Guang Lin CV
Director, Data Science Consulting Service
150 N. University Street,
West Lafayette, IN 47907-2067
, Purdue, Sep. 29-30, 2018
Workshop on the Current Trends and Challenges in Data Science and Uncertainty Quantification, Purdue, Mar. 31, 2018
Ph.D, 2007, Applied Mathematics, Brown University
M.S., 2004, Applied Mathematics, Brown University
M.S., 2000, Mechanics and Engineering Science, Peking University, P.R. China
B.S., 1997, Mechanics, Zhejiang University, P.R. China
1. Big data analysis and statistical machine learning
2. Predictive modeling and uncertainty quantification
3. Scientific computing and computational fluid dynamics
4. Stochastic multiscale modeling
My research interests include diverse topics in computational and predictive science and statistical learning both on algorithms and applications. A main current thrust is stochastic simulation (in the context of uncertainty quantification, statistical learning and beyond), and multiscale modeling of physical and biological systems (e.g., blood flow). My research goal is to develop high-order numerical algorithms to promote innovation with significant potential impact and design highly-scalable numerical solvers on petascale supercomputers to investigate new knowledge discovery and predictive modeling for critical decision making in complex physical and biological complex systems.
1. University Faculty Scholar, Purdue University, 2019
2. NSF CAREER Award, 2016
3. Mentor for Purdue undergraduate team, awarded the Prize of Finalist in the MCM math modeling contest，2016
5. Ronald L. Brodzinski Award for Early Career Exception Achievement, Department of Energy Pacific Northwest National Laboratory, 2012.
6. Early Career Award, Department of Energy Pacific Northwest National Laboratory，2012.
7. Advanced Scientific Computing Research Leadership Computing Challenge (ALCC) award, Department of Energy， 2010.
8. Outstanding Performance Award, Department of Energy Pacific Northwest National Laboratory, 2010.
9. Ostrach Fellowship, Brown University, 2005.
2 IMA PI conference grant $5000 for the workshop on “Approximation Theory and Machine Learning Conference”, Purdue University, Sep. 29-30, 2018.
3 Purdue Mathematics Department CCAM grant $6000 for the workshop on “Current Trends and Challenges in Data Science and Uncertainty Quantification”, Purdue University, Mar 31, 2018.
5 Collaborative Research: Design and Analysis of Data-Enabled High-Order Accurate Multiscale Schemes and Parallel Simulation Toolkit for Studying Electromagnetohydrodynamic Flow, awarded from Division of Mathematical Sciences, CDS&E-MSS program, 2018-2019, $50,000 (DMS-1821233), 2018.
6 Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior, awarded from NSF Division of Mathematical Science, 2017-2020, $240,000. (DMS-1736364), 2017.
7 Career: Uncertainty Quantification and Big Data Analysis in Interconnected Systems: Algorithms, Computations, and Applications, 2016 National Science Foundation (NSF) Faculty Early Career Development (CAREER) award from NSF Division of Mathematical Science, 2016-2021, $400,759.91 (DMS-1555072)
8 Startup Fund from Purdue University