I am a Ph.D. student at the University of Pennsylvania, advised by Prof. Mayur Naik. I am interested in studying how to make foundation models more accessible and trustworthy, especially in the real world. To this end, my research primarily focuses on the intersection of Efficient Deep Learning and Neurosymbolic AI.
Before my Ph.D., I obtained a B.S. in Electrical Engineering and Computer Science from UC Berkeley. I also interned at Tortuga AgTech, where I taught robots how to pick fruit.
Recent News
- I have been awarded the AWS Fellowship for Trustworthy AI.
- Our paper DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation was awarded a spotlight at ICML 2024.
Publications
Neurosymbolic AI
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Neuro-Symbolic Programming in the Age of Foundation Models: Pitfalls and Opportunities Pre-Print
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Data-Efficient Learning with Neural Programs NeurIPS 2024
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Relational Programming with Foundation Models AAAI 2024
Health and Bioinformatics:
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Crowd-sourced machine learning prediction of Long COVID using data from the National COVID Cohort Collaborative eBioMedicine 2024🏆 NIH L3C Honorable Mention Award Paper
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DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation ICML 2024
Efficient Deep Learning
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CLAM: Unifying Finetuning, Quantization, and Pruning by Chaining LLM Adapter Modules ES-FoMo II Workshop @ ICML 2024
Awards and Fellowships
- AWS Fellowship for Trustworthy AI - 01/2025
Teaching and Mentoring
Teaching:
- Teaching Assistant, CIS 7000, Large Language Models - UPenn, Fall 2024
- Lab Assistant, CS61B, Data Structures - UC Berkeley, Summer 2020
Past Mentees:
Work
- Robotics and Machine Learning Intern - Tortuga AgTech, 05/2021 - 08/2021
- Software Engineering Intern - Lockheed Martin, 06/2018 - 08/2018