Yao Xie is a Coca-Cola Foundation Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, which she joined in 2013 as an Assistant Professor. She also serves as Associate Director of Machine Learning and Data Science of the Center for Machine Learning. From September 2017 until March 2023 she was the Harold R. and Mary Anne Nash Early Career Professor. She was a Research Scientist at Duke University from 2012 to 2013. 

Her research lies at the intersection of statistics, machine learning, and optimization in providing theoretical guarantees and developing computationally efficient and statistically powerful methods for problems motivated by real-world applications. 

She is currently an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, Journal of the American Statistical Association: Theory and Methods, Sequential Analysis: Design Methods and Applications, INFORMS Journal on Data Science, and an Area Chair of NeurIPS and ICML.

Xiuwei Zhang is an Assistant Professor and J. Z. Liang Early Career Assistant Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology. Her research group works on applying machine learning and optimization skills in method development and data analysis for single-cell RNA-Seq data and other types of data on single cell level. The goal is to study cellular mechanisms during differentiation, development of cells and disease progression. 

Zhang was a postdoc researcher in Prof. Nir Yosef‘s group at UC Berkeley. She obtained a Ph.D. in computer science under the supervision of Prof. Bernard Moret in the Laboratory for Computational Biology and Bioinformatics, EPFL (École Polytechnique Fédérale de Lausanne), Switzerland. 

Before moving to the United States, she was a postdoc researcher in Dr. Sarah Teichmann’s group at the European Bioinformatics Institute (EBI) and Wellcome Trust Sanger Institute in Cambridge, UK. Zhang was supported by a Fellowship for Prospective Researchers and an Advanced Postdoc Mobility Fellowship from Swiss National Science Foundation (SNSF) from Aug. 2012 to Jul. 2015. She was a research fellow in the 2016 Simons Institute program on Algorithmic Challenges in Genomics. Her Erdös number is 3.

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.

Santosh Vempala is a prominent computer scientist. He is a Distinguished Professor of Computer Science at the Georgia Institute of Technology. His main work has been in the area of Theoretical Computer Science. 

Vempala secured B.Tech. degree in Computer Science and Engineering from Indian Institute of Technology, Delhi, in 1992 then he attended Carnegie Mellon University, where he received his Ph.D. in 1997 under professor Avrim Blum. 

In 1997, he was awarded a Miller Fellowship at Berkeley. Subsequently, he was a professor at MIT in the Mathematics Department, until he moved to Georgia Tech in 2006. 

His main work has been in the area of theoretical computer science, with particular activity in the fields of algorithms, randomized algorithms, computational geometry, and computational learning theory, including the authorship of books on random projection and spectral methods. 

In 2008, he co-founded the Computing for Good (C4G) program at Georgia Tech.

Vempala has received numerous awards, including a Guggenheim Fellowship, Sloan Fellowship, and being listed in Georgia Trend's 40 under 40.[5] He was named Fellow of ACM "For contributions to algorithms for convex sets and probability distributions" in 2015.[6] He was named a Fellow of the American Mathematical Society, in the 2022 class of fellows, "for contributions to randomized algorithms, high-dimensional geometry, and numerical linear algebra, and service to the profession".

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.

I joined the Center for Relativistic Astrophysics at GeorgiaTech in January 2015, from the University of Massachusetts Amherst. My principal research interests is gravitational wave astrophysics and LIGO – I have been a member of the LIGO Scientific Collaboration since 2002. I am also interested in particle astrophysics; I have been a member of the Borexino Collaboration (solar neutrino detection) until 2013 and the DarkSide Collaboration (direct dark matter search) until 2014.