UNCW Interdisciplinary Research Seminar Series 2025-2026: Healthcare in the Era of Machine Learning
For the past two decades, public and private organizations have heavily invested in machine learning due to increased availability of large datasets, fueling research and discovery across disciplines, especially healthcare. This trend has been amplified with the public popularity of large language models (LLM) and other generative artificial intelligence (AI) applications. In healthcare, machine learning has the potential to enhance predictive healthcare models, improve preventative care with real time monitoring, transform patient outcomes, facilitate drug discovery, and make healthcare more accessible and cost-effective.
This seminar series will bring a diverse range of scholars and experts, from nationally renowned doctors to applied mathematicians, from policy influencers to business founders, to embark on an enriched research journey with UNCW’s faculty and students.
Organizers
Xuemei Chen (Mathematics and Statistics), Jeeyae Choi (School of Nursing), Ahmed ElSaid (Computer Science), Karl Ricanek (Computer Science), Yang Song (Computer Science), Yishi Wang (Mathematics and Statistics)
Schedules:
- September 12th, 2025 Friday 11:30 - 12:30, @Congdon 1008 (Auditorium)
(Lunch starts at 11:00)
Title: Uncertainty Quantification in Neural Networks with Applications to MRI Processing
- October 3rd, 2025 Friday 12:00 - 1:00, @Congdon 1008 (Auditorium)
(Lunch starts at 11:30)
Title: Math at the Heart of Tomorrow's Medical AI
- October 31st, 2025 Friday 12:00 - 1:00, @Congdon 1008 (Auditorium)
Title: TBD
Sep 12th, Balan Bio
Dr. Balan is a Professor of Applied Mathematics at the University of Maryland. He is currently the program director of Applied Mathematics, Statistics and Scientific Computing program. Dr. Balan has extensive research in the theory of application of neural network and deep learning. He is Co-editor in chief of Applied and Computational Harmonic Analysis, a top journal in applied mathematics. Before joining Maryland, Dr. Balan was a senior research scientist at Siemens. He obtained a PhD in Computational and Applied Mathematics at Princeton University in 1998.
Abstract
In this talk we study Lipschitz properties of neural networks. In practical numerical examples (such as Alex Net, and scattering networks), estimations of local Lipschitz bounds are compared to these theoretical bounds. Based on the Lipschitz bounds, we next establish concentration inequalities for the output distribution with respect to a stationary random input signal. Such a Lipschitz analysis is next applied to medical image processing. Image reconstructions involving neural networks (NNs) are generally non-iterative and computationally efficient. However, without analytical expression describing the reconstruction process, the computation of noise propagation becomes difficult. Automated differentiation allows rapid computation of derivatives without an analytical expression. In this talk, the feasibility of computing noise propagation with automated differentiation was investigated. The noise propagation of image reconstruction by End-to-end variational-neural-network was estimated using automated differentiation and compared with Monte-Carlo simulation. The root-mean-square error (RMSE) map showed great agreement between automated differentiation and Monte-Carlo simulation over a wide range of SNRs.
October 3rd, Wu Bio
Dr. Wu received his PhD in Mathematics from Princeton University in 2011, under the supervision of Professor Ingrid Daubechies (and unofficially Professor Amit Singer). Following his postdoctoral research position from 2011 to 2014, Dr. Wu joined the University of Toronto's Department of Mathematics as a tenure-track Assistant Professor until June 2017. From there, he transitioned to Duke University, initially as a tenured Associate Professor in the Department of Mathematics and Department of Statistical Science (from June 2017 to July 2022) and later as a tenured Professor (from August 2022 to August 2023). In August 2023, he joined New York University as a tenured Professor at the Courant Institute of Mathematical Sciences. Before coming to mathematics, Dr. Wu received his medical degree from National Yang Ming University (now National Yang Ming Chiao Tung University), Taiwan, in 2003. Following his medical studies, he practiced as a resident doctor at Taipei Veterans General Hospital, Taiwan. In 2015, Dr. Wu was awarded the Sloan Research Fellowship. In 2017, he was awarded the PIMS Early Career Award by the Canadian Applied and Industrial Society (CAIMS). His research interests encompass a broad spectrum, spanning mathematics, statistics, biomedical engineering, and medicine, with a particular emphasis on biomedical signal processing, theoretical advancements, and their clinical applications and unmet requirements. Dr. Wu has coauthored over 100 journal publications and 10 conference proceedings. Furthermore, he serves as an associate editor for journals including Applied and Computational Harmonic Analysis, Information and Inference: A Journal of the IMA, and Frontiers in Applied Mathematics and Statistics.
Abstract
The rise of wearable sensors and bedside devices has transformed health monitoring from isolated snapshots into continuous streams of rich, multimodal physiological data. Yet these signals are highly nonstationary, making it difficult to extract reliable, clinically useful information. Current deep learning-based AI systems often fall short: they can be powerful but remain opaque, fragile, and disconnected from science/physiology. Mathematics offers a path forward. By grounding AI in mathematical principles, we can rigorously handle dynamic data, ensure interpretability, and uncover physiological meaning. In this talk, I will show how mathematics, which IMHO is at the heart of tomorrow's medical AI, can transform raw biomedical signals into trustworthy insights, paving the way toward reliable, science-based care.
