About Me

I am a first-year Ph.D. student in Chemical and Biological Sciences at Scripps Research.

Previously, I received my Master’s in Computer Graphics and Game Technology from the University of Pennsylvania, where I was supervised by Professor Lingjie Liu. Before that, I received Bachelor of Science in Computer Science (with Honors) and Mathematics from the University of Wisconsin-Madison, where I worked in Machine Learning and Optimization Theory (MLOPT) Research Group and advised by Professor Ramya Korlakai Vinayak, Professor Matthew L. Malloy, and Professor Steven J. Schrodi.

Research interests

AI for Life Science: I am passionate about using AI to address life science challenges. My most recent work involves developing a high-throughput ML system for malaria detection [medRxiv’25] during my internship at Cephla.

Safe and Robust ML: Safety and robustness refers to the model’s resilience against adversarial and unanticipated scenarios. My research endeavors include methods such as uncertainty estimation and out-of-distribution detection to enhance the robustness of ML systems. Relevant publications are listed below:

• Threshold-Based Auto-Labeling: [NeurIPS’23]
• Human-in-the-Loop Out-of-Distribution Detection: [ICML’22], [AISTATS’24]
• Confidence Regions for Multinomial Parameters: [ISIT’22]

Publications

Taming False Positives in Out-of-Distribution Detection with Human Feedback
Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
Artificial Intelligence and Statistics (AISTATS), 2024
[arXiv]

Promises and Pitfalls of Threshold-based Auto-labeling
Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
Neural Information Processing Systems (NeurIPS), 2023 (Spotlight)
[Project Page] [arXiv]

Geometry of the Minimum Volume Confidence Sets
Heguang Lin, Mengze Li, Daniel Pimentel-Alarcón, Matthew L. Malloy
IEEE International Symposium on Information Theory (ISIT), 2022.
[arXiv] [Video]

Adaptive Out-of-Distribution Detection with Human-in-the-Loop
Heguang Lin*, Harit Vishwakarma*, Ramya Korlakai Vinayak
International Conference on Machine Learning (ICML), Workshop on Human-Machine Collaboration and Teaming 2022.
[PDF] [Video]

Preprints

Octopi 2.0: Open and Scalable Microscopy Platform for AI-powered Diagnostic Applications
Hongquan Li, Heguang Lin, Pranav Shrestha, Rinni Bhansali, You Yan, Jaspreet Pannu, Kevin Marx, Wei Ouyang, Lucas Fuentes Valenzuela, Ethan Li, Anesta Kothari, Jerome Nowak, Hazel Soto-Montoya, Adil Jussupov, Maxime Voisin, Kajal Maran, Oswald Byaruhanga, Joaniter Nankabirwa, Byran Greenhouse, Prasanna Jagannathan, Manu Prakash
In submission, 2025.
[medRxiv]

HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering
Yunzhou Song*, Heguang Lin*, Jiahui Lei, Lingjie Liu, Kostas Daniilidis
In submission, 2024.
[Project Page] [arXiv]

Machine Learning for Glucose Prediction to Identify Diabetes-related Metabolic Pathways
Gautam Agarwal*, Collin Frink*, Brain Hu*, Ziling Hu*, Eliot Kim*,Heguang Lin*
ProjectX Undergraduate Machine Learning Research Competition, 2021.
[OpenReview]

* Denotes equal contribution.

Working experience

Cephla

• Machine Learning Research Intern
• May 2024 - August 2024

Genentech

• AI Intern
• June 2020 - August 2020

For fun

3D Modeling: Just started my journey into modeling and Maya. Check out some of my works HERE!

Awards

The David Dewitt Undergraduate Scholarship, Computer Science Department, UW–Madison, 2022
Undergraduate Scholarship for Summer Study, UW–Madison, 2020