People	Publication	Research	Teaching	Service	Awards

Guang Lin  CV Associate Dean for Research & Innovation, College of Science Director, Data Science Consulting Services Full Professor Departments of Mathematics， Statistics & School of Mechanical Engineering Purdue University 150 N. University Street, West Lafayette, IN 47907-2067 Offices: Math 950, DSAI 1025E Office phone: +1 765 49-41965 Email: guanglin at purdue dot edu

 

Biography

 

Prof. Guang Lin is the Associate Dean for Research and Innovation and the Director of Data Science Consulting Service that performs cutting-edge research on data science and provides hands-on consulting support for data analysis and business analytics. He was also the Chair of the Initiative for Data Science and Engineering Applications at the College of Engineering. Guang Lin is a Full Professor in the School of Mechanical Engineering and Department of Mathematics, Department of Statistics (courtesy), and Department of Earth, Atmospheric, and Planetary (courtesy) at Purdue University. 

 

Lin received his Ph.D. from Brown University in 2007 and worked as a Research Scientist at DOE Pacific Northwest National Laboratory before joining Purdue in 2014. Prof. Lin has received various awards, such as the NSF CAREER Award, Mid-Career Sigma Xi Award, University Faculty Scholar, College of Science Research Award, Mathematical Biosciences Institute Early Career Award, and Ronald L. Brodzinski Award for Early Career Exceptional Achievement.

 

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.     Reliable AI

2.    Interpretable, robust AI for discovery of physical laws.

3.    Interpretable, reliable AI for health.

4.    Physics-informed AI

5.    Neural Operator

6.    Fair AI.

7.    Big data analysis and statistical machine learning

8.    Predictive modeling and uncertainty quantification

9.    Scientific computing and computational fluid dynamics

10. Stochastic multiscale modeling

 

My research interests include diverse topics in artificial intelligence, machine learning, uncertainty quantification, big data analysis, computational and predictive science and statistical learning both on algorithms and applications. My work combines mathematical theory with advanced computational methods, making significant strides in Bayesian deep learning, data-driven modeling, and polynomial chaos-based uncertainty quantification. A main current thrust is artificial intelligence, machine learning, stochastic simulation (in the context of uncertainty quantification, probabilistic machine learning and beyond), and multiscale modeling of physical and biological systems (e.g., blood flow). My research goal is to develop scalable, interpretable, fair and trustworthy AI/ML algorithms and software for scientific discovery to promote innovation with significant potential impact, and design highly-scalable AI/ML algorithms and software on exascale supercomputers to investigate new knowledge discovery and predictive modeling for critical decision making in complex physical and biological complex systems.

 

Awards

·     2024 Seed for Success Acorn Award, Purdue University

 

·     2024 Faculty/Staff Recognition Award, Department of Mathematics

 

·     2022-23 Purdue University College of Science Research Award

 

·     2021 best paper award in Engineered Science Materials and Manufacturing Journal

 

·     Dean’s Fellow, College of Science, Purdue University, 2019.

 

·     Mid-Career Sigma Xi Award, Purdue University Chapter of Sigma Xi, 2019.

 

·     University Faculty Scholar, Purdue University, 2019.

 

·     2016 National Science Foundation (NSF) Faculty Early Career Development (CAREER) award from NSF Division of Mathematical Science, 2016.

 

·     2015 Mathematical Biosciences Institute Early Career Award, Fall 2015

 

·     2012 Ronald L. Brodzinski Award for Early Career Exception Achievement (two awards each year in the whole PNNL with 5000 researchers), April 2012.

 

·     2010 Department of Energy Advanced Scientific Computing Research Leadership Computing Challenge (ALCC) Award with 2010 Allocation Amount: 5,000,000 processor hours at OLCF, ORNL.

 

·     Brown University Ostrach Fellowship, Division of Applied Math, Brown University, Fall 2005.

 

·     Outstanding Performance Award, Pacific Northwest National Laboratory, 2010.

 

 

Representative publications: (* denotes corresponding author)

 

1. Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu, Bayesian Optimization with Gaussian-Process based Machine Learning for Improvement of Geometric Accuracy in Projection Multi-photon 3D Printing, Light: Science & Applications (Nature, Impact Factor 20.6), 14, 56, 2025.

https://doi.org/10.1038/s41377-024-01707-8

 

2.    Zhang, Y., Zhang, S., Wu, H., Wang, J., Lin, G.*, Zhang, A.P. Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array. Nat Commun 15, 3807 (2024).

https://www.nature.com/articles/s41467-024-47487-y

 

3.    Ziqi Guo, Zherui Han, Dudong Feng, Guang Lin*, Xiulin Ruan, Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties, Npj Computational Materials, 10, 31, 2024.

https://doi.org/10.1038/s41524-024-01215-8

 

4.    Ziqi Guo, Roy Chowdhury Prabudhya1, Zherui Han, Yixuan Sun, Dudong Feng, Guang Lin*, and Xiulin Ruan, Fast and Accurate Machine Learning of Phonon Scattering Rates and Lattice Thermal Conductivity, Nature npj Computational Material 9, 95, 2023.

https://doi.org/10.1038/s41524-023-01020-9

 

5.    Haoyang Zheng, Jeffrey Petrella, P. Murali Doraiswamy, Guang Lin*, Wenrui Hao, Data-driven causal model discovery and personalized prediction in Alzheimer’s disease, Nature NPJ Digital Medicine, 5, 137, 2022.

https://www.nature.com/articles/s41746-022-00632-7

 

6.    Yixuan Sun, Surya Mitra Ayalasomayajula, Abhas Deva, Guang Lin*, R. Edwin Garcia, Artificial Intelligence Inferred Microstructural Properties from Voltage-Capacity Curves, Scientific Reports, 12, 13421, 2022.

https://doi.org/10.1038/s41598-022-16942-5

 

7.    Ehsan Kharazmi, Min Cai, Xiaoning Zheng, Guang Lin, George Karniadakis, Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks, Nature Computational Science, 1-10, 2021.

https://doi.org/10.1038/s43588-021-00158-0

 

8.    Guang Lin, Chau-Hsing Su and George E. Karniadakis, The stochastic piston problem, Proceedings of the National Academy of Sciences of the United States of America, 101(45):15840-15845, 2004.

https://doi.org/10.1073/pnas.0405889101

 

9.    Guang Lin, Chau-Hsing Su and George E. Karniadakis, Random Roughness Enhances Lift in Supersonic Flow, Physical Review Letters, 99:104501, 2007.

https://doi.org/10.1103/PhysRevLett.99.104501

 

10. Sheng Zhang, Guang Lin*, Samy Tindel, Two-dimensional signature of images and texture classification, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A.478:20220346, 2022.

https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2022.0346

 

11. Sheng Zhang, Guang Lin*, Robust data-driven discovery of governing physical laws with error bars, Proceedings of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A 474: 20180305, 2018.  https://doi.org/10.1098/rspa.2018.0305

 

12. Yifan Du, Guang Lin*，Turbulence Generation from a stochastic wavelet model, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, 474(2217):20180093, 2018.

https://doi.org/10.1098/rspa.2018.0093

 

13. Bledar A. Konomi, Georgios Karagiannis, Kevin Lai, Guang Lin*, Bayesian treed Calibration: an application to Carbon capture with AX sorbent, Journal of American Statistical Association, 112(517): 37-53, 2017.

https://doi.org/10.1080/01621459.2016.1190279

 

14. F. Liang, Y. Cheng, and G Lin*, Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule, Journal of the American Statistical Association, 109(506): 847-863, 2014.

https://www.tandfonline.com/doi/abs/10.1080/01621459.2013.872993

 

15. Wei Deng, Xiao Zhang, Faming Liang, Guang Lin*, An adaptive empirical Bayesian method for sparse deep learning, 2019 Conference on Neural Information Processing Systems (NeurIPS), accepted, Dec. 8 – Dec. 14, 2019, Vancouver, Canada. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC7687285/

 

16. Wei Deng, Faming Liang, Guang Lin*, A contour stochastic gradient Langevin dynamics algorithm for simulations of multi-modal distributions, 2020 Conference on Neural Information Processing Systems (NeurIPS), Dec. 5 – Dec. 12, 2020, virtual meeting. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC8457681/

 

17. Wei Deng, Qi Feng, Liaoyao Gao, F. Liang, G. Lin*, Non-convex learning via replica exchange stochastic gradient MCMC, 2020 International Conference on Machine Learning (ICML), accepted, Jul 12 - 18, 2020, Virtual Meeting. (Tier 1 AI conference)

https://proceedings.mlr.press/v119/deng20b.html

 

18. Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin*, Faming Liang, Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction, The Ninth International Conference on Learning Representations (ICLR), May 4^th-7^th, 2021, accepted (virtual meeting). (Tier 1 AI conference)

https://openreview.net/forum?id=iOnhIy-a-0n

 

19. Wei Deng, Siqi Liang, Botao Hao, Guang Lin*, Faming Liang, Interacting Contour Stochastic Gradient Langevin Dynamics, The Tenth International Conference on Learning Representations (ICLR) 2022, Virtual Meeting. (Tier 1 AI conference), Apr 25^th – 29^th, accepted.

https://openreview.net/forum?id=IK9ap6nxXr2

 

20. Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin*, Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning, Thirty-seventh AAAI Conference on Artificial Intelligence, accepted, 2023. (Oral accepted talk)

https://ojs.aaai.org/index.php/AAAI/article/view/25893

 

21. Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin*, Accelerating approximate Thompson sampling with underdamped Langevin Monte Carlo, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:2611-2619, 2024.

https://proceedings.mlr.press/v238/zheng24b/zheng24b.pdf

 

22. Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin*, Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics, accepted, ICML 2024.

https://openreview.net/forum?id=fwyuupgAQ5

 

23. Jinwon Sohn, Guang Lin*, Qifan Song, Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:1594-1602, 2024.

https://proceedings.mlr.press/v238/sohn24a/sohn24a.pdf

 

24. Wei Deng, Yian Ma, Zhao Song, Qian Zhang, Guang Lin*, On Convergence of Federated Averaging Langevin Dynamics, 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Oral presentation, 2024.

https://openreview.net/forum?id=EmQGdBsOPx

 

Current Research Grants

 

1)    CDS&E: Integrated Molecular Dynamics and Machine Learning Investigation of Gas-Liquid Interface in Transcritical Reacting Flows, NSF, $404,204, Co-PI, 2024-2027.

2)    Machine learning of the additive manufacturing process for optimized fatigue performance, 09/2024-08/2026, $329,983, MRL Materials Resources LLC, Co-PI, 2024-2027.

3)    AI-empowered heterogeneously nanomanufactured imperceptible human-integrated wearables for personalized digital twins with multimodal fusion (AI-HEPHAESTUS), Purdue IPAI Postdoctoral Research Program, $280,000, Co-PI, 2024-2026.

4)    Remote monitoring of eating safety and efficiency: an AI integrated validation approach to improve Veterans’ wellbeing, Purdue University Health of Forces, $10,000, Co-PI, 2024-2025.

5)    Accelerating discovery and diagnostics of plasma-wall interactions using machine learning, Department of Energy, $2,469,160, Co-PI, 2023-2026.

6)    Modeling Mortality in Duchenne Muscular Dystrophy Cardiomyopathy: Identification of Surrogate Outcome Measures for DMD Drug Trials, National Institutes of Health, $3,600,000, Purdue PI, 2023-2028.

7)    (MOLUcQ) Uncertainty Quantification for Multifidelity Operator Learning, Department of Energy, $2,000,000, Co-PI, 2023-2027.

8)    Elements: FourPhonon: A Computational Tool for High-Order Phonon Anharmonicity and Thermal Properties, National Science Foundation, $600,000, Co-PI, 2023-2026.

9)    ASCENT: From sensors to multiscale digital twin to autonomous operation of resilient electric power grids, NSF, $1,000,000, Co-PI, 2023-2026.

10)              Hierarchical Machine Learning-based Optimal Parameterization Scheme for WECC Composite Load Model under All Disturbances, Brookhaven National Laboratory, $216,537, PI, 2023-2025.

11)              Trustworthy Heterogeneous Data-Aware Bayesian Federated Learning, Department of Energy, EXPRESS: 2022 Exploratory Research for Extreme Scale Science, $400,000, PI, 2022-2025.

12)              BRITE PIVOT: Machine Learning Enabled Rapid and Robust 3D Nanomanufacturing, NSF, $419,695, Co-PI, 2022-2025.

13)              Collaborative Research: Robust Deep Learning in Real Physical Space: Generalization, Credibility, and Scalability, NSF DMS, $1,000,000, PI, 2021-2025.

14)              Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications, NSF CDS&E program, $120,000, Purdue PI, 2021-2025.

 

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.