Yongxin Chen was born in Ganzhou, Jiangxi, China. He received his BSc in Mechanical Engineering from Shanghai Jiao Tong university, China, in 2011, and a Ph.D. degree in Mechanical Engineering, under the supervision of Tryphon Georgiou, from University of Minnesota in 2016. He is currently an Assistant Professor in the School of Aerospace Engineering at Georgia Institute of Technology. Before joining Georgia Tech, he had a one-year Research Fellowship in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center with Allen Tannenbaum from 2016.8 to 2017.8 and was an Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University from 2017.8 to 2018.8. He received the George S. Axelby Best Paper Award (IEEE Transaction on Automatic Control) in 2017 for his joint work "Optimal steering of a linear stochastic system to a final probability distribution, Part I" with Tryphon Georgiou and Michele Pavon.

Professor Yezzi was born in Gainsville, Florida and grew up in Minneapolis, Minnesota. He obtained both his Bachelor's degree and his Ph.D. in the Department of Electrical Engineering at the University of Minnesota with minors in mathematics and music. After completing his Ph.D., he continued his research as a post-Doctoral Research Associate at the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology in Boston, MA. His research interests fall broadly within the fields of image processing and computer vision. In particular he is interested in curve and surface evolution theory and partial differential equation techniques as they apply to topics within these fields (such as segmentation, image smoothing and enhancement, optical flow, stereo disparity, shape from shading, object recognition, and visual tracking). Much of Dr. Yezzi's work is particularly tailored to problems in medical imaging, including cardiac ultrasound, MRI, and CT. He joined the Georgia Tech faculty in the fall of 1999 where he has taught courses in DSP and is working to develop advanced courses in computer vision and medical image processing. Professor Yezzi consults with industry in the areas of visual inspection and medical imaging. His hobbies include classical guitar, opera, and martial arts.

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.

Sam Coogan received the B.S. degree in Electrical Engineering from Georgia Tech and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley. In 2015, he was a postdoctoral research engineer at Sensys Networks, Inc., and in 2012 he spent time at NASA's Jet Propulsion Lab. Before joining Georgia Tech in 2017, he was an assistant professor in the Electrical Engineering department at UCLA from 2015–2017. His awards and recognitions include the 2020 Donald P Eckman Award from the American Automatic Control Council recognizing "an outstanding young engineer in the field of automatic control", a Young Investigator Award from the Air Force Office of Scientific Research in 2019, a CAREER Award from the National Science Foundation in 2018, and the Outstanding paper award for the IEEE Transactions on Control of Network Systems in 2017.

Dr. Tsiotras holds the David & Andrew Lewis Endowed Chair in the School of Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He is also associate director at the Institute for Robotics and Intelligent Machines. His current research interests include nonlinear and optimal control and their connections with AI, planning, and decision-making, emphasizing autonomous ground, aerial, and space vehicles applications. He has published more than 350 journal and conference articles in these areas. Prior to joining the faculty at Georgia Tech, Dr. Tsiotras was an assistant professor of mechanical and aerospace engineering at the University of Virginia. He has also held visiting appointments with the MIT, JPL, INRIA, Rocquencourt, the Laboratoire de Automatique de Grenoble, and the Ecole des Mines de Paris (Mines ParisTech). Dr. Tsiotras is a recipient of the NSF CAREER award, the IEEE Technical Excellence Award in Aerospace Controls, the Outstanding Aerospace Engineer Award from Purdue, the Sigma Xi President and Visitor's Award for Excellence in Research, as well as numerous other fellowships and scholarships. He is currently the chief editor of the Frontiers in Robotics & AI, in the area of space robotics, and an associate editor for the Dynamic Games and Applications journal. In the past, he has served as an associate editor for the IEEE Transactions on Automatic Control, the AIAA Journal of Guidance, Control, and Dynamics, the IEEE Control Systems Magazine, and the Journal of Dynamical and Control Systems. He is a Fellow of the AIAA, IEEE, and AAS.

Evangelos Theodorou earned his Diploma in Electronic and Computer Engineering from the Technical University of Crete (TUC), Greece in 2001. He has also received a MSc in Production Engineering from TUC in 2003, a MSc in Computer Science and Engineering from University of Minnesota in spring of 2007 and a MSc in Electrical Engineering on dynamics and controls from the University of Southern California(USC) in Spring 2010. In May of 2011 he graduated with his Ph.D., in Computer Science at USC. After his Ph.D., he became a Postdoctoral Research Associate with the department of Computer Science and Engineering, University of Washington, Seattle. In July 2013 he joined the faculty of the school of Aerospace Engineering at Georgia Institute of Technology as Assistant Professor. His theoretical research spans the areas of control theory, machine learning, information theory and statistical physics. Applications involve autonomous planning and control in robotics and aerospace systems, bio-inspired control and design.

Spyros Reveliotis is a professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr. Reveliotis' research interests are primarily in discrete event systems theory and its applications, especially in the control of flexibly automated workflows and the traffic management of multi-agent systems evolving over graphs. He also has an active interest in machine learning theory and its applications. Dr. Reveliotis is an IEEE Fellow, and a member of INFORMS. Dr. Reveliotis completed his Ph.D. studies in industrial engineering at the University of Illinois at Urbana-Champaign. He also holds a B.Sc. degree in Electrical Engineering from the National Technical University of Athens, Greece, and an M.Sc. degree in Computer Systems Engineering from Northeastern University.

Dr. Ye Zhao started as an Assistant Professor at the George W. Woodruff School of Mechanical Engineering in January 2019. Previously he was a Postdoctoral Fellow at Harvard University and obtained his Ph.D. from UT Austin, where he worked on robust motion planning and decision-making for robot manipulation and locomotion problems with frictional contact behaviors. At Georgia Tech, he directs the Laboratory for Intelligent Decision and Autonomous Robots. His research interests lie broadly in planning, control, decision-making, and learning algorithms of highly agile, contact-rich, and human-cooperative robots. Dr. Zhao is especially interested in computationally efficient optimization algorithms and formal methods for challenging robotics problems with formal guarantees on robustness, safety, autonomy, and real-time performance. The LIDAR group aims at pushing the boundary of robot autonomy, intelligent decision, robust motion planning, and symbolic planning. The long-term goal is to devise theoretical and algorithmic underpinnings for collaborative humanoid and mobile robots operating in unstructured and unpredictable environments while working alongside humans. Robotic applications primarily focus on agile bipedal and quadrupedal locomotion, manipulation, heterogeneous robot teaming, and mobile platforms for extreme environment maneuvering.

Dr. Sadegh's early research work was in the field of robotics and automation. His major contribution to this field was the development of a class of adaptive and learning controllers for nonlinear mechanical systems including robotic manipulators. This work, which evolved from his doctoral research, enables a robot to learn a repetitive task through practice, much like a human being, and without requiring a precise model. He later demonstrated that implementing this learning controller can significantly improve the performance of industrial robots without significantly increasing their cost or complexity, and has the potential to improve the accuracy, autonomy, and productivity of automated manufacturing systems. In addition to robotics, he developed a similar learning controller for speed regulation of copier photoreceptors as part of a project sponsored by the Xerox Corporation. Dr. Sadegh began at Tech in 1988 as an Assistant Professor.