Pengtao Xie

Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine.

Education:

• PhD in Machine Learning, School of Computer Science, Carnegie Mellon University

Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery.

Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation.

Scientific Awards:

• NIH MIRA Award (2025)
• NSF CAREER Award (2024)
• Best Graduate Teacher Award – UCSD ECE (2023)
• ICLR Notable-Top-5% Paper (2023)
• Global Top-100 Chinese Young Scholars in AI (2022)
• UCSD Faculty Career Development Award (2022)
• Tencent Faculty Award (2021)
• Outstanding Reviewer – ICLR (2021)
• AMIA Doctoral Dissertation Award Finalist (2020)
• Amazon AWS Research Award (2020)
• Tencent AI-Lab Faculty Award (2020)
• Innovator Award – Pittsburgh Business Times (2018)
• Siebel Scholarship (2014)

Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students.

Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models. His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.