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. 

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.

Dr. Haesun Park is a Regents' Professor and Chair in the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, U.S.A. She was elected as a SIAM Fellow in 2013 and IEEE Fellow in 2016 for her outstanding contributions in numerical computing, data analysis, and visual analytics. She was the Executive Director of Center for Data Analytics 2013-2015 and was the director of the NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics) Center 2008-2014. She has published extensively in the areas of numerical computing, large-scale data analysis, visual analytics, text mining, and parallel computing. She was the conference co-chair for SIAM International Conference on Data Mining in 2008 and 2009 and an editorial board member of the leading journals in computational science and engineering such as IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Matrix Analysis and Applications, and SIAM Journal on Scientific Computing. She was the plenary keynote speaker at major international conferences including SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011. Before joining Georgia Tech, she was a professor in Department of Computer Science and Engineering, University of Minnesota, Twin Cities 1987- 2005 and a program director in the Computing and Communication Foundations Division at the National Science Foundation, Arlington, VA, U.S.A., 2003 - 2005. She received a Ph.D. and an M.S. in Computer Science from Cornell University, Ithaca, NY in 1987 and 1985, respectively, and a B.S. in Mathematics from Seoul National University, Seoul, Korea in 1981 with the Presidential Medal for the top graduate.

Devi Parikh is an Assistant Professor in the School of Interactive Computing at Georgia Tech, and a Research Scientist at Facebook AI Research (FAIR). From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, Carnegie Mellon University, and Facebook AI Research. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests include computer vision and AI in general and visual recognition problems in particular. Her recent work involves exploring problems at the intersection of vision and language, and leveraging human-machine collaboration for building smarter machines. She has also worked on other topics such as ensemble of classifiers, data fusion, inference in probabilistic models, 3D reassembly, barcode segmentation, computational photography, interactive computer vision, contextual reasoning, hierarchical representations of images, and human-debugging.

Tony Pan is a Senior Research Scientist at the Institute for Data Engineering and Science (IDEaS). He develops high performance computing algorithms and implementations and data management solutions for IDEaS research efforts and collaborations. Dr. Pan's research interests focus around enabling large scale bioinformatic and biomedical studies through secure, multi-institutional data sharing and efficient parallel algorithms for architectures ranging from CPUs and GPUs to leadership-class supercomputers. He has more than two decades of experience in industry and academia in developing data science solutions for applications including cancer research cyberinfrastructure, microscopy imaging, and DNA sequence analysis.

Nepomuk Otte is a Georgia Tech Professor of Physics. When not working on his astrophysics research programs, his mind revolves around flying. His passion for flying started at a very early age but never turned into a rating. That was until 2020, when he joined the Yellow Jacket Flying Club (YJFC) and eight months later was an instrument-rated private pilot. Although he never saw himself instructing, his flight instructor and DPE encouraged him, and it turns out he loves every bit of it.

His main research is about understanding the acceleration of charged particles (cosmic rays) in pulsars and using gamma-rays as probes of Lorentz invariance violation but his group also deviates and does other interesting research in the VHE gamma-ray band. They are members of the VERITAS Cherenkov telescope array and participate in the development and construction of the next generation VHE instrument the Cherenkov Telescope Array CTA.

Shamkant B. Navathe is a noted researcher in the field of databases with more than 150 publications on different topics in the area of databases. 

He is a professor in the College of Computing at Georgia Institute of Technology and founded the Research Group in Database Systems at the College of Computing at Georgia Institute of Technology (popularly called Georgia Tech). He has been at Georgia Tech since 1990. He has been teaching in the database area since 1975 and his textbook Fundamentals of Database Systems (with Ramez Elmasri, published by Pearson, Seventh Edition, 2015) has been a leading textbook in the database area worldwide for the last 19 years. It is now in its seventh edition and is used as a standard textbook in India, Europe, South America, Australia and South-east Asia. The book has been translated into Spanish, German, French, Italian, Portuguese, Chinese, Korean, Greek, and in Arabic.

His research is in the area of bioinformatics. Navathe is working in advisory roles with Indian companies like Tata Consultancy Services (TCS), and Persistent Systems. He is also consultant for companies in information systems and software products design area and is an independent director of GTL Limited, a Mumbai-based telecommunications company.

Arkadi Nemirovski is the John P. Hunter, Jr. Chair in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. 

Dr. Nemirovski's research interests focus on Optimization Theory and Algorithms, with emphasis on investigating complexity and developing efficient algorithms for nonlinear convex programs, optimization under uncertainty, applications of convex optimization in engineering, and nonparametric statistics. 

Dr. Nemirovski has made fundamental contributions in continuous optimization in the last thirty years that have significantly shaped the field. In recognition of his contributions to convex optimization, Nemirovski was awarded the 1982 Fulkerson Prize from the Mathematical Programming Society and the American Mathematical Society (joint with L. Khachiyan and D. Yudin), the Dantzig Prize from the Mathematical Programming Society and the Society for Industrial and Applied Mathematics in 1991 (joint with M. Grotschel). He was elected a Member of the National Academy of Engineering (2017) and a Fellow of the American Academy of Arts and Sciences (2018). 

In recognition of his seminal and profound contributions to continuous optimization, Nemirovski was awarded the 2003 John von Neumann Theory Prize by the Institute for Operations Research and the Management Sciences (along with Michael Todd). He He continues to make significant contributions in almost all aspects of continuous optimization: complexity, numerical methods, stochastic optimization, and non-parametric statistics. 

Dr. Nemirovski earned a Ph.D. in Mathematics (1974) from Moscow State University, the Doctor of Sciences in Mathematics (1990) from the Supreme Attestation Board at the USSR Council of Ministers, and the Doctor of Mathematics (Honoris Causa) from the University of Waterloo, Canada (2009).

Sridhar Narasimhan is Professor of IT Management and Co-Director -Business Analytics Center (BAC), Scheller College of Business. The BAC partners with its Executive Council companies in the analytics space and supports Scheller’s BSBA, MBA, and MS Analytics programs. Professor Narasimhan has developed and taught the MBA IT Practicum course. Since 2016, he has been teaching Business Analytics to undergraduate and MBA students at Scheller. 

Professor Narasimhan is the founder and first Area Coordinator of the nationally ranked Information Technology Management area. In fall 2010, he was the Acting Dean and led the College in its successful AACSB Maintenance of Accreditation effort. He was Senior Associate Dean from 2007 through 2015.