Matrix Computations, GPU and High-Performance Computing, Data-Driven Modeling, Computational optimization, Statistical computing. Publication list.
Randomized matrix computing, Simulations of photonic device and organic polymer , Deep learning for medical images, Healthcare applications.
Introduction to computational mathematics, Computational methods and tools for data science（計算數學導論，資料科學之計算方法與工具)
Professor affiliated with Institute of Applied Mathematical Sciences, Department of Mathematics, and Data Science Degree Program at NTU. CV.
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.
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.
Many computer experiments require performance analysis and optimization in which only objective function values are available. Our main focuses are design of experiments and surrogates models assisted techniques with emphasis in software auto-tuning. We also develop efficient methods for statistical computing and optimal experiment designs.