★ News ★
[Mar'24] New preprint on time series foundation models
[Mar'24] Invited talk at UMass Amherst
[Feb'24] Paper accepted to Nature Medicine on bias in computational pathology
[Feb'24] Paper accepted to Knowledge and Information Systems on explaining multi-class time series classifiers
[Feb'24] New preprint on label noise in time series
[Feb'24] New preprint on black-box NLP robustness
[Feb'24] New preprint on generating math word problems
[Jan'24] Talk at Dartmouth CS
[Jan'24] Talk at UCSF/UC Berkeley
[Jan'24] Talk at the University of Alabama, Birmingham
MIT News covered our perspective drawing lessons from aviation safety for AI in health
Workshop accepted to ICLR'24 on Time Series for Health
Two NeurIPS'23 papers
PI on $200k grant to work on editing and debiasing LLMs (recruiting postdoc)
General Co-Chair of ML4H 2023
Paper accepted to npj Digital Medicine
[Nov '23] Invited talk at UVA Anesthesiology's Grand Rounds Seminar Series
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 hiring a postdoc to work on editing LLMs - email me if interested
Research
I'm broadly interested in machine learning and natural language processing. I work to enable responsible model deployment in ever-changing environments, especially for health.
These days, I mostly focus on:
Continually updating and adapting language models
Time series foundation models
Pre-training multi-modal models
Detecting and mitigating harmful social biases in 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: Using LLMs to detect and mitigate implicit social biases (ACL'22 + dataset)
Impact: ToxiGen has been used while training Llama2, Code Llama, phi-1.5, phi-2, and other LLMs, and to detect toxicity in Econ Forums and Laws.
Robustness to uncertain/incomplete data/labels (see preprint'24, 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)
Reinforcement Learning for early warning systems on time series (see CIKM'22; KDD'20; KDD'19)
In the News
Our work drawing lessons from aviation safety for health AI was covered by MIT News and Innovate Healthcare
GRACE was featured in the Microsoft Research blog
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.