Munmun De Choudhury is an Associate Professor at the School of Interactive Computing in Georgia Institute of Technology. Dr. De Choudhury is renowned for her groundbreaking contributions to the fields of computational social science, human-computer interaction, and digital mental health. Through fostering interdisciplinary collaborations across academia, industry, and public health sectors, Dr. De Choudhury and her collaborators have contributed significantly to advancing the development of computational techniques for early detection and intervention in mental health, as well as in unpacking how social media use benefits or harms mental well-being. De Choudhury's contributions have been recognized worldwide, with significant scholarly impact evidenced by numerous awards like induction into the SIGCHI Academy and the 2023 SIGCHI Societal Impact Award. Beyond her academic achievements, Dr. De Choudhury is a proactive community leader, a persistent contributor to policy-framing and advocacy initiatives, and is frequently sought for expert advice to governments, and national and international media.

Juba Ziani is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering. Prior to this, Juba was a Warren Center Postdoctoral Fellow at the University of Pennsylvania, hosted by Sampath Kannan, Michael Kearns, Aaron Roth, and Rakesh Vohra. Juba completed his Phd at Caltech in the Computing and Mathematical Sciences department, where he was advised by Katrina Ligett and Adam Wierman.

Juba studies the optimization, game theoretic, economic, ethical, and societal challenges that arise from transactions and interactions involving data. In particular, his research focuses on the design of markets for data, on data privacy with a focus on "differential privacy", on fairness in machine learning and decision-making, and on strategic considerations in machine learning.

Enlu Zhou is a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She received the B.S. degree with highest honors in electrical engineering from Zhejiang University, China, in 2004, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2009. Prior to joining Georgia Tech in 2013, she was an assistant professor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign from 2009-2013. She is a recipient of the Best Theoretical Paper award at the Winter Simulation Conference, AFOSR Young Investigator award, NSF CAREER award, and INFORMS Outstanding Simulation Publication Award. She has served as an associate editor for Journal of Simulation, IEEE Transactions on Automatic Control, and Operations Research. She is currently the Vice President and President-Elect of the INFORMS Simulation Society.

Mayya Zhilova is an associate professor in the School of Mathematics at Georgia Tech and an affiliated member of the Machine Learning Center. She received her Ph.D. in statistics from the Humboldt University of Berlin in 2015. 

Her primary research interests lie in the areas of mathematical statistics, statistical learning theory, and uncertainty quantification, particularly in statistical inference for complex high-dimensional data, performance of resampling procedures for various classes of problems, functional estimation, and inference for misspecified models.

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.