Xiuquan Wang

I am Xiuquan Wang, Ph.D., an Associate Professor of Mathematics, the H. M. Thompson Endowed Chair in Mathematics, and Chair of the Department of Mathematics & Computer Science at Tougaloo College.

My research lies at the intersection of applied mathematics, computational biology, machine learning, and biomedical data science. I develop mathematical and computational methods for analyzing high-dimensional and large-scale biomedical data, with particular interests in biological state transitions, interpretable machine learning, graph-based analysis, long-read sequencing, and physics-informed modeling. A central theme of my work is to develop methods that are not only computationally effective, but also biologically interpretable and grounded in meaningful scientific structure.

Current Research

• Macrophage State Transitions and Transcript Isoform Dynamics in Lung Cancer: Developing an integrated computational framework that combines RNA velocity, single-cell trajectory analysis, and long-read RNA sequencing to investigate macrophage plasticity in non-small cell lung cancer. By connecting inferred cellular state transitions with transcript isoform variation, this project seeks to identify molecular programs associated with tumor-related macrophage states and prioritize candidate isoforms for future mechanistic investigation. Preliminary analyses of 34,420 monocyte/macrophage-lineage cells reveal substantial cellular heterogeneity and tumor-associated differences in macrophage composition.

• Physics-Informed Machine Learning for Biomedical Imaging: Developing physics-informed neural-network methods that integrate mathematical models with medical imaging data. Current work investigates identifiable tumor-growth parameters from single-snapshot brain MRI and explores PDE-constrained inference for mechanism-aware tumor characterization and robust image classification.

• BioFilter-EVTS: Biology-Guided Evaluation of RNA Velocity: Developing quantitative methods to evaluate and refine RNA velocity visualizations by distinguishing transition consistency from biological plausibility. BioFilter-EVTS integrates transition probabilities with pseudotime, lineage structure, and marker-gene progression to identify locally ambiguous velocity directions and improve the interpretation of cellular state transitions.

• Nanopore Long-Read Sequencing and Machine Learning: Developing mathematical, statistical, and machine-learning approaches to extract biological information from Nanopore long-read sequencing data. Current interests include long-read RNA velocity, transcript and isoform analysis, and transformer-based methods for detecting DNA modifications directly from Nanopore signal data.

• Large-Scale Biological Network Analysis: Applying scalable graph and network methods to investigate cellular relationships, biological heterogeneity, and developmental organization in large single-cell datasets. Current work includes analysis of a cell-cell similarity network containing more than one million embryonic mouse brain cells to identify developmental modules, hub cells, and candidate transition-associated bridge cells.

• Topology- and Graph-Guided AI for Precision Medicine: Integrating topological data analysis, patient similarity networks, graph neural networks, and explainable machine learning to discover cardiometabolic phenotypes and predict longitudinal disease risk using large-scale biomedical data. This work extends my earlier research in topological data analysis toward modern precision-medicine applications.

I lead and collaborate on externally funded research supported by programs including the NSF HBCU-UP Research Initiation Award, NSF HBCU-UP Implementation Project, and MS-INBRE Project Development Grant. My research combines methodological development with applications in biomedical data science and creates meaningful research opportunities for undergraduate students.

I am strongly committed to research-integrated teaching and student mentoring. I involve undergraduate students in computational biology, data science, machine learning, and mathematical modeling projects, with an emphasis on developing both technical skills and scientific independence. My students have received awards at national and regional research conferences and have pursued advanced study in data science, biostatistics, and related STEM fields.

In addition to my faculty appointment, I serve as a Visiting Data Scientist at the Children’s Hospital of Philadelphia Research Institute.

My long-term research goal is to develop robust, interpretable, and biologically grounded mathematical and computational methods for understanding complex biological systems, while building an interdisciplinary research environment that prepares students for graduate education, scientific research, and data-intensive careers.