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Bio
I am an Assistant Professor in the School of Data Science and Society (SDSS) with a secondary appointment in the Department of Mathematics at the University of North Carolina at Chapel Hill. I am a core faculty member of the Carolina Health Informatics Program (CHIP), a member of the Computational Medicine Program, and an affiliate member of the Carolina Center for Interdisciplinary Applied Mathematics (CCIAM).
My research interests span a diverse range of fields, including control theory, network science, tensor algebra, numerical analysis, data science, machine learning, deep learning, hypergraph learning, data analysis, and computational biology.
I received my B.S. degree in Mathematics (with minor in Statistics) from the University of California, Irvine in 2016, and my M.S. degree in Electrical & Computer Engineering and my Ph.D. degree in Applied & Interdisciplinary Mathematics (supervised by Dr. Anthony M. Bloch & Dr. Indika Rajapakse) from the University of Michigan in 2020 and 2021, respectively. I was a Postdoctoral Research Fellow in the Channing Division of Network Medicine (CDNM) at Brigham and Women’s Hospital and Harvard Medical School from 2021 to 2023.
Here is my Curriculum Vitae.
Features
Our article “Stability of Ecological Systems: A Theoretical Review” has been published in Physics Reports. This article provides a systematic and comprehensive review on the theoretical frameworks employed to assess various stability types in ecological systems, including linear stability, sign stability, diagonal stability, D-stability, total stability, sector stability, and structural stability. It examines necessary or sufficient conditions for achieving such stability and demonstrates the interplay of these conditions on the network structures of ecological systems. The article can be downloaded at here.
My book “Tensor-based Dynamical Systems: Theory and Applications” has been published in the Synthesis Lectures on Mathematics & Statistics series by Springer Nature. This book offers a comprehensive review of tensor algebra, covering topics such as tensor products, tensor unfolding, tensor eigenvalues, and tensor decompositions. It also delves into the role of tensors and tensor algebra in tensor-based dynamical systems, where system evolutions are modeled using various tensor products. The book can be downloaded at here.
Our article “Teasing out Missing Reactions in Genome-scale Metabolic Networks through Hypergraph Learning” has been published in Nature Communications. This article presents a deep learning-based method — CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) — to predict missing reactions in genome-scale metabolic models (GEMs) purely from metabolic network topology. CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes. The article can be downloaded at here.
Openings
I am currently seeking highly motivated individuals for Ph.D. and Master’s students who have a background or interest in control theory, machine learning, network science, and computational biology. If you are interested, please reach out to me and provide your CV for consideration.