I am a PhD student at the University of Pennsylvania (2023–) advised by Jianbo Shi, and a visiting student at Berkeley AI Research (Jan 2026–). My research is supported by the NDSEG Fellowship. Previously, I graduated from MIT with bachelor's and master's degrees in computer science.
I work on creative and controllable generation for AI models. My research interests include:
- How data shapes visual representation learning and model behavior.
- How to identify data gaps and guide data curation to improve model capabilities.
- How humans develop intuition, and likewise improving AI agents on dynamic reasoning.
- How to make AI models efficient, such as for videos and limited data domains.
I have interned at Meta Superintelligence Labs (2025), Adobe Research (2024), and Meta FAIR (2022). During my undergrad, I conducted research at MIT CSAIL and the MIT Quest for Intelligence.
News
- Feb 2026: Vibe Spaces accepted to CVPR 2026. See you in Denver!
- Jan 2026: SAM 3: Segment Anything with Concepts accepted to ICLR 2026.
- Jun 2025: Started research scientist internship at Meta Superintelligence Labs.
- Oct 2024: Secret Seeds and Origin Attribution accepted to WACV 2025. See you in Tucson!
- Apr 2024: Awarded the NDSEG Fellowship (4.9% acceptance rate).
- Feb 2024: Amodal Completion selected as a CVPR 2024 Highlight. See you in Seattle!
- Jan 2023: Started my PhD at the University of Pennsylvania. See you in Philly!
Selected Publications
Awards
2024
NDSEG Fellowship (4.9% acceptance rate)
2021
Tau Beta Pi Engineering Honor Society
2020
IEEE Eta Kappa Nu EECS Honor Society
2020
MIT Undergraduate Research and Innovation Scholar
Invited Talks
Mar 2026
NSF ARNI Language and Vision Working Group
Feb 2024
NYC Computer Vision Day
Service
Organizer
- Founder, NSF ARNI AI Institute Emerging Researchers Group (2024–2026)
- Penn Vision and Learning Reading Group (2023–2025)
- GRASP Student, Faculty, and Industry (SFI) Seminar Committee (2024–2025)
- GRASP Lab Social Committee (2023–2024)
Teaching Assistant
- Penn CIS 6800 Advanced Topics in Machine Perception (Fall 2023, Fall 2024)