Resume
Academic Achievements
- - Selected Publications:
- - Interpretable Image Classification with Adaptive Prototype-based Vision Transformers, NeurIPS 2024
- - This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations, NeurIPS 2023
- - Achieving Domain-Independent Certified Robustness via Knowledge Continuity, NeurIPS 2024
- - Reviewer Services: AAAI-AISI track 2023, ICML 2024, NeurIPS 2024, ICLR 2025, ICML 2025, NeurIPS IAI workshop 2024, ICLR LLM Reasoning and Plan Workshop 2025, TMLR 2024
Research Experience
- - Ph.D. student in Computer Science at Dartmouth College, working on interpretable and reliable machine learning methods
- - Master's student in Statistical Science at Duke University, member of the Interpretable Machine Learning Lab
- - Collaborated with Prof. Chaofan Chen from UMaine
Education
- - Ph.D. in Computer Science, Dartmouth College, 2028 (expected), Advisor: Prof. Soroush Vosoughi
- - M.S. in Statistical Science, Duke University, 2023, Advisor: Prof. Cynthia Rudin
- - B.S. in Statistics (with Honors), Carnegie Mellon University, 2021, Advisor: Prof. Zach Branson
Background
- - Research Interests: Developing interpretable and reliable machine learning methods that promote transparency, fairness, and usability
- - Professional Field: Computer Science
- - Brief Introduction: A second-year Ph.D. student in Computer Science at Dartmouth College, focusing on prototype-based vision transformer models, statistical modeling, and visualization techniques.
Miscellany
- - Teaching Experiences:
- - Duke Decision 618/521: Decision Analytics and Modeling TA: Fall 2021
- - Duke CS 617: Introduction to Machine Learning TA: Fall 2022
- - Dartmouth COSC 070: Foundations of Applied Computer Science TA: Fall 2023