Giri Krishnan

giri@gatech.edu

Dr Krishnan is research professor in the Georgia Tech’s Interdisciplinary Research Institute, Institute for Data Engineering and Science, School of Computational Science and Engineering, College of Computing. He is an associate director of the Center for AI in Science and Engineering. His current interest is in developing AI methods for computational science problems across many domains. He is a computational neuroscientist by training, with past work spanning across a wide range of computational modeling and AI methods. His group's current focus is on generative methods for computational workflow, neural approaches for accelerating compute intensive problems and applying interpretable methods to scientific AI for advancing scientific understanding.

Prior to joining Georgia Tech, he was research scientist at UC San Diego and his research involved developing large-scale modeling of the brain to study sleep, memory and learning. In addition, he has contributed towards neuro-inspired AI and neuro-symbolic approaches. He is broadly interested in the emergence of intelligent behavior from neural computations in the brain and AI systems. 

Dr Krishnan has more than 50 publications and his research has been supported by multiple grants from NIH and NSF. He is passionate about open-science and reproducible science and strongly believes that progress in science requires reproducibility.

Associate Director, Center for Artificial Intelligence in Science and Engineering (ARTISAN)
Principal Research Scientist
Phone
404.894.2132
Office
CODA Building
Additional Research
AI : Deep learning, Neuro-symbolic ApproachesGeosciences.Molecular DynamicsNeuroscience : Theoretical and computational modeling
Research Focus Areas
AI
Geosystems
Neuroscience
IRI And Role
Data Engineering and Science > Faculty
Data Engineering and Science > Leadership
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computational Science and Engineering
Google Scholar
https://scholar.google.com/citations?hl=en&user=IGsdszkAAAAJ&view_op=list_works&sortby=pubdate

Peng Chen

Peng Chen
pchen402@gatech.edu
Scientific Machine Learning (SciML) and Uncertainty Quantification (UQ)

Dr. Chen is an Assistant Professor in the School of Computational Science and Engineering. Previously he was a Research Scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Dr. Chen’s research is in the multidisciplinary fields of computational mathematics, data science, scientific machine learning, and parallel computing with various applications in materials, energy, health, and natural hazard. Specifically, his research focuses on developing fast, scalable, and parallel computational methods for integrating data and models under high-dimensional uncertainty to make (1) statistical model learning via Bayesian inference, (2) reliable system prediction with uncertainty quantification, (3) efficient data acquisition through optimal experimental design, and (4) robust control and design by stochastic optimization.

Assistant Professor
Office
CODA | E1350B
Additional Research
Bayesian InferenceInfectious DiseasesOptimal Experimental DesignPlasma FusionStochastic OptimizationUncertainty Quantification
Research Focus Areas
Advanced Materials
Geosystems
Machine Learning
IRI And Role
Sustainable Systems > Fellow
Data Engineering and Science > Faculty
Sustainable Systems
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computational Science and Engineering
Google Scholar
https://scholar.google.com/citations?hl=en&user=AaVPa5kAAAAJ&view_op=list_works&sortby=pubdate
LinkedIn
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