Roshan Vengazhiyil Joseph is a A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Joseph's research interests are in the broad areas of applied and computational statistics. A major focus of his research is in developing novel data analytic methods for solving complex engineering problems. He has several years of consulting experience in solving quality-related problems in industries.

Dr. Joseph's honors include Distinguished Dissertation Award from the University of Michigan in 2003, CAREER Award from National Science Foundation in 2005, Jack Youden Prize from ASQ in 2005, Coca-Cola Junior Chair Professorship from ISYE in 2008, Best Paper Award from IIE Transactions in 2009, Franz Edelman Laureate from INFORMS in 2017, Statistics in Physical & Engineering Sciences Award from ASA in 2019, SPAIG Award from the ASA in 2020, and Lloyd S. Nelson Award from ASQ in 2021. He is a Fellow of ASA (elected in 2012) and ASQ (elected in 2020). Currently he is serving as the Editor of Technometrics (2020-2022).

Dr. Joseph received a Ph.D. degree in Statistics from the University of Michigan, Ann Arbor in 2002 and holds an M.Tech. degree in Quality, Reliability, and Operations Research and a B.Tech. degree in Production Engineering and Management. 

Aaron Drysdale, a Master of Computer Science graduate from Georgia Tech, is the Chief Technologist at the Cloud Hub. He manages the proposal process for research grants, organizes industry training sessions, and provides direct technical support to research teams utilizing cloud resources. Aaron's role also involves collaborating with Microsoft’s technical teams to resolve complex issues, ensuring seamless and efficient research progress. His expertise and proactive approach are vital to the success of the Cloud Hub's mission to advance innovative research.

Helen Xu comes to Georgia Tech from Lawrence Berkeley National Laboratory where she was the 2022 Grace Hopper Postdoctoral Scholar. She completed her Ph.D. at MIT in 2022 with Professor Charles E. Leiserson. Her main research interests are in parallel and cache-friendly algorithms and data structures. Her work has previously been supported by a National Physical Sciences Consortium fellowship and a Chateaubriand fellowship. She has interned at Microsoft Research, NVIDIA Research, and Sandia National Laboratories. 

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. 

Raphaël Pestourie earned his Ph.D. in Applied Mathematics and an AM in Statistics from Harvard University in 2020. Prior to Georgia Tech, he was a postdoctoral associate at MIT Mathematics, where he worked closely with the MIT-IBM Watson AI Lab. Raphaël’s research focuses on scientific machine learning at the intersection of applied mathematics and machine learning and inverse design via scientific machine learning and large-scale electromagnetic design. 

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.

Moinuddin Qureshi is a Professor of Computer Science at Georgia Tech. His research interests include computer architecture, memory systems, hardware security, and quantum computing. Previously, he was a research staff member (2007-2011) at IBM T.J. Watson Research Center, where he developed the caching algorithms for Power-7 processors. He is a member of the Hall of Fame for ISCA, MICRO, and HPCA. His research has been recognized with the best paper award at MICRO 2018, best paper award at HiPC 2014, and two awards (and three honorable mentions) at IEEE MICRO Top Picks. His ISCA 2009 paper on Phase Change Memory was awarded the 2019 Persistent Impact Prize in recognition of “exceptional impact on the fields of study related to non-volatile memories”. He was the Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017.  He received his Ph.D. (2007) and M.S. (2003) from the University of Texas at Austin.