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.

Dr. Andre Calmon is an Assistant Professor of Operations Management at Scheller College of Business, the co-director of Sustainable-X, and a Brook Byers Institute Faculty Fellow. Before joining Georgia Tech, he was an Assistant Professor of Operations Management at INSEAD.

Andre’s research uses data, analytics, and mathematical modeling to address sustainability and efficiency issues in innovative business models. More broadly, his research investigates how organizations can use analytics and business model innovation to generate positive social and environmental impact while increasing profits. His work has been published in premier management journals such as Management Science, Manufacturing & Service Operations Management, and Production and Operations Management.

Andre is a renowned educator, and his innovative pedagogy resulted in several award-winning new courses, case studies, and student-led ventures. In particular, the sustainability pedagogical material he developed was the Grand Prize winner of the Page Prize. Furthermore, Andre’s teaching fosters a “classroom-to-startup-to-research” pipeline, and much of his research examines new management challenges faced by startups founded by his former students.

Andre received a Ph.D. in Operations Research from MIT. He also holds an M.Sc. in Electrical Engineering from the Universidade Estadual de Campinas (Unicamp) and a B.Sc. in Electrical Engineering from the Universidade de Brasília (UnB).

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.

I am an Assistant Professor at the School of Interactive Computing in the College of Computing. I am also affiliated with the Georgia Tech Research Institute and serve as an Associate Director of ML@GT which is the machine learning center recently created at Georgia Tech. Previously I was a Research Scientist at SRI International Sarnoff in Princeton, and before that received my Ph.D. in 2010 with Professor Ron Arkin as my advisor. I lead the RobotIcs Perception and Learning (RIPL) lab. My areas of research specifically focus on the intersection of learning methods for sensor processing and robotics, developing novel machine learning algorithms and formulations towards solving some of the more difficult perception problems in these areas. I am especially interested in moving beyond supervised learning (un/semi/self-supervised and continual/lifelong learning) as well as distributed perception (multi-modal fusion, learning to incorporate information across a group of robots, etc.).

Larry P. Heck is a Professor with a joint appointment in the Schools of Electrical and Computer Engineering and Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Distinguished Chair of Advanced Computing Concepts and is a Georgia Research Alliance Eminent Scholar. His received the BSEE from Texas Tech University (1986), and MSEE and PhD EE from the Georgia Institute of Technology (1989,1991). He is a Fellow of the IEEE, inducted into the Academy of Distinguished Engineering Alumni at Georgia Tech and received the Distinguished Engineer Award from the Texas Tech University. He was a Senior Research Engineer with SRI (1992-98), VP of R&D at Nuance (1998-2005), VP of Search and Advertising Sciences at Yahoo! (2005-2009), Chief Scientist of the Microsoft Speech products and Distinguished Engineer in Microsoft Research (2009-2014), Principal Scientist with Google Research (2014-2017), CEO of Viv Labs and SVP at Samsung (2017-2021).

Alex Endert is an Associate Professor in the School of Interactive Computing at Georgia Tech. He directs the Visual Analytics Lab, where he works with his students to design and study how interactive visual tools help people make sense of data and AI. His lab often tests these advances in domains, including intelligence analysis, cyber security, decision-making, manufacturing safety, and others. His lab receives generous support from sponsors, including NSF, DOD, DHS, DARPA, DOE, and industry. In 2018, he received a CAREER award from the National Science Foundation for his work on visual analytics by demonstration. He received his Ph.D. in Computer Science from Virginia Tech in 2012. In 2013, his work on Semantic Interaction was awarded the IEEE VGTC VPG Pioneers Group Doctoral Dissertation Award, and the Virginia Tech Computer Science Best Dissertation Award.

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).

Sudheer Chava, Ph.D, is an associate director of the Institute for Information Security & Privacy for the area of risk management, and professor of finance at Scheller College of Business at the Georgia Institute of Technology. He also serves as finance area coordinator at Scheller and as the director of the nationally top 10 ranked Master of Science in Quantitative and Computational Finance (QCF) program at Georgia Tech (a joint program by the School of Mathematics, Industrial and Systems Engineering, and Scheller).  Dr. Chava has taught a variety of courses at the undergraduate, masters, MBA and Ph.D. levels, including derivatives, risk management, valuation, credit risk, financial technology ("fintech"), and management of financial institutions. He also has taught both theoretical and empirical finance doctoral courses and is a faculty advisor to multiple doctoral students. Dr. Chava's main research interests are risk management, credit risk and financial institutions. He has extensively published on these topics in the leading finance journals such as the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Monetary Economics, Journal of Financial and Quantitative Analysis, and Management Science. His research won a Ross Award for the best paper published in Finance Research Letters in 2008, was a finalist for the Brattle Prize for the best paper published in Journal of Finance in 2008, and was nominated for the Goldman Sachs Award for the best paper for published in Review of Finance during 2004.  Dr. Chava is the recipient of multiple external research grants such as FDIC-CFR Fellowship, Morgan Stanley Research grant, Financial Service Exchange Research grant, Q-group Research Award (2010, 2012) and GARP Research Award. He has presented his research at finance conferences such as AFA, WFA, EFA, Federal Reserve Banks and at many universities in the United States and abroad. Chava received his Ph.D. from Cornell University in 2003. Prior to that he earned an MBA degree from the Indian Institute of Management – Bangalore, an undergraduate degree in Computer Science Engineering, and worked as a fixed-income analyst at a leading investment bank in India. In 2014, he was awarded the Linda and Lloyd L. Byars Award for faculty research excellence at Georgia Tech and he has also received multiple research awards and fellowships at Texas A&M University.

Xiaoming Huo is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Huo's research interests include statistical theory, statistical computing, and issues related to data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE since May 2004.