Gil Weinberg is a professor and the founding director of Georgia Tech Center for Music Technology, where he leads the Robotic Musicianship group. His research focuses on developing artificial creativity and musical expression for robots and augmented humans. Among his projects are a marimba playing robotic musician called Shimon that uses machine learning for Jazz improvisation, and a prosthetic robotic arm for amputees that restores and enhances human drumming abilities. Weinberg presented his work worldwide in venues such as The Kennedy Center, The World Economic Forum, Ars Electronica, Smithsonian Cooper-Hewitt Museum, SIGGRAPH, TED-Ed, DLD and others. His music was performed with Orchestras such as Deutsches Symphonie-Orchester Berlin, the National Irish Symphony Orchestra, and the Scottish BBC Symphony while his research has been disseminated through numerous journal articles and patents. Dr. Weinberg received his MS and Ph.D. degrees in Media Arts and Sciences from MIT and his BA from the interdisciplinary program for fostering excellence in Tel Aviv University.

Erik I. Verriest received the degree of 'Burgerlijk Electrotechnisch Ingenieur' from the State University of Ghent, Ghent, Belgium in 1973, and the M.Sc. and Ph.D. degrees from Stanford University in 1975 and 1980, respectively. He was employed by the Control Systems Laboratory and the Hybrid Computation Centre, Ghent, Belgium, where he worked on process simulation and control in 1973-74. His doctoral research at Stanford was on the algebraic theory and balancing for time varying linear systems and array algorithms. He joined the faculty of Electrical and Computer Engineering at Georgia Tech in 1980. He spent the 1991-92, 1993-94 and 1994-95 academic years at Georgia Tech Lorraine. He has contributed to the application of the theory of systems over finite fields in cryptography, data compression, sensitivity analysis of array algorithms with applications in estimation and control, algorithms for optical computing. More recently he contributed to the theory of periodic and hybrid systems, delay - differential systems, model reduction for nonlinear systems, and control with communication constraints. He served on several IPC's and is a member of the IFAC Committee on Linear Systems.

Patricio Vela was born in Mexico City, Mexico and grew up in California. He earned his bachelor of science degree in 1998 and his doctorate in 2003 at the California Institute of Technology, where he did his graduate research on geometric nonlinear control androbotics. Dr. Vela came to Georgia Tech as a post-doctoral researcher in computer vision and joined the ECE faculty in 2005. His research interests lie in the geometric perspectives to control theory and computer vision. Recently, he has been interested in the role that computer vision can play for achieving control-theoretic objectives of (semi-)autonomous systems. His research also covers control of nonlinear systems, typically robotic systems.

Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5 year degree, equivalent to a Master of Science) in Electronic and Computer Engineering from Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States of America, he studied at The University of Texas at Arlington with Frank L. Lewis as his advisor and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. From May 2011 to January 2012, he was working as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and at the Automation and Robotics Research Institute. During the period from 2012 to 2016 he was a project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. He currently serves as an Assistant Professor at The School of Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. His research interests include approxIMaTe dynamic programming, game theory, and optimal control. Recently, his research has focused on cyber-physical security, networked control, smart grid and multi-agent optimization. Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, and of several international awards including the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. He is a member of Tau Beta Pi, Eta Kappa Nu and Golden Key honor societies and is listed in Who's Who in the World, Who's Who in Science and Engineering, and Who's Who in America. He has also served on various international program committees and has organized special sessions for several international conferences. He currently is a member of the Technical Committee on Intelligent Control of the IEEE Control Systems Society (TCIC), a member of the Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning of the IEEE Computational Intelligence Society (ADPRLTC), an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Optimization Theory and Applications, an Editor in Chief of the Communications in Control Science and Engineering, a registered Electrical/Computer engineer (PE) and a member of the Technical Chamber of Greece. He is a Senior Member of IEEE.

Jun Ueda received his B.S., M.S., and Ph.D. degrees from Kyoto University, Japan, in 1994, 1996, and 2002 all in Mechanical Engineering. From 1996 to 2000, he was a Research Engineer at the Advanced Technology Research and Development Center, Mitsubishi Electric Corporation, Japan. He was an Assistant Professor of Nara Institute of Science and Technology, Japan, from 2002 to 2008. During 2005-2008, he was a visiting scholar and lecturer in the Department of Mechanical Engineering, Massachusetts Institute of Technology. He joined the G. W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology as an Assistant Professor in 2008 where he is currently a Professor. He received Fanuc FA Robot Foundation Best Paper Award in 2005, IEEE Robotics and Automation Society Early Academic Career Award in 2009, Advanced Robotics Best Paper Award in 2015, and Nagamori Award in 2021. 

Jun Ueda joined Georgia Tech in May 2008 as Assistant Professor. Before Georgia Tech, he was a Visiting Scholar and Lecturer at MIT, where he worked on the development and control of cellular actuators inspired by biological muscle. He developed compliant, large strain piezoelectric actuators and a robust control method called stochastic broadcast feedback. From 2002-2008 he was Assistant Professor at Nara Institute of Science and Technology in Japan, where he led a research group dedicated to dynamics and control in robotics, such as robot hand manipulation, tactile sensing, and power-assisting. From 1996 to 2002 and prior to obtaining his Ph.D, he worked at the Advanced Technology R&D Center of Mitsubishi Electric Corporation in Japan. Here he was involved in a variety of activities including disk drives, machine tools, and satellite tracking antennas. His Ph.D. work at Kyoto University was on the end-point control of a robot manipulator mounted on a non-rigid base. He studied feedback control robustness in terms of the coupling of the arm and base dynamics.

Maegan received her Ph.D. in Mechanical Engineering (ME) from the California Institute of Technology (Caltech) in May 2023. Prior, she also received a M.S. in ME from Caltech in 2019 and a B.S. in ME from Georgia Tech in 2017. After graduating with her Ph.D., Maegan conducted a brief postdoc at Caltech (May–August 2023), followed by a brief research position at Disney Research (September–December 2023). Generally speaking, her research interests lie at the intersection of control theory and human-robot interaction, with specific applications towards lower-limb assistive devices. Much of her research is centered around the question: “What is the right way to walk?”. In her free time, Maegan enjoys puzzles, playing video games, and the piano.

Maegan Tucker joined Georgia Tech as an assistant professor with joint appointments in the School of Electrical & Computer Engineering and the School of Mechanical Engineering in January 2024.

Craig Tovey is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He also co-directs CBID, the Georgia Tech Center for Biologically Inspired Design. 

Dr. Tovey's principal research and teaching activities are in operations research and its interdisciplinary applications to social and natural systems, with emphasis on sustainability, the environment, and energy. His current research concerns inverse optimization for electric grid management, classical and biomimetic algorithms for robots and webhosting, the behavior of animal groups, sustainability measurement, and political polarization.  

Dr. Tovey received a Presidential Young Investigator Award in 1985 and the 1989 Jacob Wolfowitz Prize for research in heuristics. He was granted a Senior Research Associateship from the National Research Council in 1990, was named an Institute Fellow at Georgia Tech in 1994, and received the Class of 1934 Outstanding Interdisciplinary Activity Award in 2011. In 2016, Dr. Tovey was recognized by the ACM Special Interest Group on Electronic Commerce with the Test of Time Award for his work as co-author of the paper “How Hard Is It to Control an Election?” He was a 2016 Golden Goose Award recipient for his role on an interdisciplinary team that studied honey bee foraging behavior which led to the development of the Honey Bee Algorithm to allocate shared webservers to internet traffic. 

Dr. Tovey received an A.B. in applied mathematics from Harvard College in 1977 and both an M.S. in computer science and a Ph.D. in operations research from Stanford University in 1981. 

I am an engineer and neuroscientist focused on how the brain and body cooperate to allow us to move. Fundamental abilities like standing and walking appear effortless until we–or someone we love–loses that ability. Movement is impacted in a wide range of diseases because it involves almost all parts of the brain and body, and their interactions with the environment. How we move is also highly individualized, changing across our lifetimes as a function of our experiences, and adapting in different situations. As such, assessing and treating movement impairments remains highly challenging. My approach is to dissect the complexities of how we move in health and disease by bridging what may seem to be disparate fields across engineering, neuroscience, and physiology. Our current application areas are Parkinson’s disease, stroke, aging and cerebral palsy, and we are interested in extending our work toward mild cognitive impairment and concussion.

My lab uses robotics, computation, and artificial intelligence to identify new physiological principles of sensing and moving that are enabling researchers to personalize rehabilitation and medicine. Primarily, we study people in the lab, studying brain and muscle activity in relationship to the body’s biomechanics in standing and walking. We use and develop robotic devices for assessing and assisting human movement, while interpreting brain and muscle activity to personalize the interactions. Our novel computer simulations of muscle, neurons, and joints establish a virtual platform for predicting how movements change in disease and improve with interventions. Recently, we have demonstrated the critical role of cognitive function motor impairment that may increase fall risk, suggesting that how we move and how we think may be closely related. Current projects include developing physiologically-inspired controllers to enable exoskeletons to enhance user balance, identifing individual differences that predict response to gait rehabilitation in stroke survivors, and developing more precise and physiologically-based methods to interpret clinical motor test outcomes.