Glen Chou joined Georgia Tech as an assistant professor with a joint appointment in the School of Cybersecurity & Privacy and the School of Aerospace Engineering in November 2024. He directs the Trustworthy Robotics Lab, which designs principled algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely, while remaining resilient to real-world failures and uncertainty. To achieve this, his research leverages control theory and machine learning, while connecting to optimization, computer vision, formal methods, planning, human-robot interaction, and statistics. Glen is interested in broad applications of autonomy, including robotic manipulation, vision-based navigation, aerospace, and large-scale cyber-physical systems more generally.

Glen is from Northern California. He holds dual B.S. degrees in EECS and ME from UC Berkeley, as well as an M.S. and Ph.D. in ECE from the University of Michigan. Prior to joining Georgia Tech, Glen was a postdoc at MIT CSAIL.

Dr. Fan Zhang received her Ph.D. in Nuclear Engineering and M.S. in Statistics from UTK in 2019. She is the recipient of the 2021 Ted Quinn Early Career Award from the American Nuclear Society and joined the Woodruff School in July, 2021. She is actively involved with multiple international collaborations on improving nuclear cybersecurity through the International Atomic Energy Agency (IAEA) and the DOE Office of International Nuclear Security (INS). Dr. Zhang’s research primarily focuses on the cybersecurity of nuclear facilities, online monitoring & fault detection using data analytics methods, instrumentation & control, and nuclear systems modeling & simulation. She has developed multiple testbeds using both simulators and physical components to investigate different aspects of cybersecurity as well as process health management.

Dr. Danfei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Tech. Dr. Xu received a B.S. in Computer Science from Columbia University in 2015 and a Ph.D. in Computer Science from Stanford University in 2021. His research goal is to enable physical autonomy in everyday human environments with minimum expert intervention. Towards this goal, his work draws equally from Robotics, Machine Learning, and Computer Vision, including topics such as imitation & reinforcement learning, representation learning, manipulation, and human-robot interaction. His current research focuses on visuomotor skill learning, structured world models for long-horizon planning, and data-driven approaches to human-robot collaboration.

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.

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.

Dr. Lonnie Parker is the Collaborative Autonomy Branch Chief in the Robotics and Autonomous Systems Division of the ATAS Laboratory at GTRI and serves as the PI for multiple programs. He has 10+ years of experience in managing DoD-sponsored projects and is focused on designing collaborative behaviors for unmanned systems in both the maritime and air domains. Prior to joining GTRI, Dr. Parker spent seven years at a NAVSEA warfare center, NUWC Division Newport, where he performed research into maritime autonomy through ONR-sponsored and internally funded efforts. Lonnie received a Ph.D. in Electrical & Computer Engineering from Georgia Tech in 2012. His research advisor was Prof. Ayanna M. Howard. Lonnie received an M.S. and B.S. in Electrical Engineering from the Rochester Institute of Technology in 2006.

Omobolanle Ogunseiju is an assistant professor in the School of Building Construction, at Georgia Tech. Omobolanle received her Ph.D. in Environmental Design and Planning, from the Department of Building Construction, at Virginia Tech.

Her research interests focus on advancing workforce development (safety, health, and well-being), and developing smart communities through the application of wearable robots and Artificial Intelligence (enabled by digital twin, cyber-physical systems, data sensing, and reality capture technologies). She is particularly interested in understanding and shaping the human–technological dynamics involved in workforce development, safety, and health, especially within the construction sector. This includes understanding the ethical concerns of automation and robotics in the construction industry.

Omobolanle is an active member of the Diversity and Inclusion Council at the College of Design. During her Ph.D. studies, Omobolanle was recognized as the outstanding doctoral candidate at the Myers Lawson school of construction, and the outstanding doctoral student in the College of Architecture and Urban Studies at Virginia Tech.

She believes that the next generation of construction engineers should be trained to serve as innovators, risk managers, and leaders that shape public policy. As such, Omobolanle believes that teaching should be based on promoting experiential learning amongst students, applying a variety of engagement techniques, and providing hierarchical learning assessments. Omobolanle developed and teaches Construction Cost Management at the School of Building Construction, Georgia Tech, and will teach and develop Construction Technology courses in the coming semesters. She had the opportunity to teach course sections and conduct laboratories in Smart Construction, Building Systems Technology, and Wireless Sensing in Construction Management as a graduate teaching assistant at Virginia Tech.

Dr. Noel is a Research Engineer II with the Aerospace and Acoustics Technologies Division in GTRI’s Aerospace, Transportation, and Advanced Systems Laboratory (ATAS). She received her B.S. and Ph.D. degrees in Mechanical Engineering from Georgia Tech in 2009 and 2018, respectively. In her doctoral work, Dr. Noel specialized in biomechanics, with a particular focus on biological adhesive mechanisms. Her work has been highlighted in media outlets like NPR, The New York Times, Science Magazine, and the Discovery Channel. Dr. Noel’s ongoing areas of research include haptic feedback for mixed reality platforms, biomechanics and bio-inspired design, and additive manufacturing.

Shreyas Kousik is an assistant professor in the George W. Woodruff School of Mechanical Engineering. Previously, Shreyas was a postdoctoral scholar at Stanford University, working in the ASL under Prof. Marco. Kousik completed a postdoc with Prof. Grace Gao in the NAV Lab. He received his Ph.D. in Mechanical Engineering at the University of Michigan, advised by Prof. Ram Vasudevan in the ROAHM Lab and received his undergraduate degree in Mechanical Engineering at Georgia Tech, advised by Prof. Antonia Antoniou.

Kousik’s research is focused on guaranteeing safety in autonomy via collision avoidance methods for robots. His lab’s goal is to translate safety in math to safety on real robots by exploring ways to model uncertainty from autonomous perception and estimation systems and ensure that these models are practical for downstream planning and control tasks

Benjamin Joffe is a Research Scientist in the Aerospace, Transportation & Advanced Systems Laboratory at the Georgia Tech Research Institute. He holds an M.S. in Computer Science from Georgia Tech. His work is at the intersection of Computer Vision, Machine Learning, and Robotics. His research interests include 3D Perception for highly-variable and deformable objects; robot learning for manipulation tasks; real-world generalization from synthetic and multi-modal data; Machine Learning for chemical sensing and biomanufacturing; Deep Learning algorithms for novel modalities and low-data scenarios.