Lu Gan joined the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology as an Assistant Professor in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab at Georgia Tech, and is on the core faculty of the Institute for Robotics and Intelligent Machines. Her research interests include robot perception, robot learning, and autonomous navigation. Her group explores the use of computer vision, machine learning, estimation, probabilistic inference, kinematics and dynamics to develop autonomous systems in ground, air, and space applications.

She holds a B.S. in Automation from the University of Electronic Science and Technology of China, an M.S. in Control Engineering from Beihang University, and received her M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor. Before joining Georgia Tech, she had a two-year appointment as a Postdoctoral Scholar at the Graduate Aerospace Laboratories of the California Institute of Technology and the Center for Autonomous Systems and Technologies at Caltech.

Elizabeth Qian joined the Daniel Guggenheim School in November 2022. She holds a joint appointment at Georgia Tech as Assistant Professor in the Schools of Aerospace Engineering and Computational Science and Engineering. Her interdisciplinary research develops new computational methods to enable engineering design and decision-making for complex systems. Her specialties are in developing efficient surrogate models through model reduction and scientific machine learning, and in developing multifidelity approaches to accelerate expensive computations in uncertainty quantification, optimization, and control. 

Elizabeth previously held a postdoctoral appointment as von Karman Instructor at Caltech in the Department of Computing + Mathematical Sciences. She has been the recipient of many awards, including a Caltech-wide award for teaching bestowed by the undergraduate student body, the 2020 SIAM Student Paper Prize, the Fannie and John Hertz Foundation Fellowship, and the NSF Graduate Research Fellowship. She is also an alumna of the U.S. Fulbright student program. She earned her PhD, SM, and SB degrees from the MIT Department of Aeronautics & Astronautics.

Christos Athanasiou is an Assistant Professor at Georgia Tech's Daniel Guggenheim School of Aerospace Engineering, leading the Daedalus Lab. The lab's mission is to advance science and technology in biological and man-made systems for tackling grand social and environmental challenges with a major focus on energy storage, environmental remediation, and sustainable space exploration. Christos holds a Ph.D. in Photonics from EPFL. Initially, he carried out postdoctoral research at Brown University's School of Engineering, and later jointly at Brown University and MIT Media Lab.

Dr. Walker's primary research interests lie in electric propulsion, plasma physics, and hypersonic aerodynamics/plasma interaction. He has extensive design and testing experience with Hall thrusters and ion engines. Dr. Walker has performed seminal work in Hall thruster clustering, vacuum chamber facility effects, plasma-material interactions, and electron emission from carbon nanotubes. His current research activities involve both theoretical and experimental work in advanced spacecraft propulsion systems, diagnostics (including THz time-domain spectroscopy and Thomson scattering), plasma physics, helicon plasma sources, magnetoplasmadynamic thrusters, and pulsed inductive thrusters. Dr. Walker also teaches the undergraduate Jet & Rocket Propulsion course, as well as the graduate level Rocket Propulsion, Electric Propulsion, and Gasdynamics courses.

Dr. Koki Ho is the Dutton-Ducoffe Professor, an Associate Professor, and the director of the Space Systems Optimization Group in the Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. His research focuses on developing modeling and optimization methods for rigorous space mission analysis and design. Some of his specific research interests include (1) network modeling for campaign-level space mission design; (2) optimization and probabilistic modeling for in-space logistics infrastructure design and operations; (3) design, deployment, and maintenance of mega-scale satellite constellations; and (4) sensor management for space domain awareness. His unique research connecting logistics-based modeling, optimization, systems engineering, and space applications has provided a substantial impact on modern and future space missions that involve multiple missions, multiple vehicles, and reusable infrastructure elements. Dr. Ho earned his Ph.D. at MIT and his bachelor’s and master’s degrees at the University of Tokyo. He is the recipient of the NSF CAREER Award (2020), the NASA Early Career Faculty Award (2019), the DARPA Young Faculty Award (2019), and the Luigi Napolitano Award (2015), and he is a co-author of one of the most downloaded Acta Astronautica articles. Dr. Ho served as the Chair of the AIAA Space Logistics Technical Committee in 2017-2024 and currently serves on the Steering Committee for the NASA-funded Consortium for Space Mobility and In-Space Servicing, Assembly, and Manufacturing Capabilities (COSMIC).

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.

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.

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.

Ben Emerson completed his Ph. D. in Aerospace Engineering from Georgia Tech in August, 2013. Since then, Ben has worked as a Research Engineer at the Ben T. Zinn Combustion Lab at Georgia Tech. Ben’s research portfolio includes projects on combustion instabilities, alternative fuels, and combustion system R&D with a core focus and motivation of cleaner combustion. Ben’s research primarily consists of three core competencies, which are experimental combustion system development, combustor diagnostics, and combustion theory and modeling. Ben’s combustion system development work spans a wide variety of applications, from small lab-scale burners to combustor rigs that test full-scale gas turbine combustor hardware. His combustor diagnostics work encompasses the state of the art optical diagnostic techniques for reacting flow field measurements and imaging, and aims to implement those techniques in both laboratory-scale and large-scale rig tests. Finally, Ben’s combustion theory and modeling work is geared towards analysis of experimental datasets, development of reduced-order engineering tools, and the development of a suite of hydrodynamic stability analysis tools. Together, these core competencies form the pillars of Ben’s research, which facilitates the design of cleaner-burning combustion systems.