Kai Wang

Kai Wang
kwang692@gatech.edu

Kai Wang recently attained his Ph.D. in Computer Science at Harvard University where he was advised by Professor Milind Tambe. His research interests include multi-agent systems, computational game theory, machine learning and optimization, and their applications in public health and conservation. One of Wang's key technical contributions includes decision-focused learning, which integrates machine learning and optimization to strengthen learning performance; with his algorithms currently deployed assisting a non-profit in India focused on improving maternal and child health. He is the recipient of the Siebel Scholars award and the best paper runner-up award at AAAI 2021. 

Assistant Professor
Additional Research

AI for Social ImpactData-Driven Decision MakingMulti-Agent SystemsOptimization

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science

Raphaël Pestourie

Raphaël Pestourie
rpestourie3@gatech.edu

Raphaël Pestourie earned his Ph.D. in Applied Mathematics and an AM in Statistics from Harvard University in 2020. Prior to Georgia Tech, he was a postdoctoral associate at MIT Mathematics, where he worked closely with the MIT-IBM Watson AI Lab. Raphaël’s research focuses on scientific machine learning at the intersection of applied mathematics and machine learning and inverse design via scientific machine learning and large-scale electromagnetic design. 

Assistant Professor
Additional Research

Scientific Machine LearningInverse Design in Electromagnetism

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
Matter and Systems > Affiliated Faculty
Energy > Research Community
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science
Research Areas
Matter and Systems
  • Computing and Communication Technologies
Energy
  • Energy

Kamran Paynabar

Kamran Paynabar
kamran.paynabar@isye.gatech.edu

Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran in 2002 and 2004, respectively, and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles. He is a recipient of the INFORMS Data Mining Best Student Paper Award, the Best Application Paper Award from IIE Transactions, the Best QSR refereed paper from INFORMS, and the Best Paper Award from POMS. He has been recognized with the Georgia Tech campus level 2014 CETL/BP Junior Faculty Teaching Excellence Award and the Provost Teaching and Learning Fellowship. He served as the chair of QSR of INFORMS, and the president of QCRE of IISE.

Assistant Professor
Phone
404.385.3141
Office
Groseclose Building, Room 436
Additional Research

High-dimensional data analysis for systems monitoring, diagnostics and prognostics, and statistical and machine learning for complex-structured streaming data including multi-stream signals, images, videos, point clouds and network data with applications ranging from manufacturing including automotive and aerospace to healthcare.

IRI and Role
Manufacturing > Affiliated Faculty
Data Engineering and Science > Faculty
Data Engineering and Science > TRIAD Associate
Renewable Bioproducts > Affiliated Faculty
Manufacturing
Data Engineering and Science
Renewable Bioproducts
University, College, and School/Department
Georgia Institute of Technology > College of Engineering > School of Industrial Systems Engineering

Suresh Marru

Suresh Marru
smarru@gatech.edu

Suresh Marru is a research professor dedicated to advancing science and engineering through AI and cyberinfrastructure. Over the past two decades, he has focused on accelerating and democratizing computational science. His work includes the development of science gateways and the pioneering of the Apache Airavata distributed systems framework.

In his current role as the Director of Georgia Tech's ARTISAN Center, his team is at the forefront of pioneering efforts to integrate AI into diverse scientific domains. His group is dedicated to bridging the gap between theory, experimentation, and computation by fostering open-source integration frameworks. These frameworks automate research processes, optimize complex models, and integrate disparate scientific data with simulation engines.

Collaboration is at the heart of Suresh’s ethos. He has had the privilege of working alongside brilliant scientists and technologists, contributing to groundbreaking research in domains such as geosciences, neuroscience, and molecular dynamics. These collaborations have not only accelerated scientific discovery but have also offered valuable insights into the potential of AI in scientific innovation.

Beyond his professional endeavors, Suresh is deeply passionate about open science and open-source software. He also believes in building synergies between academia and industry. He has played an instrumental role in a series of tech startups. Currently, he serves as the Chief Technology Officer at Folia, a company dedicated to unleashing the power of annotations.

Director, Georgia Tech Center for Artificial Intelligence in Science and Engineering (ARTISAN)
Research Professor, Institute for Data Engineering and Science (IDEaS)
Phone
405.816.1686
Office
CODA 12th Floor | #1217
Additional Research

Atmospheric SciencesComputer ModelingCyberinfrastructureData Fusion and IntegrationOpen Science Integration FrameworksScience Gateway Frameworks

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science > Leadership
Data Engineering and Science > Research Professional
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computational Science and Engineering

Divya Mahajan

Divya Mahajan
divya.mahajan@gatech.edu

Divya is an Assistant Professor in School of ECE and Computer Science. Divya received her Ph.D. from Georgia Institute of Technology and Master’s from UT Austin. She obtained her Bachelor’s from IIT Ropar where she was conferred the Presidents of India Gold Medal, the highest academic honor in IITs.

Prior to joining Georgia Tech, Divya was a Senior Researcher at Microsoft Azure since September 2019. Her research has been published in top-tier venues such as ISCA, HPCA, MICRO, ASPLOS, NeurIPS, and VLDB. Her dissertation has been recognized with the NCWIT Collegiate Award 2017 and distinguished paper award at High Performance Computer Architecture (HPCA), 2016.

Currently, she leads the Systems Infrastructure and Architecture Research Lab at Georgia Tech. Her research team is devising next-generation sustainable compute platforms targeting end-to-end data pipeline for large scale AI and machine learning. The work draws insights from a broad set of disciplines such as, computer architecture, systems, and databases.

Assistant Professor
Additional Research

Computer ArchitectureSystems for Machine LearningLarge Scale Infrastructure for AI and Data Storage

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Computer Science
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering

Yingyan (Celine) Lin

Yingyan (Celine) Lin
celine.lin@gatech.edu

Yingyan (Celine) Lin is currently an Associate Professor in the School of Computer Science at the Georgia Institute of Technology. She leads the Efficient and Intelligent Computing (EIC) Lab, which focuses on developing efficient machine learning systems via cross-layer innovations from algorithm to architecture down to chip design, aiming to promote green AI and enable ubiquitous machine learning powered intelligence. She received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017. 

Prof. Lin is a Facebook Research Award (2020), NSF CAREER Award (2021), IBM Faculty Award (2021), and Meta Faculty Research Award (2022) recipient, and received the ACM SIGDA Outstanding Young Faculty Award in 2022. She was selected as a Rising Star in EECS by the 2017 Academic Career Workshop for Women at Stanford University. She received the Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016), and the 2016 Robert T. Chien Memorial Award for Excellence in Research at UIUC. Prof. Lin is currently the lead PI of multiple multi-university projects, such as RTML and 3DML, and her group has been funded by NSF, NIH, DARPA, SRC, ONR, Qualcomm, Intel, HP, IBM, and Meta. Her group’s research won first place in both the University Demonstration at DAC 2022 and the ACM/IEEE TinyML Design Contest at ICCAD 2022, and was selected as an IEEE Micro Top Pick of 2023

Associate Professor
IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Computing

Pan Li

Pan Li
panli@gatech.edu

Pan Li joined Georgia Tech in 2023 Spring. Before that, Pan Li worked at the Purdue Computer Science Department as an assistant professor from the 2020 fall to the 2023 Spring. Before joining Purdue, Pan worked as a postdoc at Stanford Computer Science Department from 2019 to 2020. Pan did his Ph.D. in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. Pan Li has got the NSF CAREER award, the Best Paper award from the Learning on Graph Conference, Sony Faculty Innovation Award, JPMorgan Faculty Award.

Assistant Professor
Office
CODA Number S1219
Additional Research

Develop and analyze more expressive, generalizable, robust machine learning algorithms with graph and geometric data, using e.g., Graph neural networks, geometric deep learning, and equivariant models.  Build scalable analysis and learning tools for large-scale graph data, such as graph and hypergraph clustering algorithms, and large-scale graph machine learning.    Artificial Intelligence for Science: Interpretable and trustworthy graph machine learning for physics.

IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Engineering > School of Electrical and Computer Engineering

Alexander Lerch

Alexander Lerch
alexander.lerch@gatech.edu

Alexander Lerch is an Associate Professor at the School of Music, Georgia Institute of Technology. He received his "Diplom-Ingenieur'' (EE) and his PhD (Audio Communications) from Technical University Berlin. Lerch joined Georgia Tech in 2013 and teaches classes on music signal processing, computational music analysis, audio technology, and audio software engineering. Before he joined Georgia Tech, Lerch was Head of Research at his company zplane.development, an industry leader in music technology licensing. zplane technology includes algorithms such as time-stretching and automatic key detection and is used by millions of musicians and producers world-wide.       

Lerch's research focuses on teaching computers to listen to and comprehend music. His research field, Music Information Retrieval (MIR), positions him at the intersection of signal processing, machine learning, music psychology, and systematic musicology. His Music Informatics Group (http://www.musicinformatics.gatech.edu) creates artificially intelligent software for music generation, production, and consumption and generates new insights into music and its performance.

Lerch authored more than 40 peer-reviewed journal and conference papers. His text book "An Introduction to Audio Content Analysis" (IEEE/Wiley 2012) and the accompanying online materials at www.AudioContentAnalysis.org helped define educational practice in the field.

Associate Dean of Research and Creative Practice
Associate Professor
IRI and Role
Data Engineering and Science > Faculty
Data Engineering and Science
University, College, and School/Department
Georgia Institute of Technology > College of Design > School of Music

Giri Krishnan

Placeholder for headshot
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

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

Irfan Essa

Irfan Essa
irfan@cc.gatech.edu

Irfan Essa is a Professor in the School of Interactive Computing and Senior Associate Dean in the College of Computing (CoC), at the Georgia Institute of Technology. Professor Essa works in the areas of Computer Vision, Artificial Intelligence, Machine Learning, Robotics, Computer Graphics, and Social Computing, with potential impact on Content Creation, Analysis and Production (e.g., Computational Photography & Video, Image-based Modeling and Rendering, etc.) Human Computer Interaction, Artificial Intelligence, Computational Behavioral/Social Sciences, and Computational Journalism research.He has published over 150 scholarly articles in leading journals and conference venues on these topics and several of his papers have also won best paper awards. He has been awarded the National Science Foundation CAREER Award and was elected an IEEE Fellow. He has held extended research consulting positions with Disney Research and Google Research and also was an Adjunct Faculty Member at Carnegie Mellon's Robotics Institute. He joined Georgia Tech in 1996 after his earning his Master's (1990), Ph.D. (1994), and holding a research faculty position at the Massachusetts Institute of Technology Media Lab (1988-1996).

Senior Associate Dean; College of Computing
Professor; School of Interactive Computing
Phone
404.894.6856
Additional Research

Healthcare Security; Machine Learning; Mobile & Wireless Communications; Computer Vision and Robotics; Computer Graphics and Animation; Computational Photography and Video; Intelligent and Aware Environments; Digital Special Effects; Computational Journalism; Social Computing

IRI and Role
Data Engineering and Science > Faculty
People and Technology > Affiliated Faculty
Robotics > Core Faculty
Data Engineering and Science
People and Technology
Robotics
University, College, and School/Department
Georgia Institute of Technology > College of Computing > School of Interactive Computing