Elizabeth Cherry is an Associate Professor in the School of Computational Science and Engineering. Her research involves modeling and simulation, high-performance computing, and numerical methods. In particular, her group is focused on computational modeling of cardiac arrhythmias, including model development, validation, and parameter estimation; design and implementation of efficient solution methods; implementations on traditional parallel and GPGPU architectures; integration with experiments through data assimilation; and applications to understand the mechanisms responsible for particular complex dynamical states. She is a member of the editorial board of Chaos and a review editor for Frontiers in Physiology. She has served on the organizing committees of the SIAM Conference on Applications of Dynamical Systems in 2017, Dynamics Days 2020, and the Biology and Medicine Through Mathematics Conference 2018 and 2019 and on the program committees for the International Workshop on Hybrid Systems 2019 and 2020 and the International Congress on Electrocardiology 2018 and 2019. She received a BS in Mathematics from Georgetown University and a PhD in Computer Science from Duke University focusing on efficient computational methods for solving partial-differential-equations models of electrical signals in the heart. Her research is supported by the National Science Foundation and the National Institutes of Health

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.

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.

Duen Horng "Polo" Chau, Ph.D., is an Assistant Professor at Georgia Tech’s School of Computational Science and Engineering, and an Associate Director of the MS Analytics program. He holds a Ph.D. and Master's in Machine Learning from Carnegie Mellon University, where his doctoral thesis won CMU’s Computer Science Dissertation Award, Honorable Mention. Chau has received faculty awards from Google, Yahoo, and LexisNexis. He also received the Raytheon Faculty Fellowship, Edenfield Faculty Fellowship, Outstanding Junior Faculty Award. He is the only two-time Symantec fellow and an award-winning designer. Chau’s research lab  -- the Polo Club of Data Science -- bridges data mining and HCI to solve large-scale, real-world problems by developing scalable, interactive, and interpretable tools for big data analytics. The group's "Polonium" malware detection technology (patented with Symantec) protects 120 million people worldwide. Its auction fraud detection research was widely covered by media, and its fake-review-detection research received the “Best Student Paper” award at the 2014 SIAM Data Mining Conference. Other work has addressed content spam, insider trading, and unauthorized mobile device access. He co-organized the IDEA workshop series at KDD that facilitate cross-pollination across HCI and data mining. He served as general chair for ACM IUI 2015 and was a steering committee member of the conference.

Nisha Chandramoorthy is an assistant professor in the School of Computational Science and Engineering at Georgia Tech. Her research involves mathematical analyses and development of rigorous computational methods for better understanding and engineering nonlinear, possibly chaotic, dynamical systems. Some themes from her research are statistical response to perturbations, probability measure transport and high-dimensional Bayesian inference, and generalization of learning algorithms. These are motivated by fundamental scientific questions about nonlinearity as well as computational problems surrounding nonlinear systems. Both aims feed each other to improve our collective understanding of complex nonlinear processes, including in systems biology, climate studies and machine learning.

Prior to joining Georgia Tech, Nisha was a postdoctoral researcher at the Institute for Data, Systems and Society at MIT. She received her Ph.D. and master’s degrees from MIT in 2021 and 2016 respectively, and her bachelor’s degree from Indian Institute of Technology, Roorkee, in 2014.

Ümit V. Çatalyürek is currently a Professor and the Associate Chair of the School of Computational Science and Engineering in the College of Computing at the Georgia Institute of Technology. Prior joining Georgia Institute of Technology, he was a Professor and Vice Chair of the Department of Biomedical Informatics, and Professor in the Departments of Electrical & Computer Engineering, and Computer Science & Engineering at the Ohio State University. He received his Ph.D., M.S. and B.S. in Computer Engineering and Information Science from Bilkent University, Turkey, in 2000, 1994 and 1992, respectively. 

Dr. Çatalyürek is a Fellow of IEEE and SIAM. He was the elected Chair for IEEE TCPP for 2016-2019, and currently serves as Vice-Chair for ACM SIGBio for 2015-2021 terms. He also serves as the member of Board of Trustees of Bilkent University. 

He currently serves as the Editor-in-Chief for Parallel Computing. In the past, he also served on the editorial boards of the IEEE Transactions on Parallel and Distributed Computing Systems, the SIAM Journal of Scientific Computing, Journal of Parallel and Distributed Computing, and Network Modeling and Analysis in Health Informatics and Bioinformatics. He also serves on the program committees and organizing committees of numerous international conferences. 

A recipient of an NSF CAREER award, Dr. Çatalyürek is the primary investigator of several awards from the Department of Energy, the National Institute of Health, and the National Science Foundation. He has co-authored more than 200 peer-reviewed articles, invited book chapters and papers. His main research areas are in parallel computing, combinatorial scientific computing and biomedical informatics.

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).

Conan Cao is a member of the Institute for Data Engineering and Science.