People	Publication	Research	Teaching	Service	Award

Guang Lin  CV Director, Data Science Consulting Service Professor Departments of Mathematics， Statistics & School of Mechanical Engineering Purdue University 150 N. University Street, West Lafayette, IN 47907-2067 Office: Math 410 Office phone: +1 765 49-41965 Email: guanglin at purdue dot edu

Professional activities

Approximation Theory and Machine Learning Conference, Purdue, Sep. 29-30, 2018    Workshop on the Current Trends and Challenges in Data Science and Uncertainty Quantification, Purdue, Mar. 31, 2018

Current Teaching:

• Spring 2019: MATH 266 Ordinary Differential Equations (Section 113)

Past Teaching:

• Fall 2018: Math 303 Differential Equations and Partial Differential Equations for Engineering and the Sciences (Section 074)
• Spring 2018: ME 309 Fluid Mechanics
• Fall 2017: Math 303 Differential Equations and Partial Differential Equations for Engineering and the Sciences (Sections 072, 078)
• Spring 2017: Math 265 Linear Algebra (Section 185)
• Fall 2016: Math 598 Machine Learning and Uncertainty Quantification in Data Science
• Fall 2016: Math 265 Linear Algebra (Section 041)

 Degrees

 

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

 

Research Interest

 

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.

 

Awards

 

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

 

1   2018 NVIDIA GPU Grant.

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.

 

4   DOE LLNL Subcontract B627599 $19,962.00, 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

 

To Applicants

Graduate students and postdoc positions are available in my group. If you are interested in machine learning, big data analysis, uncertainty quantification & predictive modeling, welcome to contact me via email.