Kexin Rong is an assistant professor in the School of Computer Science at Georgia Tech. She is broadly interested in developing systems and tools to help simplify large-scale data analytics, i.e., making it easy for non-experts to utilize their large and complex datasets, by synthesizing techniques from data management, machine learning, and human-computer interaction. She is part of the Georgia Tech database group. She received a Ph.D. in Computer Science from Stanford University in 2021 and a B.S. in Computer Science from the California Institute of Technology in 2015.

Saeed is an assistant professor at the School of Computer Science at Georgia Tech. Before joining Georgia Tech, he was postdoctoral associate at MIT working with Professor Mohammad Alizadeh. Saeed received his Ph.D. in computer science from Georgia Tech, where he was advised by Professors Mostafa Ammar and Ellen Zegura. His Ph.D. was partially supported by the Google Ph.D. Fellowship in Systems and Networking. He received his bachelor's degree from Alexandria University in 2010. His research interests broadly cover the theory, design, and implementation of scalable computer networks and systems, including resource scheduling, congestion control, wireless networks, and cyber-physical systems.

Eric Greenlee is a Ph.D. candidate in the School of Computer Science in the College of Computing. Eric’s research focuses on designing Internet-connected environmental sensing systems in areas with limited infrastructure. By simultaneously addressing the cost, power, and usability concerns of project stakeholders, he aspires to make sensor deployments more accessible for partners who promote environmental justice. Currently, Eric is working closely with indigenous Ojibwe knowledge-holders to co-design a sensing platform to improve outcomes for Manoomin (wild rice), which is central to the Ojibwe way of life and is especially sensitive to environmental change. He recently received the Dartmouth College Postgraduate Project Fellowship to strengthen partnerships in Madagascar, as well as the Georgia Institute of Technology President’s Fellowship. Eric loves spending time outside and hopes to make field science an integral part of his research.

Eric earned a Master of Engineering degree in Electrical and Computer Engineering from the University of Maryland, College Park, and Bachelors of Arts and Engineering degrees in Electrical Engineering from Dartmouth College.

Advisor: Ellen Zegura

Alberto Dainotti is an Associate Professor in the School of Computer Science at the College of Computing at Georgia Tech where is the Director of the Internet Intelligence Lab. His research is at the intersection of Internet measurement, data science and cybersecurity. He is interested in understanding when and how Internet infrastructure can fail and proposing remedies. To this end, he develops methods and builds near-real-time streaming data analytics systems (IODA, BGPStream, GRIP) that combine diverse data to monitor and improve Internet infrastructure security and reliability. He is also interested in understanding political motivations and implications of Internet cybersecurity events and phenomena. Before joining Georgia Tech, he was an Associate Research Scientist and Principal Investigator at CAIDA, the Center for Applied Internet Data Analysis at the San Diego Supercomputer Center, University of California San Diego. He received my Ph.D. in Computer Engineering and Systems at University of Napoli "Federico II", Italy, in 2008.

Dr. Vijay Ganesh is a professor of computer science at the Georgia Institute of Technology. Prior to joining Georgia Tech in 2023, Vijay was a professor at the University of Waterloo in Canada from 2012 to 2023 and a research scientist at the Massachusetts Institute of Technology from 2007 to 2012. Vijay completed his PhD in computer science from Stanford University in 2007. Vijay's primary area of research is the theory and practice of SAT/SMT solvers, and their application in AI, software engineering, security, mathematics, and physics. In this context he has led the development of many SAT/SMT solvers, most notably, STP, Z3str4, AlphaZ3, MapleSAT, and MathCheck. He has also proved several decidability and complexity results in the context of first-order theories. More recently he has started working on topics at the intersection of learning and reasoning, especially the use of machine learning for efficient solvers, and the use of solvers aimed at making AI more trustworthy, secure, and robust. For his research, Vijay has won over 30 awards, honors, and medals to-date, including an ACM Impact Paper Award at ISSTA 2019, ACM Test of Time Award at CCS 2016, and a Ten-Year Most Influential Paper citation at DATE 2008.

Kai Wang recently attained his Ph.D. in Computer Science at Harvard University where he was advised by Professor Milind Tambe. His research interests include multi-agent systems, computational game theory, machine learning and optimization, and their applications in public health and conservation. One of Wang's key technical contributions includes decision-focused learning, which integrates machine learning and optimization to strengthen learning performance; with his algorithms currently deployed assisting a non-profit in India focused on improving maternal and child health. He is the recipient of the Siebel Scholars award and the best paper runner-up award at AAAI 2021. 

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".

I've started as a tenure-track Assistant Professor in the School of Computer Science at Georgia Tech in August 2019, transitioning from my postdoc at the University of California Berkeley, where I worked with Ion Stoica and collaborated closely with Joseph Gonzalez. I completed my Ph.D. at Carnegie Mellon University, advised by Gregory Ganger. At Carnegie Mellon, I was honored by the prestigious NSERC Alexander Graham Bell Canada Graduate Scholarship (NSERC CGS-D3) and partially funded by the Intel Science and Technology Centre for Cloud Computing and Parallel Data Lab. Prior to Carnegie Mellon, I worked on agile stateful VM replication with para-virtualization at the University of Toronto, where I worked with Eyal de Lara and Michael Brudno. My interest in cloud computing, datacenter operating systems, and programming the cloud brought me to the University of Toronto from industry, where I had been developing cluster middleware for distributed datacenter resource management.