
Guang Lin CV
Director, Data Science Consulting Service Associate Professor Departments
of Mathematics，
Statistics & School of Mechanical Engineering 150
N. University Street, West
Lafayette, IN 479072067 Office:
Math
410 Office
phone: +1 765 4941965 
Professional activities 
Approximation
Theory and Machine Learning Conference, Purdue,
Sep. 2930, 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 highorder numerical algorithms to promote
innovation with significant potential impact and design highlyscalable
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
4.
Mathematical Biosciences Institute
Early Career Award, 2015
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.
Current Research Grants
2
IMA PI conference grant $5000 for the workshop on “Approximation Theory
and Machine Learning Conference”, Purdue University, Sep. 2930, 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.
4 DOE LLNL Subcontract B627599 $19,962.00,
2018.
5 Collaborative Research: Design and Analysis
of DataEnabled HighOrder Accurate Multiscale Schemes and Parallel Simulation
Toolkit for Studying Electromagnetohydrodynamic Flow, awarded from Division of
Mathematical Sciences, CDS&EMSS program, 20182019, $50,000 (DMS1821233), 2018.
6
Collaborative Research: AMPS: MultiFidelity Modeling via Machine
Learning for Realtime Prediction of Power System Behavior, awarded from NSF
Division of Mathematical Science, 20172020, $240,000. (DMS1736364), 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, 20162021, $400,759.91
(DMS1555072)
8
Startup Fund from Purdue University