I will be at ACL 2022 in Dublin, ping me and let's chat!
I am a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT) in the Computer Science and Artificial Intelligence Laboratory (CSAIL) with Prof. Marzyeh Ghassemi. My research spans Time Series, Deep Learning, Data Mining, NLP, Fairness in AI Systems, and applications to understand and improve Health.
I aim to build trustworthy and deployable machine learning systems for health. The projects that excite me the most: (1) robustly model complex time-varying systems, (2) prevent perpetuating bias via machine learning, and/or (3) have impact through real-world deployment.
Some recent highlights:
ToxiGen: a dataset for detecting implicitly toxic language targeting disadvantaged identity groups. These data are hard to collect in the wild, so we control language models to generate it at scale automatically! (ACL'22)
Systems for learning to stop and classify ongoing time series early in time-sensitive domains (see KDD'19 and KDD'20 papers).
Explainability for time series and NLP models (see CIKM'21 and ACL'20 papers).
Methods for recovering models of annotators' sequential labeling behavior from machine learning datasets (see AAAI'22 and SDM'22 papers).
All papers are collaborations, please see my full list of publications.
AAAI, CVPR, ICCV, ACL, EMNLP, NAACL, KDD
Chaired Deep Learning with PyTorch for Undergrads workshop (2019)
WPI: data science council, graduate student senate, Arts & Sciences council, Organized deep learning reading group
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. Before graduate 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.