Date/Time
Date(s) - 01/17/2025
9:00 am - 9:50 am
Category(ies)
Ziyi Huang, PhD
Machine Learning Researcher, Bell Labs
Date: Friday, January 17, 2025
Time: 9:00 – 9:50 a.m., SCOB 228
Faculty Host: Sarah Stabenfeldt
Abstract:
Artificial Intelligence (AI) has demonstrated immense potential to revolutionize healthcare, promising enhanced diagnostics, personalized treatments, and improved patient outcomes. However, the widespread adoption of AI in real-world clinical settings has been hindered, primarily due to concerns surrounding trustworthiness. In this talk, I will explore the concept of trustworthy AI in healthcare, examining its critical components and showcasing our research on enhancing AI trustworthiness for healthcare applications. I will introduce three specific areas of our work: robust tissue analysis, adaptive precision medicine, and machine learning safety analysis. My research on weakly supervised learning enables robust tissue analysis from imperfectly annotated datasets, substantially reducing the workload associated with data collection and annotation. Leveraging reinforcement learning techniques, I have developed models that can potentially provide tailored decision making, enabling adaptive, optimal, and personalized treatment strategies with statistical guarantees. Furthermore, my work on safety analysis centers on quantifying model uncertainty, providing practical guidance to enhance the reliability of deep neural networks. Finally, I will share my broader research vision on the trustworthy smart health system and its potential applications across diverse healthcare domains.
Biosketch:
Dr. Ziyi Huang is currently a machine learning researcher at Bell Labs and an adjunct faculty at Stevens Institute of Technology. Prior to these roles, she got her Ph.D. degree in Electrical Engineering from Columbia University, her master’s degree from the University of Michigan, and her bachelor’s degree from the University of Science and Technology of China (USTC). Her research primarily focuses on trustworthy AI for health, computational biology, and image-guided therapy. Her vision is to empower machines to deliver trustworthy disease analysis and reliable treatment strategies across populations. Dr. Huang’s work has been accepted at top venues, including IEEE-JBHI (IF 7.7), NeurIPS, AISTATS, and MICCAI. Her contribution has been recognized by selective fellowships/grants with over $283K awarded, and she was selected as Machine Learning and Systems Rising Star in 2024.