معرفی
Qi Tang is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. He joined Georgia Tech in 2024 after serving as a Staff Scientist at Los Alamos National Laboratory (LANL) from 2018 to 2024. His research focuses on computational plasma physics, high-performance computing, and scientific machine learning, with applications in fusion energy, plasma simulations, and structure-preserving neural networks.
Education:
- Ph.D. in Applied Mathematics, Michigan State University, 2015
- B.S. in Mathematics & Applied Mathematics, Zhejiang University, 2010
Research Interests:
Qi’s work spans scalable numerical algorithms for exascale computing, fusion modeling, and scientific machine learning. Key areas include:
- High-order schemes, adaptive mesh refinement, and GPU acceleration for MHD and plasma simulations
- Structure-preserving neural networks for dynamical systems and multiscale physics
- Multi-physics modeling of tokamak disruptions and magnetic reconnection
Grants & Collaborations:
- Principal Investigator (PI) for multiple DOE grants, including ASCR MMICC Center (CHaRMNET)
- Led a multi-institutional ASCR SciML team with LANL, ANL, and universities
- Recipient of LANL LDRD and NSF grants for fusion and plasma research
Advising & Teaching:
- Advises Ph.D., master’s, and undergraduate students in CSE, physics, and engineering
- Teaches Parallel Computing Programming and Applications (CSE-6230)
- Co-advises students in DOE-funded programs and LANL collaborations
Labs & Teams: Qi is affiliated with the DOE ASCR MMICC Center (CHaRMNET) and LANL’s Applied Mathematics and Plasma Physics Group. His team develops open-source tools like MFEM-based MHD solvers and structure-preserving ML frameworks.
