MAY 4: Dr. Huimin Cheng, Boston University School of Public Health

“Introduction to Artificial Intelligence in Health”

Monday, May 4, 2026
Hybrid Event
In-person: Crosstown Center (801 Mass Ave), 2nd floor, Room 2128
12:00-1:00pm Seminar
1:00-2:00pm Luncheon

Abstract: Artificial intelligence (AI) is rapidly transforming how we understand, deliver, and improve health. From clinical care and biomedical research to public health and health system operations, AI-driven approaches are reshaping how data are used to inform decisions and generate new insights. Yet for many health professionals, the landscape remains complex, evolving, and at times difficult to navigate. This seminar provides a high-level, accessible introduction to AI in health for a broad audience across clinical, research, and public health domains. It will outline the key concepts underlying modern AI, including machine learning, deep learning, and generative AI. Using concrete, real-world examples, the session will illustrate how AI is being applied to challenges such as disease prediction, clinical decision-making, operational efficiency, and population health. This seminar will also address critical considerations for responsible use, including data quality, bias, generalizability, and ethical and regulatory frameworks. Participants will leave with a foundational understanding of how AI works, where it can add value, and what is needed to use these tools thoughtfully and effectively in health contexts. No prior experience with AI is required.

Bio: Dr. Cheng’s research is highly interdisciplinary. Her methodological research focuses on LLM, machine learning, statistical network analysis, deep learning, and causal inference. She modeled the generating process of a network from both non-parametric (e.g., graphon model) and parametric (e.g., SBM) perspectives. Dr. Cheng has developed various methods, including network cross-validation, network sampling, network ANOVA, and graphon convolutional network.
Dr. Cheng works closely with biophysicists, engineers, computer scientists, political scientists, public health scientists, and sociologists to solve scientific problems arising from various disciplines.