Wei Xu is an associate professor in the School of Interactive Computing at the Georgia Institute of Technology. Xu received her Ph.D. in Computer Science from New York University, and her B.S. and M.S. from Tsinghua University. Her research interests are in natural language processing, machine learning, and social media. Her recent work focuses on text generation, stylistics, information extraction, robustness and controllability of machine learning models, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award, CrowdFlower AI for Everyone Award, Criteo Faculty Research Award, and Best Paper Award at COLING'18. She has also received funds from DARPA and IARPA and is part of the Machine Learning Center and NSF AI CARING Institute at Georgia Tech.

Christopher J. MacLellan is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology, where he leads the Teachable AI Lab (TAIL; https://tail.cc.gatech.edu). His work on cognitive systems aims to advance our understanding of how people teach and learn and to build AI systems that can teach and learn like people do and in ways that are compatible with people. He explores the development of computational models of learning and how these models can support the development of AI technologies, such as intelligent tutoring systems and medical decision support systems, at scale.

He also investigates how data collected about how people learn and make decisions can be leveraged to drive the development of better cognitive models and computational learning systems. Chris has been a principal investigator on multiple sponsored project awards with DARPA, the U.S. Army, ONR, and NSF. He has also received external recognition for his work, such as the 2022 EAAI Now and Future AI Educator award as well as being named on the 2021 Technical.ly RealLIST of technologists building Philadelphia’s future.  

Prior to his position at Georgia Tech, Chris was an Assistant Professor of Information Science and Computer Science (by co-appointment) at Drexel University. Before that, he completed his PhD in Human-Computer Interaction at Carnegie Mellon University's School of Computing, where he was a fellow in the Program for Interdisciplinary Education Research (PIER).

The products of his work have immediate implications for AI-powered technology development. For example, through his work with the NSF-funded AI ALOE Institute, Chris is developing tools that let teachers build AI-powered tutors by naturally teaching an AI agent rather than programming. His work also has many broader implications, such as enabling doctors to support the development of AI-powered diagnoses tools where few training examples are available (DARPA-funded POCUS project) and for creating personal assistant agents that can engage in collaborative learning to support more effective human-machine teaming (ARL-funded STRONG project).

Research Areas: 

Artificial Intelligence; Cognitive Systems; Cognitive and Learning Sciences; Human-Computer Interaction; Learning Technology.

Athanassios (Thanos) Economou is Professor at the School of Architecture in the College of Design, and Adjunct Professor at the School of Interactive Computing at Georgia Institute of Technology. Dr. Economou’s teaching and research are in the areas of shape grammars, computational design, computer-aided design and design theory, with over sixty published papers in these areas. He is the Director of the Shape Computation Lab, a research group that explores how the visual nature of shape can be formally implemented with new technologies to enable new paradigms in visual computation, design automation, creative design and digital heritage. Recent projects include the Shape Machine, a new computational technology that allows shape matching in CAD systems, and Courtsweb, the most significant visual database on Federal Courthouses, sponsored by GSA and US.Courts. Design projects from his studios at Georgia Tech have received prestigious awards in international and national architectural competitions. He has been invited to give talks, seminars, and workshops at several universities including MIT, Harvard, TU Vienna, U. Michigan, KAIST, Chiao Tung U Taiwan, Emory, Seoul National U, Cambridge U, Tsinghua U, UCLA, NTUA, U.Thessaly, U.Aegean, among others. Dr. Economou holds a Diploma in Architecture from NTUA, Athens, Greece, an M.Arch from USC, and a PhD in Architecture from UCLA.

Josiah Hester works broadly in computer engineering, with a special focus on wearable devices, edge computing, and cyber-physical systems. His Ph.D. work focused on energy harvesting and battery-free devices that failed intermittentently. He now focuses on sustainable approaches to computing, via designing health wearables, interactive devices, and large-scale sensing for conservation. 
   
His work in health is focused on increasing accessibility and lowering the burden of getting preventive and acute healthcare. In both situations, he designs low-burden, high-fidelity wearable devices that monitor aspects of physiology and behavior, and use machine learning techniques to suggest or deliver adaptive and in-situ interventions ranging from pharmacological to behavioral. 
   
His work is supported by multiple grants from the NSF, NIH, and DARPA. He was named a Sloan Fellow in Computer Science and won his NSF CAREER in 2022. He was named one of Popular Science's Brilliant Ten, won the American Indian Science and Engineering Society Most Promising Scientist/Engineer Award, and the 3M Non-tenured Faculty Award in 2021. His work has been featured in the Wall Street Journal, Scientific American, BBC, Popular Science, Communications of the ACM, and the Guinness Book of World Records, among many others.

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.

Prof. Pradalier is Associate Professor at GeorgiaTech Lorraine, the French campus of the Georgia Institute of Technology (a.k.a. GeorgiaTech) since September 2012. He defended his “Habilitation à Diriger des Recherches” (Authority to Supervise Research) in 2015 on the topic of “Autonomous Mobile Systems for Long-Term Operations in Spatio-Temporal Environments” at the National Polytechnic Institute of Toulouse (INPT). 

His objective is to extend the activity of the CNRS IRL2958 GT-CNRS towards robotics, leveraging on one side the strong robotic research inside CNRS and on the other side the collaboration potential with the Robotics and Intelligent Machines (RIM) laboratory at GTL. 

At the IRL, he is now the coordinator of the H2020 BugWright2 project, has been involved in H2020 project Flourish and PF7 project Noptilus, as well as in projects on environmental monitoring. 

From November 2007 until December 2012, Dr. Pradalier has been deputy director in the Autonomous Systems Lab at ETH Zürich. In this role, he was the technical coordinator of the V-Charge project (IP, 2010-2014) and also involved in the development of innovative robotic platforms such as autonomous boats for environment monitoring or prototype space rovers funded by the European Space Agency. He is a founding member of the ETH start-up Skybotix, within which he was responsible for software development and integration. 

From 2004 to 2007, Dr. Pradalier was a research scientist at CSIRO Australia. He was then involved in the development of software for autonomous large industrial robots and an autonomous underwater vehicle for the monitoring of the Great Barrier Reef, Australia. 

He received his Ph.D. in 2004 from the National Polytechnic Institute of Grenoble (INPG) on the topic of autonomous navigation of a small urban mobility system and he is Ingénieur from the National Engineering School for Computer Science and Applied Math in Grenoble (ENSIMAG).

Henrik I Christensen is the Qualcomm Chancellor's Chair of Robot Systems and the director of the Contextual Robotics Institute at UC San Diego, and also a Distinguished Professor of Computer Science in the Department of Computer Science and Engineering. 

Dr. Christensen was initially trained in Mechanical Engineering and worked subsequently with MAN/BW Diesel. He earned M.Sc. and Ph.D. EE degrees from Aalborg University, 1987 and 1990, respectively. 

Upon graduation, Dr. Christensen has participated in many international research projects across four continents. He has held positions at Aalborg University, Oak Ridge National Laboratory, Royal Institute of Technology and Georgia Tech before joining UC San Diego. 

Dr. Christensen does research on robotics, with a particular emphasis on a systems perspective to the problem. Solutions must have a strong theoretical basis, a corresponding well-defined implementation, and it must be evaluated in realistic settings. There is a strong emphasis on “real systems for real applications!†

Dr. Christensen has published more than 400 contributions across robotics, vision and artificial intelligence. 
Dr. Christensen served as the Founding Chairman of EURON (1999-2006) and research coordinator for ECVision (2000-2004). He has led and participated in a many of EU projects, such as VAP, CoSy, CogVis, SMART, CAMERA, EcVision, EURON, Cogniron, and Neurobotics. He served as the PI for the CCC initiative on US Robotics. He is a Co-PI on ARL DCIST RCA, TILOS, the Robotics-VO, and several projects with industry. He was awarded the Joseph Engelberger Award 2011 and also named a Boeing Supplier of the Year 2011. He is a fellow of AAAS (2013) and IEEE (2015). He was awarded an honorary doctorate in engineering (Dr. Techn. h.c.) from Aalborg University, 2014. 

Dr. Christensen has served or serves on the editorial board for many of the most prestigious journals in the field, incl. Intl. Jour. of Robotics Research (IJRR), Autonomous Robots, Robotics and Autonomous Systems (RAS), IEEE Pattern Analysis and Machine Intelligence (PAMI), and Image & Vision Computing. In addition, he serves on the editorial board of the MIT Series on Intelligent Robotics and Autonomous Agents. He was the founding co-editor-in-chief of Trends and Foundations in Robotics

Harish is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. He is also a core faculty member of Georgia Tech’s Institute for Robotics and Intelligent Machines (IRIM). His research interests span the areas of robot learning, human-robot interaction, and multi-agent systems. He directs the Structured Techniques for Algorithmic Robotics (STAR) Lab, where he and his team works on structured algorithms that help robots reliably operate and collaborate in unstructured environments alongside humans.

I am an Assistant Professor at the School of Interactive Computing in the College of Computing. I am also affiliated with the Georgia Tech Research Institute and serve as an Associate Director of ML@GT which is the machine learning center recently created at Georgia Tech. Previously I was a Research Scientist at SRI International Sarnoff in Princeton, and before that received my Ph.D. in 2010 with Professor Ron Arkin as my advisor. I lead the RobotIcs Perception and Learning (RIPL) lab. My areas of research specifically focus on the intersection of learning methods for sensor processing and robotics, developing novel machine learning algorithms and formulations towards solving some of the more difficult perception problems in these areas. I am especially interested in moving beyond supervised learning (un/semi/self-supervised and continual/lifelong learning) as well as distributed perception (multi-modal fusion, learning to incorporate information across a group of robots, etc.).