Srinivas Aluru is Executive Director of the Institute for Data Engineering and Science (IDEaS) and Professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He co-leads the NSF South Big Data Regional Innovation Hub which nurtures big data partnerships between organizations in the 16 Southern States and Washington D.C., and the NSF Transdisciplinary Research Institute for Advancing Data Science. Aluru conducts research in high performance computing, large-scale data analysis, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. An early pioneer in big data, Aluru led one of the eight inaugural mid-scale NSF-NIH Big Data projects awarded in the first round of federal big data investments in 2012. He has contributed to NITRD and OSTP led white house workshops, and NSF and DOE led efforts to create and nurture research in big data and exascale computing. He is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, the John. V. Atanasoff Discovery Award from Iowa State University, and the Outstanding Senior Faculty Research Award, Dean’s award for faculty excellence, and the Outstanding Research Program Development Award at Georgia Tech. He is a Fellow of AAAS, IEEE, and SIAM, and is a recipient of the IEEE Computer Society Golden Core and Meritorious Service awards.
Dr. Sherrill serves as IDEaS Associate Director for Research and Education, and as Director of the Center for Computational Molecular Science and Technology. He organizes bootcamps and seminars to promote education in data science, and research workshops. Dr. Sherrill is a professor with a joint appointment between the School of Chemistry and Biochemistry and the School of Computational Science and Engineering. He conducts research in the development of new methods and algorithms in computational quantum chemistry, and he uses these methods in studies of chemistry and biophysics. His group specializes in the generation of large chemical datasets to be used in machine learning and model validation. He is a Fellow of the AAAS, American Physical Society, and American Chemical Society.
Renata Rawlings-Goss, IDEaS Director of Industry Engagement
Dr. Rawlings-Goss is responsible for interfacing with industry to cultivate industrial partnerships and embedded innovations labs, organizing workshops, retreats on topics relevant to emerging initiatives, job fairs, and numerous other activities. She builds bridges between industry and IRI activities. Renata brings rich experience in Big Data technology and policy from the National Science Foundation and the White House Office of Science and Technology Policy. During her tenure, she co-led inter-agency Big Data initiatives and led the formation of the National Data Science Organizers group. She is a biophysicist and her scientific work, at the University of Pennsylvania and the University of Michigan-Ann Arbor, was focused on biophysics, bioinformatics and next-generation genomics research. She also plays the lead role in managing the NSF South Big Data Innovation Hub from Georgia Tech. Dr. Rawlings-Goss has a Ph.D. in Biophysics.
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 PhD 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.
Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Tech. He co-directs Georgia Tech's MS Analytics program. He is the Director of Industry Relations of The Institute for Data Engineering and Science (IDEaS), and the Associate Director of Corporate Relations of The Center for Machine Learning. His research group bridges machine learning and visualization to synthesize scalable interactive tools for making sense of massive datasets, interpreting complex AI models, and solving real world problems in cybersecurity, human-centered AI, graph visualization and mining, and social good. His Ph.D. in Machine Learning from Carnegie Mellon University won CMU's Computer Science Dissertation Award, Honorable Mention. He received awards and grants from NSF, NIH, NASA, DARPA, Intel (Intel Outstanding Researcher), Symantec, Google, NVIDIA, IBM, Yahoo, Amazon, Microsoft, eBay, LexisNexis; Raytheon Faculty Fellowship; Edenfield Faculty Fellowship; Outstanding Junior Faculty Award; The Lester Endowment Award; Symantec fellowship (twice); Best student papers at SDM'14 and KDD'16 (runner-up); Best demo at SIGMOD'17 (runner-up); Chinese CHI'18 Best paper; ACM TiiS 2018 Best Paper, Honorable Mention. His research led to open-sourced or deployed technologies by Intel (for ISTC-ARSA: ShapeShifter, SHIELD, ADAGIO, MLsploit), Google, Facebook, Symantec (Polonium, AESOP protect 120M people from malware), and Atlanta Fire Rescue Department. His security and fraud detection research made headlines.
B. Aditya Prakash is an Associate Professor in the College of Computing at the Georgia Institute of Technology (“Georgia Tech”). He received a Ph.D. from the Computer Science Department at Carnegie Mellon University in 2012, and a B.Tech (in CS) from the Indian Institute of Technology (IIT) -- Bombay in 2007. He has published one book, more than 80 papers in major venues, holds two U.S. patents and has given four tutorials (SDM 2017, SIGKDD 2016, VLDB 2012 and ECML/PKDD 2012) at leading conferences. His work has also received a best paper award and five best-of-conference selections (AAMAS 2020, ICDM 2017, ASONAM 2013, CIKM 2012, ICDM 2012, ICDM 2011) and multiple travel awards. His research interests include Data Science, Machine Learning and AI, with emphasis on big-data problems in large real-world networks and time-series, with applications to computational epidemiology, urban computing, security and the Web. His work has been supported by the National Science Foundation (NSF), the Department of Energy (DoE), the National Security Agency (NSA), the National Endowment for Humanities (NEH) and various companies. Tools developed by his group have been in use in many places including ORNL, Walmart and Facebook. He received a Facebook Faculty Award in 2015, was named as one of ‘AI Ten to Watch’ 2017 by IEEE, and received the NSF CAREER award in 2018. He was previously on the faculty of Computer Science at Virginia Tech. He is also a member of the infectious diseases modeling MIDAS network and core-faculty at the Center for Machine Learning (ML@GT) and the Institute for Data Engineering and Science (IDEaS) at Georgia Tech. Aditya’s Twitter handle is @badityap.