Weichung Wang


Weichung Wang, Ph.D. 

Current Position

Research Interest

Awards and Honors

Educational Background

Short Biography

Dr. Weichung Wang is a professor at the Institute of Applied Mathematical Sciences, Department of Mathematics and Data Science Degree Program of National Taiwan University. 

Professor Weichung Wang has extensive research experience and has made significant contributions in the fields of artificial intelligence for medical image analysis and applications, high-performance scientific computing, matrix computing, and numerical optimization. He has published over 100 papers in top-tier international journals and conferences, including Nature Medicine, The Lancet Digital Health, Radiology, SIAM Journal on Matrix Analysis and Applications, and Journal of Computational Physics. He has served as an editorial board member for renowned international journals such as SIAM Journal on Mathematics of Data Science (2019-2020), Annals of Mathematical Sciences and Applications, and has also been a member of the organizing committee for major international conferences such as SIAM Supercomputing, HPC Asia, VECPAR, and others.

Professor Wang founded MeDA Lab (Medical Data Analytics Laboratory, http://meda.ai) at National Taiwan University  and  a startup company (http://pancad.ai) to develop AI solutions for healthcare and medicine through interdisciplinary collaborations. The lab focuses on AI medical image and data analysis, emphasizing innovation and evidence-based research, resulting in publications in leading journals and practical AI-based applications. In addition, MeDA Lab has collaborated with domestic and international partners to develop life-saving algorithms and integrate them into daily clinical workflows, furthering their goal of using AI to improve patient outcomes.

Professor Wang has been the recipient of numerous awards, including the National Taiwan University's Outstanding Teacher Award, the Nien Tzu Award, the Y. Z. Hsu Science Paper Award in Artificial Intelligence, the Ministry of Science and Technology's Outstanding Young Scholar Award, the Ministry of Science and Technology's Future Science and Technology Award, the National Innovation Award, the National Center for Theoretical Science and Research Honorary Fellowship and Center Scientist, among others. He has also held various leadership positions, including serving as Convener of National Science and Technology Council (Mathematics Discipline, Department of Natural Sciences and Sustainable Development), President of the Taiwan Society for Industrial and Applied Mathematics, Secretary General of the Mathematical Society of the ROC, and Secretary of the East Asia Society for Industrial and Applied Mathematics.

Professor Wang received his Ph.D. in Applied Mathematics from the University of Maryland, College Park, USA in 1996, and a B.S. in Applied Mathematics from National Chiao-Tung University in Taiwan in 1989.

Research Summary

I am interested in developing numerical algorithms and software for medical AI, data sciences and scientific computing. My researches usually involve artificial intelligence, numerical linear algebra, computational optimization, parallel computing, and their applications.

Medical AI

Professor Weichung Wang founded MeDA Lab (Medical Data Analytics Laboratory, http://meda.ai) to develop medical AI Engines and medical AI Workflows. The former extracts hidden information from high-dimensional medical image and numerical datasets. The latter turns the information into clinical intelligence. MeDA Lab works with physicians and industry partners worldwide to boost intelligent and precision medical software solutions.

Matrix Computations in Computational and Data Sciences

We consider large-scale linear systems, nonlinear eigenvalue problems, and matrix factorizations arising in numerical simulations and data analytics. Applications include 3D photonic devices, big data analysis, and healthcare. The main focuses include Krylov type algorithms, randomized methods, and accelerations on parallel computers with GPU.

Data-driven Modeling and Statistical Computing

Many computer experiments conduct performance analysis and optimization while only objective function values are available. The main research focuses are the design of computer experiments and surrogate models-assisted techniques with an emphasis on software auto-tuning. We also develop efficient methods for statistical computing and optimal experiment designs.

GPU and High-performance Computing

We study how GPU, CPU, and heterogeneous CPU-GPU clusters can be used to accelerate scientific computations. The focuses include CPU-GPU accelerated solvers for linear systems and eigenvalue problems, fast medical image reconstructions on computed tomography, and particle swarm optimization with applications in medical and statistical sciences.

Numerical Methods

Computer Aided Learning