Developing numerical algorithms and software for
applications arising in computational and data sciences.

Research Summary

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

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.

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.

Data-Driven Modeling and Statistical Computing

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.


Here are some exciting results taken from researches and teaching.