Minghan Chen

Minghan Chen is an Associate Professor in the Computer Science Department at Wake Forest University and a Z. Smith Reynolds Foundation Faculty Fellow. Her research bridges computational methods with biological applications, focusing on developing innovative algorithms for understanding complex biological systems and diseases.

Education:

• PhD from Virginia Tech (2019)

Dr. Chen's research interests center on multiscale modeling, knowledge-guided machine learning, and parameter optimization algorithms specifically designed for bio-related applications. Her work particularly focuses on Alzheimer's disease progression, where she develops computational frameworks to model the spatiotemporal dynamics of neuropathological events. She integrates techniques from computational biology, bioinformatics, and machine learning to create models that can simulate and predict disease progression at multiple scales.

Her recent publications demonstrate a strong trend toward increasingly sophisticated computational approaches to Alzheimer's disease research, with particular emphasis on brain network analysis, single-cell genomics, and multimodal data integration. Her work combines graph theory, deep learning, and multiscale modeling to create comprehensive frameworks that capture the complexity of neurodegenerative processes.

Awards and Recognition:

• Z. Smith Reynolds Foundation Faculty Fellow

Dr. Chen actively mentors students in her research group, currently recruiting both graduate and undergraduate students interested in computational biology and machine learning. She has successfully guided students to recognition, including Jingwen who received the best poster award at ACM-BCB in 2022. Her research is supported by stipend funding for students engaged in rigorous, publication-quality research. Dr. Chen has organized significant academic events including the Biological Modeling and Mining workshop (BMM, 2021) and the Virtual Deep Learning Bootcamp (May 2022), demonstrating her commitment to advancing computational methods in biological sciences.