Calton Pu, Ph.D., is a Professor in the School of Computer Science, College of Computing at the Georgia Institute of Technology. His research interests are in the areas of distributed computing, Internet data management, and operating systems. His current projects fall under the areas of cloud computing (Elba) and big data (GRAIT-DM) research. Using experimental data from realistic benchmarks, the Elba project studies the  phenomena of very short bottlenecks that have large impact on N-tier system response time. The GRAIT-DM project collects real world data from social sensors (e.g., Twitter and YouTube) and physical sensors (e.g., USGS GSN and NASA TRMM) to detect physical events and manage real-time information on them. The sponsors for Pu's research include both government funding agencies such as the National Science Foundation, and companies from industry such as IBM, Intel, and Hewlett-Packard.  He is a co-director of Center for Experimental Research in Computer Systems (CERCS) and affiliate of the Institute for Information Security and Privacy (IISP) at Georgia Tech.  Pu has taught several courses in the areas of systems and databases. In Fall, he teaches CS4220/6235 Real-Time Embedded Systems. In spring, he teaches CS4365/6365 Introduction to Enterprise Computing.

Milos Prvulovic, Ph.D., is a professor in the School of Computer Science, College of Computing at the Georgia Institute of Technology. His research focuses on hardware and software support for program monitoring, debugging, and security. His research of side-channel emmanations and side-channel attacks has led to widespread interest from professional societies, the media and additional reserach sponsors -- most recently attracting a $9.4 million award from the Defense Advanced Research Projects Agency (DARPA) for continued study. In general, the goal of his research is to make both hardware and software more reliable and secure. Prvulovic is a senior member of Association of Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE), served as the chair of the IEEE Technical Committee on Microprogramming and Microarchitecture in 2016, and is a member of the Steering Committee for the ACM/IEEE MICRO conference. Prvulovic received his doctorate from the University of Illinois at Urbana-Champaign.

Roberto Perdisci is an Associate Professor in the Computer Science at the University of Georgia; an Adjunct Associate Professor in the School of Computer Science at the Georgia Institute of Technology, and a faculty member of the UGA Institute for Artificial Intelligence. Before joining UGA, he was a post-doctoral fellow at the College of Computing of the Georgia Institute of Technology, working under the supervision of Wenke Lee. He also worked as Principal Scientist at Damballa, Inc., and prior to joining Damballa, he was Research Scholar at the Georgia Tech Information Security Center and Ph.D. candidate at the University of Cagliari, Italy with the Pattern Recognition and Applications Group. His research focuses on securing networked systems. He is particularly interested in web security, automating the analysis of security incidents, and defending networks from malware. He often combines systems research with machine learning and large-scale data mining techniques to solve challenging computer and network security problems. Perdisci also is interested in broader aspects of networked systems, including Internet-scale measurements, analysis and optimization of systems performance, and the design of networking protocols. In 2012, he received the National Science Foundation CAREER award for a project titled "Automatic Learning of Adaptive Network-Centric Malware Detection Models."

Paul Pearce is an Assistant Professor at the Georgia Tech School of Computer Science and a Visiting Researcher at Facebook. By developing Internet-scale measurement platforms and new empirical methods, his research brings grounding and understanding to the study of large-scale, hidden Internet security problems. His work spans the areas of cybercrime, censorship, and “advanced persistent threats” (APTs). His work has been distinguished at the IEEE Symposium on Security and Privacy, and he has been recognized as an EECS Distinguished Graduate Student Instructor.  Paul completed his Ph.D. at UC Berkeley advised by Vern Paxson and was a member of the Center for Evidence-based Security Research (CESR).

Santosh Pande is an Associate Professor in the School of Computer Science, College of Computing at the Georgia Institute of Technolohgy. Pande's primary interest is in investigating static and dynamic compiler optimizations on evolving architectures. His research philosophy involves tackling practical problems which are relevant and important to the current issues in systems research and propose foundational solutions to them for good impact. Currently, his research is focused on developing compiler optimizations for embedded and configurable systems to improve code size, efficiency and power consumption. His work in this area has resulted in several techniques for efficient compilation given limited memory sizes, limited addressing modes and data paths on embedded processors. His research is primarily supported by the National Science Foundation and Defense Advanced Research Projects Agency.  He has published more than 40 papers in journals and conferences which include ACM Programming Language Design and Implementation (PLDI), IEEE Real Time Systems Symposium (RTSS) and Journal of Parallel and Distributed Computing (JPDC) and IEEE Transactions on Parallel and Distributed Systems (TPDS). He has also done extensive compiler development and is a part of SUIF collaborative program amongst universities. He has also managed and delivered large scale software projects to the funding agencies such as DARPA. He served as a co-guest editor for the special issue of Journal of Parallel and Distributed Systems on `Compilation Techniques for Distributed Memory Systems' published in December 1996 and also for the special issue of the Parallel Processing Letters journal on `Challenges in Compiler Optimizations for Scalable Parallel Systems' published in December 1997. He has also served on program committees of many conferences. He co-chaired ACM LCTES '01 and served on the program committee of PLDI '01. He served as an IEEE Distinguished Visitor for the period of 1996-2000.

Alessandro Orso, Ph.D., is a Professor and Associate School Chair in the School of Computer Science, College of Computing at the Georgia Institute of Technology. His research interests are in the areas of software engineering, with emphasis on software testing and program analysis, and include the development of techniques and tools for improving software reliability, security, and trustworthiness, and the validation of such techniques on real-world systems. Orso has received funding for his research from both government agencies, such as the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation as well as from industry leaders, such as Fujitsu Labs, Google, IBM, and Microsoft. He served on the editorial boards of ACM TOSEM and on the Advisory Board of Reflective Corp, served as program chair for ACM-SIGSOFT ISSTA 2010 and program co-chair for IEEE ICST 2013 and ACM-SIGSOFT FSE 2014, and will serve as program co-chair for ACM-SIGSOFT/IEEE ICSE 2017. He has also served as a technical consultant to DARPA. He is a senior member of the ACM and of the IEEE Computer Society. Orso received his Master's in Electrical Engineering (1995) and his Ph.D. in Computer Science (1999) from Politecnico di Milano, Italy. Since March 2000, he has taught at Georgia Tech.

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.