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.

Animesh Garg is a Stephen Fleming Early Career Assistant Professor at School of Interactive Computing at Georgia Tech. He leads the People, AI, and Robotics (PAIR) research group. He is on the core faculty in the Robotics and Machine Learning programs. Animesh is also a Senior Researcher at Nvidia Research. Animesh earned a Ph.D. from UC Berkeley and was a postdoc at the Stanford AI Lab. He is on leave from the department of Computer Science at University of Toronto and CIFAR Chair position at the Vector Institute.

Garg earned his M.S. in Computer Science and Ph.D. in Operations Research from UC, Berkeley. He worked with Ken Goldberg at Berkeley AI Research (BAIR). He also worked closely with Pieter Abbeel, Alper Atamturk & UCSF Radiation Oncology. Animesh was later a postdoc at Stanford AI Lab with Fei-Fei Li and Silvio Savarese.

Garg's research vision is to build the Algorithmic Foundations for Generalizable Autonomy, that enables robots to acquire skills, at both cognitive & dexterous levels, and to seamlessly interact & collaborate with humans in novel environments. His group focuses on understanding structured inductive biases and causality on a quest for general-purpose embodied intelligence that learns from imprecise information and achieves flexibility & efficiency of human reasoning.

I am an Assistant Professor in the School of Computational Science and Engineering (CSE), Georgia Institute of Technology since January 2022. I received my PhD from the Department of Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Jian Peng. Prior to that, I received my bachelor’s degree in Computer Science from Yao Class at Tsinghua University in 2016.

I am broadly interested in computational biology and machine learning, with a focus on developing AI and data science methods to reveals core scientific insights into biology and medicine. Recent interests include deep learning, transfer learning, sequence and graph representation learning, network and system biology, functional genomics, cancer genomics, drug repositioning and discovery, and AI-guided biological design and discovery.

Tom Conte holds a joint appointment in the Schools of Electrical & Computer Engineering and Computer Science at the Georgia Institute of Technology. He is the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH). His research is in the areas of computer architecture and compiler optimization, with emphasis on manycore architectures, microprocessor architectures, back-end compiler code generation, architectural performance evaluation and embedded computer system architectures.

Ada Gavrilovska is an Associate Professor at the College of Computing and a researcher with the Center for Experimental Research in Computer Systems (CERCS) at Georgia Tech. Her interests include experimental systems, focusing on operating systems, virtualization, and systems software for heterogeneous many-core platforms, emerging non-volatile memories, large scale datacenter and cloud systems, high-performance communication technologies and support for novel end-user devices and services. Her research is supported by the National Science Foundation, the US Department of Energy, and industry grants, including from Cisco, HP, IBM, Intel, Intercontinental Exchange, LexisNexis, VMware, and others. She has published numerous book chapters, journal and conference publications, and edited a book “High Performance Communications: A Vertical Approach” (CRC Press, 2009). In addition to research, she also teaches courses on operating systems and high performance communications. She has a Bachelor's  in Computer Engineering from University Sts. Cyril and Methodius in Macedonia ('98), and a Master's ('99) and Ph.D. ('04) degrees in Computer Science from Georgia Tech.

Richard Fujimoto is a Regents’ Professor, Emeritus in the School of Computational Science and Engineering at the Georgia Institute of Technology. He received the Ph.D. degree from the University of California-Berkeley in 1983 in Computer Science and Electrical Engineering. He also received an M.S. degree from the same institution as well as two B.S. degrees from the University of Illinois-Urbana. 

Fujimoto is a pioneer in the parallel and distributed discrete event simulation field. Discrete event simulation is widely used in areas such as telecommunications, transportation, manufacturing, and defense, among others. His work developed fundamental understandings of synchronization algorithms that are needed to ensure the correct execution of discrete event simulation programs on high performance computing (HPC) platforms. His team developed many new algorithms and computational techniques to accelerate the execution of discrete event simulations and developed software realizations that impacted several application domains. For example, his Georgia Tech Time Warp software was deployed by MITRE Corp. to create online fast-time simulations of commercial air traffic to help reduce delays in the U.S. National Airspace. An active researcher in this field since 1985, he authored or co-authored three books and hundreds of technical papers including seven that were cited for “best paper” awards or other recognitions. His research included several projects with Georgia Tech faculty in telecommunications, transportation, sustainability, and materials leading to numerous publications co-authored with faculty across campus.

Jacob Abernethy is an Associate Professor in the College of Computing at Georgia Tech. He started his faculty career in the Department of Electrical Engineering and Computer Science at the University of Michigan. He completed his Ph.D. in Computer Science at the University of California at Berkeley, and then spent two years as a Simons postdoctoral fellow at the CIS department at UPenn. Abernethy's primary interest is in Machine Learning, with a particular focus in sequential decision making, online learning, online algorithms and adversarial learning models. He did his Master's degree at TTI-C, and his Bachelor's Degree at MIT.