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.

Joy Arulraj is an assistant professor in the School of Computer Science at Georgia Institute of Technology. His research interest is in database management systems, specifically large-scale data analytics, main memory systems,  machine learning, and big code analytics. At Georgia Tech, he is a member of the Database group.