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SCI Seminar: Principled Learning for Medical AI: Structure, Reliability, and Interpretability
Xiaoling Hu
Principled Learning for Medical AI: Structure, Reliability, and Interpretability, Abstract: The widespread deployment of AI in medicine demands not only predictive accuracy but also structural awareness, reliability under uncertainty, and interpretability for clinical trust. In this talk, I will present a unified research agenda toward principled learning for medical AI, grounded in these core pillars.
First, I will discuss how incorporating explicit structure, such as topology and spatial priors, into neural networks enhances the model's ability to reason about fine-grained anatomical and pathological features, which are critical for tasks like brain and tumor segmentation. Second, I will focus on reliability, exploring how we can quantify and mitigate uncertainty arising from imperfect labels, limited data, and domain shifts, using methods such as distributional modeling, hyperparameter learning, and probabilistic inference. Third, I will show how these approaches naturally support…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Monday, March 30, 2026, 10:30 AM – 11:30 AM.
SCI Seminar: Efficient and Reliable AI for Real-World Healthcare Deployment
Md Mostafijur Rahman
Efficient and Reliable AI for Real-World Healthcare Deployment, Abstract: Healthcare is one of the highest-impact domains for AI, yet reliable deployment at scale remains difficult. To truly improve patient care and clinical workflows, AI must operate under real clinical constraints, not just in ideal lab settings. In practice, deployment is limited by high compute and memory costs, scarce labeled data, and distribution shifts across sites and time. Many clinically important findings are also rare and long-tailed, which makes generalization especially challenging. My research makes deployability a design objective by developing methods that stay accurate under strict resource and data constraints. In this talk, I will first discuss high-performance lightweight deep learning architectures built by redesigning core building blocks. I will then present training-time generative supervision strategies that improve data efficiency and generalization to rare and long-tailed cases with no…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Wednesday, April 1, 2026, 10:30 AM – 11:30 AM.
Memorial Day
University Observed Holidays
Campus Wide Event: Yes.
Monday, May 25, 2026.