Tom Hartvigsen
Assistant Professor
Data Science
University of Virginia
Visiting Assistant Professor
CSAIL
MIT
★ News ★
Two papers accepted to NeurIPS'23
General Co-Chair of ML4H 2023
Paper accepted to npj Digital Medicine
[Nov '23] Invited talk at UVA Anesthesiology's Grand Rounds Seminar
Two papers accepted to IEEE BigData'23
[Oct '23] Invited talk at Cornell
EAMMO'23 paper on Drawing lessons from Aviation Safety for Health AI
[Oct' 23] Awarded MSFT Foundation Model grant
[Oct '23] Talk at Oxford
Best Paper at IMLH Workshop @ ICML 2023
Preprint on unified ethical in-context learning
Chairing ICML 2023 Workshop
SERC paper on algorithmic fairness in chest x-ray diagnosis
Preprint on classifying irregularly-sampled time series
AAAI'23 paper on multi-label knowledge amalgamation
Invited talk at AJCAI'22 Workshop on Toxic Language Detection
Co-chairing NeurIPS '22 workshop
Invited talks at WPI and Northeastern
Four workshop papers accepted at NeurIPS'22
Hi! I'm an Assistant Professor of Data Science at the University of Virginia. I am spending the 2023-2024 academic year in Cambridge, MA where I am a Visiting Assistant Professor at MIT. Previously, I was a postdoc at MIT working with Marzyeh Ghassemi. Before that, I did my PhD in Data Science at WPI where I was advised by Elke Rundensteiner and Xiangnan Kong.
I am recruiting highly-motivated students and postdocs to start Fall 2024
Research
I'm broadly interested in machine learning and natural language processing, especially to enable responsible deployment in ever-changing environments.
I'm mainly focusing on:
Test-time interventions for big, pre-trained models
Pre-training for time series, language, and multi-modal models
Detecting and mitigating harmful social biases in LLMs and natural language
Healthcare applications: NLP for scientific medical literature, learning from ICU time series, fair computational pathology, understanding mental health via wearable devices
Recent highlights
GRACE: Continually editing the behavior of large language models during deployment (NeurIPS'23 + code + blog post)
ToxiGen: Constructing large, diverse hate speech datasets with large language models to train better hate speech classifiers (ACL'22 + dataset)
Impact: ToxiGen has been used while training Llama2, Code Llama, phi-1.5, and more, and to detect toxicity in Econ Forums and Laws.
Reinforcement Learning for early warning systems on time series (see KDD'19; KDD'20; CIKM'22)
Robustness to uncertain/incomplete data/labels (see preprint'23, AAAI'23; AAAI'22; SDM'22; CIKM'22; AAAI'21)
Explainability for time series and NLP models (see NeurIPS'23; FAccT'22; ICDM'22; ACL'20; CIKM'21)
In the News
Our work on GRACE was featured by Microsoft Research
Our work on ToxiGen was covered by TechCrunch and Microsoft Research
Our work on Fair Explainability was covered by MIT News
Misc
Outside of research, I enjoy bouldering, biking, books (science fiction/science fact), birding, juggling, vegan cooking, and playing guitar. I also spent a summer living at BioSphere 2 in Arizona.