Devesh Ranjan was named the Eugene C. Gwaltney, Jr. School Chair in the Woodruff School of Mechanical Engineering at Georgia Tech and took over the role on January 1, 2022. He previously served as the Associate Chair for Research, and Ring Family Chair in the Woodruff School. He also holds a courtesy appointment in the Daniel Guggenheim School of Aerospace Engineering and serves as a co-director of the $100M Department of Defense-funded University Consortium for Applied Hypersonics (UCAH). At Georgia Tech, Ranjan has held several leadership positions including chairing ME’s Fluid Mechanics Research Area Group (2017 - 2018), serving as ME’s Associate Chair for Research (2019-present), and as co-chair of the “Hypersonics as a System” task-force, and serving as Interim Vice-President for Interdisciplinary Research (Feb 2021-June 2021). 

Ranjan joined the faculty at Georgia Tech in 2014. Before coming to Georgia Tech, he was a director’s research fellow at Los Alamos National Laboratory (2008) and Morris E. Foster Assistant Professor in the Mechanical Engineering department at Texas A&M University (2009-2014). He earned a bachelor's degree from the NIT-Trichy (India) in 2003, and master's and Ph.D. degrees from the UW-Madison in 2005 and 2007 respectively, all in mechanical engineering. 

Ranjan’s research focuses on the interdisciplinary area of power conversion, complex fluid flows involving shock and hydrodynamic instabilities, and the turbulent mixing of materials in extreme conditions, such as supersonic and hypersonic flows. Ranjan is a Fellow of the American Society of Mechanical Engineers (ASME), and has received numerous awards for his scientific contributions, including the DOE-Early Career Award (first GT recipient), the NSF CAREER Award, and the US AFOSR Young Investigator award. He was also named the J. Erskine Love Jr. Faculty Fellow in 2015. He was invited to participate in the National Academy of Engineering’s 2016 US Frontiers in Engineering Symposium. For his educational efforts and mentorship activity, he has received CATERPILLAR Teaching Excellence Award from College of Engineering at Texas A&M, as well as 2013 TAMU ASME Professor Mentorship Award from TAMU student chapter of the ASME. At Georgia Tech, Ranjan served as a Provost’s Teaching and Learning Fellow (PTLF) from 2018-2020, and was named 2021 Governor’s Teaching Fellow. He was also named Diversity, Equity and Inclusion (DEI) Fellow for 2020-21. 

Ranjan is currently part of a 10-member Technical Screening Committee of the NAE’s COVID-19 Call for Engineering Action taskforce, an initiative to help fight the coronavirus pandemic. He currently serves on the Editorial Board of Shock Waves and was a former Associate Editor for the ASME Journal of Fluids Engineering.

Dana Randall is an American computer scientist. She works as the ADVANCE Professor of Computing, and adjunct professor of mathematics at the Georgia Institute of Technology. She is also an External Professor of the Santa Fe Institute. Previously she was executive director of the Georgia Tech Institute of Data Engineering and Science (IDEaS) that she co-founded, and director of the Algorithms and Randomness Center. Her research include combinatorics, computational aspects of statistical mechanics, Monte Carlo stimulation of Markov chains, and randomized algorithms.

Ramprasad joined the School of Materials Science and Engineering at Georgia Tech in February 2018. Prior to joining Georgia Tech, he was the Centennial Term Professor of Materials Science and Engineering at the University of Connecticut. He joined the University of Connecticut in Fall 2004 after a 6-year stint with Motorola’s R&D laboratories at Tempe, AZ. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science and Engineering at the Washington State University, and a Ph.D. degree also in Materials Science and Engineering at the University of Illinois, Urbana-Champaign.

Ramprasad’s area of expertise is in the development and utilization of computational and data-driven (machine learning) methods aimed at the design and discovery of new materials. Materials classes under study include polymers, metals and ceramics (mainly dielectrics and catalysts), and application areas include energy production and energy storage. Prof. Ramprasad’s research has been funded by the Office of Naval Research (ONR), the National Science Foundation (NSF), the Department of Energy (DOE), the Army Research Office (ARO), and Toyota Research Institute (TRI). He has lead a ONR-sponsored Multi-disciplinary University Research Initiative (MURI) in the past to accelerate the discovery of polymeric capacitor dielectrics for energy storage, and is presently leading another MURI aimed at the understanding and design of dielectrics tolerant to enormous electric fields.

Ramprasad is a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists.

Kishore Ramachandran received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 1986, and has been on the faculty of Georgia Tech since then. He led the definition of the curriculum and the implementation for an online MS program in Computer Science (OMSCS) using MOOC technology for the College of Computing, which is currently providing an opportunity for students world-wide (with an enrollment of over 10,000) to pursue a low-cost graduate education in computer science. He has served as the Director of STAR Center from 2007 to 2014, and as the Director of Korean Programs for the College of Computing from 2007 to 2011. Ramachandran has also served as the Chair of the Core Computing Division within the College of Computing. His research interests are in architectural design, programming, and analysis of parallel and distributed systems. Currently, he is leading a project that deals with large-scale situation awareness using distributed camera networks and multi-modal sensing with applications to surveillance, connected vehicles, and transportation. He is the recipient of an NSF PYI Award in 1990, the Georgia Tech doctoral thesis advisor award in 1993, the College of Computing Outstanding Senior Research Faculty award in 1996, the College of Computing Dean's Award in 2003 and 2014, the College of Computing William "Gus'' Baird Teaching Award in 2004, the "Peter A. Freeman Faculty Award" from the College of Computing in 2009 and in 2013, the Outstanding Faculty Mentor Award from the College of Computing in 2014, and became an IEEE Fellow in 2014.

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.

Peng Qiu is a professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech School of Engineering and Emory University School of Medicine. 

His research interests are in the areas of bioinformatics and computational biology, focusing on machine learning, data integration, statistical signal processing, control systems and optimization. 

In particular, he is interested in developing machine learning methods to advance single-cell data science, with applications in characterizing cellular heterogeneity, identifying cancer biomarkers, understanding disease progression, reconstructing gene regulatory networks, etc.