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.

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.

Dr. Fan Zhang received her Ph.D. in Nuclear Engineering and M.S. in Statistics from UTK in 2019. She is the recipient of the 2021 Ted Quinn Early Career Award from the American Nuclear Society and joined the Woodruff School in July, 2021. She is actively involved with multiple international collaborations on improving nuclear cybersecurity through the International Atomic Energy Agency (IAEA) and the DOE Office of International Nuclear Security (INS). Dr. Zhang’s research primarily focuses on the cybersecurity of nuclear facilities, online monitoring & fault detection using data analytics methods, instrumentation & control, and nuclear systems modeling & simulation. She has developed multiple testbeds using both simulators and physical components to investigate different aspects of cybersecurity as well as process health management.

Chao Zhang is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology. His research area is data mining, machine learning, and natural language processing. His research aims to enable machines to understand text data in more label-efficient and robust way in open-world settings. Specific research topics include weakly-supervised learning, out-of-distribution generalization, interpretable machine learning, and knowledge extraction and reasoning. He is a recipient of Google Faculty Research Award, Amazon AWA Machine Learning Research Award, ACM SIGKDD Dissertation Runner-up Award, IMWUT distinguished paper award, and ECML/PKDD Best Student Paper Runner-up Award. Before joining Georgia Tech, he obtained his Ph.D. degree in Computer Science from University of Illinois at Urbana-Champaign in 2018.

I am currently a Senior Research Scientist at Georgia Tech working in the School of Computer Science in the College of Computing since 2015. Previously, I have worked as as a research scientist in the School of Computational Science and Engineering (CSE) from 2013 to 2015. This work focused on advanced user support and benchmarking for the Keeneland project and investigating architecture-related research topics for Dr. Jeff Vetter’s Future Technologies Group at Oak Ridge National Lab.

With a background in computer architecture, my main research interests are focused on the intersection of high-performance computing and novel accelerators including GPUs, Xeon Phi, FPGAs, and Arm SVE processors. I am currently working on a collaborative research program for near-memory computing with High Bandwidth Memory (HBM) for processors and GPUs, SuperSTARLU, which is funded by the NSF. I am co-director of Georgia Tech’s Center for High Performance Computing, and I am also the director of a novel architecture testbed, the CRNCH Rogues Gallery, that aims to simplify and democratize access to novel post-Moore accelerators in the neuromorphic, reversible, and novel networking spaces.

I defended my PhD in August 2013 in the area of computer architecture working under Dr. Sudhakar Yalamanchili. More information on this networks- and memory-related research can be found under the publications tab.

Dr. Shihao Yang is an assistant professor in the School of Industrial & Systems Engineering at Georgia Tech. Prior to joining Georgia Tech, he was a post-doc in Biomedical Informatics at Harvard Medical School after finishing his PhD in statistics from Harvard University. Dr. Yang’s research focuses on data science for healthcare and physics, with special interest in electronic health records causal inference and dynamic system inverse problems.

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.

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.

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".