Hello! I'm a postdoc in the MIT Computational Biology Lab (PI: Prof. Manolis Kellis) at MIT CSAIL and the Broad Institute. My research is currently supported by the Alana Down Syndrome Center at MIT, supporting our aims of using contextualized machine learning to elucidate the biologic bases and therapeutic options for complex diseases. I completed my PhD at the CS Department at Carnegie Mellon University, advised by Prof. Eric Xing. I've also been very blessed to spend time working with Rich Caruana at Microsoft Research (2019, 2020) and Chris Potts at Roam Analytics (2017). My work has previously been supported by the CMLH Fellowship.
New preprint: Our study of biases and implications in EHR datasets ("Death by Round Numbers") is now available on Medrxiv!
New publication: Our study of Personalized Treatment Effects in Covid-19 is now available in JBI!
Code available: The alpha version of Contextualized is now available.
Code available: Contextualized GAMs have been open-sourced.
New publication: Our paper on the Interaction Effect perspective of Dropout has been accepted to AISTATS 2022.
New publication: Our paper on Neural Additive Models has been accepted for an oral presentation at NeurIPS 2021.
New publication: Our paper on counter-intuitive untrustworthiness of GAMs has been accepted to KDD 2021.
Research Interests and Selected Publications
I research machine learning methods and build artificial intelligence systems to automate the process of scientific discovery. I am particularly interested in questions of automatically identifying and adapting to changing contexts. This research focus requires advances in interpretable machine learning, multi-task learning, and task representation learning, and finds natural applications in precision medicine and computational genomics of complex diseases such as Alzheimer’s Disease, Down Syndrome, and cancer.
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and ParametersArxiv 2021
Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep LearningMedrxiv 2020
- Code: Contextualized.ML Python package.
- Code: Interpret.ML Python package.
A more complete list of my publications can be found here.