Tom Hartvigsen
☆ News ☆
AAAI'23 paper on multi-label knowledge amalgamation
Invited talk at WPI CS Colloquium
Invited talk at Northeastern
Four workshop papers accepted at NeurIPS'22
ML4H'22 paper on detecting stress from wearable devices
ICDM'22 paper on explainability for deep time series classifiers
CIKM'22 paper on early classification of irregular time series
CIKM'22 paper on recurrent classifier chains with missing labels
Chairing NeurIPS workshop
Invited talks at Google Research, Tufts and the University of Rochester
ACL'22 paper on constructing datasets with language models.
Invited talk at MIT Horizons
AAAI'22 paper on positive unlabeled learning.
SDM'22 paper on positive unlabeled learning.
I am on the Faculty Market for CS and Data Science!
About
I am a Postdoctoral Associate at MIT in the Computer Science and Artificial Intelligence Laboratory (CSAIL) with Prof. Marzyeh Ghassemi.
Research
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)
Service
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
Our work on TOXIGEN was covered by TechCrunch and Microsoft Research
Our work on Fair Explainability was covered by MIT News
History
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.
Misc
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.