016.


Outside of research, I enjoy bouldering, biking, books (science fiction/science fact), birding, juggling, vegan cooking, and guitar. I also spent a summer living at BioSphere 2 in rural Arizona.