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

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

Chris Gu is an Assistant Professor of Marketing in the Scheller College of Business at Georgia Institute of Technology. His research focuses on the quantitative study of the behaviors of individuals and organizations under various types of information constraints and economic structures, with the goal of improving their well-being. His current work focuses on understanding how consumers search for products under partially revealed information, how consumers adopt sustainable technologies under the influence of government policies, how companies decide about internal technology adoption and upgrade, and how social network connections influence individual crowdsourcing behaviors. He is an AMS Mary Kay Foundation Doctoral Dissertation Competition Finalist, and his research has received the ISMS Doctoral Dissertation Award.

Ben Wang is Professor Emeritus in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. In addition, Dr. Wang previously served as the Executive Director of the Georgia Tech Manufacturing Institute. 

Dr. Wang's primary research interest is in applying emerging technologies to improve manufacturing competitiveness. He specializes in process development for affordable composite materials. Dr. Wang is widely acknowledged as a pioneer in the growing field of nanomaterials science. His main area of research involves a material known as "buckypaper", which has shown promise in a variety of applications, including the development of aerospace structures, improvements in energy and power efficiency, enhancements in thermal management of engineering systems, and construction of the next-generation of computer displays.

Dr. Wang served on the National Materials and Manufacturing Board (NMMB). NMMB is the principal forum at the U.S. National Academies for issues related to innovative materials and advanced manufacturing, and has oversight responsibility for National Research Council activities in these technology areas. Dr. Wang is a Fellow of the Institute of Industrial Engineers, the Society of Manufacturing Engineers, and the Society for the Advancement of Material and Process Engineering.

Because of his contributions to advanced manufacturing and materials, Dr. Wang was invited to deliver a presentation to the U.S. National Research Council Review Panel in support of the U.S. National Nanotechnology Initiative in 2005. In 2012, he was invited to give testimony before the National Academies Committee on Manufacturing Extension Partnership. In 2012 he was invited to participate in the Roundtable on Strengthening U.S. Advanced Manufacturing in Clean Energy in the White House.

In addition to authoring or co-authoring more than 240 refereed journal papers, he is a co-author of three books: Computer-Aided Manufacturing (Prentice-Hall, 1st Edition, 2nd Edition, and 3rd Edition), Computer-Aided Process Planning (Elsevier Science Publishers), and Computer Aided Manufacturing PC Application Software (Delmar Publishers).

Dr. Wang earned his bachelor's in industrial engineering from Tunghai University in Taiwan, and his master's in industrial engineering and Ph.D. from Pennsylvania State University.

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.

Tuo Zhao is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering and the school of Computational Science and Engineering (By Courtesy) at Georgia Tech. 

His research focuses on developing principled methodologies, nonconvex optimization algorithms and practical theories for machine learning (especially deep learning). He is also interested in natural language processing and actively contributing to open source software development for scientific computing. 

Tuo Zhao received his Ph.D. degree in Computer Science at Johns Hopkins University in 2016. He was a visiting scholar in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health from 2010 to 2012, and the Department of Operations Research and Financial Engineering at Princeton University from 2014 to 2016. 

He was the core member of the JHU team winning the INDI ADHD 200 global competition on fMRI imaging-based diagnosis classification in 2011. He received the Google summer of code awards from 2011 to 2014. He received the Siebel scholarship in 2014, the Baidu Fellowship in 2015-2016 and Google Faculty Research Award in 2020. He was the co-recipient of the 2016 ASA Best Student Paper Award on Statistical Computing and the 2016 INFORMS SAS Best Paper Award on Data Mining.

Prof. Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. His research develops data science solutions to address the high-stakes challenges on the web and in the society. He has pioneered the development of user models and network science tools to enhance the well-being and safety of people. Applications of his research widely span e-commerce, social media, finance, health, web, and cybersecurity. His methods to predict malicious users and false information have been widely adopted in practice (being used in production at Flipkart and Wikipedia) and taught at graduate level courses worldwide. He has received several awards including the ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and best paper awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.

Siva is Fouts Family Early Career Professor and an Assistant Professor in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech.

Before joining Georgia Tech, he spent two years in the Stochastic Processes and Optimization group, which is part of the Mathematical Sciences Department at the IBM T. J. Watson Research Center. He received my Ph.D. in ECE from the University of Illinois at Urbana-Champaign in 2014 and was advised by Prof R. Srikant. Before that, he received an MS in ECE from UIUC, which was advised by Prof R. Srikant and Prof. Bruce Hajek. Maguluri also hold an MS in Applied Maths from UIUC. He obtained my B.Tech in Electrical Engineering from Indian Institute of Technology Madras.

Maguluri received the NSF CAREER award in 2021, 2017 Best Publication in Applied Probability Award from INFORMS Applied Probability Society, and the second prize in 2020 INFORMS JFIG best paper competition. Joint work with his students received the Stephen S. Lavenberg Best Student Paper Award at IFIP Performance 2021. As a recognition of his teaching efforts, Siva received the Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award in 2020 for ISyE 6761 and the CTL/BP Junior Faculty Teaching Excellence Award, also in 2020, both presented by the Center for Teaching and Learning at Georgia Tech.

Richard (Rich) Vuduc is an Associate Professor at the Georgia Institute of Technology (“Georgia Tech”), in the School of Computational Science and Engineering, a department devoted to the study of computer-based modeling and simulation of natural and engineered systems. His research lab, The HPC Garage (@hpcgarage), is interested in high-performance computing, with an emphasis on algorithms, performance analysis, and performance engineering. He is a recipient of a DARPA Computer Science Study Groupgrant; an NSF CAREER award; a collaborative Gordon Bell Prize in 2010; Lockheed-Martin Aeronautics Company Dean’s Award for Teaching Excellence (2013); and Best Paper Awards at the SIAM Conference on Data Mining (SDM, 2012) and the IEEE Parallel and Distributed Processing Symposium (IPDPS, 2015), among others. He has also served as his department’s Associate Chair and Director of its graduate programs. External to Georgia Tech, he currently serves as Chair of the SIAM Activity Group on Supercomputing (2018-2020); co-chaired the Technical Papers Program of the “Supercomputing” (SC) Conference in 2016; and serves as an associate editor of both the International Journal of High-Performance Computing Applications and IEEE Transactions on Parallel and Distributed Systems. He received his Ph.D. in Computer Science from the University of California, Berkeley, and was a postdoctoral scholar in the Center for Advanced Scientific Computing the Lawrence Livermore National Laboratory.

Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data systems and analytics, including performance, availability, security, privacy and trust. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award (2012). She has published over 300 international journal and conference articles and is a recipient of the best paper award from numerous top venues, including ICDCS, WWW, IEEE Cloud, IEEE ICWS, ACM/IEEE CCGrid. In addition to serve as general chair and PC chairs of numerous IEEE and ACM conferences in big data, distributed computing, cloud computing, data engineering, very large databases fields, Prof. Liu served as the editor in chief of IEEE Transactions on Service Computing (2013-2016), on editorial board of over a dozen international journals. Ling’s current research is sponsored primarily by NSF and IBM.