Incoming Assistant Professor
University of Virginia
School of Data Science
Postdoc at MIT CSAIL
★ News ★
General Chair of ML4H 2023
Invited talks at Stanford MedAI, IIT Delhi, and Harvard Medical School
Preprint on unified ethical in-context learning
Co-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 CS Colloquium and Northeastern
Four workshop papers accepted at NeurIPS'22
ML4H'22 paper on detecting stress from wearable devices
ICDM'22 paper explainability for deep time series classifiers
Hi! Starting Fall 2023, I'll be an Assistant Professor at the University of Virginia's School of Data Science. I'm currently a postdoc at MIT CSAIL working with Marzyeh Ghassemi. I study machine learning, data mining, and applications to improve healthcare. Previously, I completed my PhD at Worcester Polytechnic Institute, where I was advised by Elke Rundensteiner and Xiangnan Kong.
I will be recruiting highly-motivated students to join my group at the University of Virginia, please reach out if you feel you're a good fit!
I study core challenges in making machine learning responsibly deployable for healthcare. I focus on time series and NLP where we need:
Robust methods for learning from data and labels that are are biased, missing, or noisy
Models that generalize and adapt to ever-shifting distributions and requirements
Tools to make models meet strong human requirements like early warnings, explainability, updatability, and safety/fairness.
GRACE: Continually editing 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)
Reinforcement Learning for early warning systems on time series (see KDD'19; KDD'20; CIKM'22)
Robustness to uncertain/incomplete labels (see AAAI'23; AAAI'22; SDM'22; CIKM'22; AAAI'21; preprint)
Explainability for time series and NLP models (see FAccT'22; ICDM'22; ACL'20; CIKM'21)
The projects that excite me the most: (1) robustly model dynamic environments through time series, (2) prevent perpetuating bias via machine learning and/or (3) have impact through real-world deployment.
General Chair, ML4H 2023
Co-Chair, ICML 2023 Workshop on Challenges in Deploying Generative AI
Organizer, CHIL 2023
Co-Chair, NeurIPS 2022 Workshop on Learning from Time Series for Health
Program Committee: AAAI, NeurIPS, WSDM, CVPR, ICCV, ACL, EMNLP, NAACL, KDD, CHIL, 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
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 Arizona.