During his graduate work at UC, Berkeley, Simon sought to uncover general principles of animal locomotion that reveal control strategies underlying the remarkable stability and maneuverability of movement in nature. His work has demonstrated the importance animals’ natural dynamics for maintaining stability in the absence of neural feedback. His research emphasizes the importance of placing neural control in the appropriate dynamical context using mathematical and physical models. He has collaborated with researchers at four other institutions to transfer these principles to the design of the next generation of bio-inspired legged robots. 

Simon received his Ph.D. in Integrative Biology at UC, Berkeley and has been a Hertz Fellow since 2002. His work has led to fellowships and awards from the National Science Foundation, the University of California, the Woods Hole Marine Biological Institute, the American Physical Society, the Society of Integrative and Comparative Biology, and the International Association of Physics Students. He is also currently affiliated the new Center for Interdisciplinary Bio-Inspiration in Education and Research (CIBER) at Berkeley.

Yue Chen is an assistant professor in the Department of Biomedical Engineering, GT/Emory. He received his Ph.D. degree in Mechanical Engineering from Vanderbilt University, M.S. in Mechanical Engineering from Hong Kong Polytechnic University, and a B.S. in Vehicle Engineering from Hunan University. His research focused on designing, modeling, and control of continuum robots and apply them in medicine.

Ashok Goel is a Professor of Computer Science in the School of Interactive Computing at Georgia Institute of Technology in Atlanta, USA. He obtained his Ph.D. from The Ohio State University. At Georgia Tech, he is also the Director of the Ph.D. Program in Human-Centered Computing, a Co-Director of the Center for Biologically Inspired Design, and a Fellow of Brook Byers Institute for Sustainable Systems. For more than thirty years, Ashok has conducted research into artificial intelligence, cognitive science and human-centered computing, with a focus on computational design, modeling and creativity. His recent work has explored design thinking, analogical thinking and systems thinking in biological inspired design (https://www.youtube.com/watch?v=wiRDQ4hr9i8), and his research is now developing virtual research assistants for modeling biological systems. Ashok teaches a popular course on knowledge-based AI as part of Georgia Tech's program on Online Masters of Science in Computer Science. He has pioneered the development of virtual teaching assistants, such as Jill Watson, for answering questions in online discussion forums (https://www.youtube.com/watch?v=WbCguICyfTA). Chronicle of Higher Education recently called virtual assistants exemplified by Jill Watson as one of the most transformative educational technologies in the digital era. Ashok is the Editor-in-Chief of AAAI's AI Magazine.

Animesh Garg is a Stephen Fleming Early Career Assistant Professor at School of Interactive Computing at Georgia Tech. He leads the People, AI, and Robotics (PAIR) research group. He is on the core faculty in the Robotics and Machine Learning programs. Animesh is also a Senior Researcher at Nvidia Research. Animesh earned a Ph.D. from UC Berkeley and was a postdoc at the Stanford AI Lab. He is on leave from the department of Computer Science at University of Toronto and CIFAR Chair position at the Vector Institute.

Garg earned his M.S. in Computer Science and Ph.D. in Operations Research from UC, Berkeley. He worked with Ken Goldberg at Berkeley AI Research (BAIR). He also worked closely with Pieter Abbeel, Alper Atamturk & UCSF Radiation Oncology. Animesh was later a postdoc at Stanford AI Lab with Fei-Fei Li and Silvio Savarese.

Garg's research vision is to build the Algorithmic Foundations for Generalizable Autonomy, that enables robots to acquire skills, at both cognitive & dexterous levels, and to seamlessly interact & collaborate with humans in novel environments. His group focuses on understanding structured inductive biases and causality on a quest for general-purpose embodied intelligence that learns from imprecise information and achieves flexibility & efficiency of human reasoning.

Karen M. Feigh is a Professor at Georgia Tech's Daniel Guggenheim School of Aerospace Engineering with a courtesy appointment in the School of Interactive Computing. As the director of the Georgia Tech Cognitive Engineering Center, she leads a research and education program focused on the computational cognitive modeling and design of cognitive work support systems and technologies to improve the performance of socio-technical systems. She is responsible for undergraduate and graduate level instruction in the areas of flight dynamics, human reliability analysis methods, human factors, human-automation interaction and cognitive engineering. Feigh has over 14 years of relevant research and design experience in fast-time air traffic simulation, ethnographic studies, airline operation control centers, synthetic vision systems for helicopters, expert systems for air traffic control towers, human extra-vehicular activities in space, and the impact of context on undersea warfighters. Recently her work has focused on human-autonomy teaming and the human experience of machine learning across a number of domains.

Feigh has served as both Co-PI and PI on a number of FAA, NIA, ONR, NSF and NASA sponsored projects. As part of her research, Feigh has published 35 scholarly papers in the field of Cognitive Engineering with primary emphasis on the aviation industry. She serves as an Associate Editor for the Journal of Cognitive Engineering and Decision Making. She previously served as the Chair to the Human Factor and Ergonomics Society’s Cognitive Engineering and Decision Making Technical Group, and on the National Research Council’s Aeronautics and Space Engineering Board (ASEB).

Dhruv Batra is an Associate Professor in the School of Interactive Computing at Georgia Tech. His research interests lie at the intersection of machine learning, computer vision, natural language processing, and AI, with a focus on developing intelligent systems that are able to concisely summarize their beliefs about the world with diverse predictions, integrate information and beliefs across different sub-components or `modules' of AI (vision, language, reasoning, dialog), and interpretable AI systems that provide explanations and justifications for why they believe what they believe. In past, he has also worked on topics such as interactive co-segmentation of large image collections, human body pose estIMaTion, action recognition, depth estIMaTion, and distributed optimization for inference and learning in probabilistic graphical models. He is a recipient of the Office of Naval Research (ONR) Young Investigator Program (YIP) award (2016), the National Science Foundation (NSF) CAREER award (2014), Army Research Office (ARO) Young Investigator Program (YIP) award (2014), Virginia Tech College of Engineering Outstanding New Assistant Professor award (2015), two Google Faculty Research Awards (2013, 2015), Amazon Academic Research award (2016), Carnegie Mellon Dean's Fellowship (2007), and several best paper awards (EMNLP 2017, ICML workshop on Visualization for Deep Learning 2016, ICCV workshop Object Understanding for Interaction 2016) and teaching commendations at Virginia Tech. His research is supported by NSF, ARO, ARL, ONR, DARPA, Amazon, Google, Microsoft, and NVIDIA. Research from his lab has been extensively covered in the media (with varying levels of accuracy) at CNN, BBC, CNBC, Bloomberg Business, The Boston Globe, MIT Technology Review, Newsweek, The Verge, New Scientist, and NPR. From 2013-2016, he was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, where he led the VT Machine Learning & Perception group and was a member of the Virginia Center for Autonomous Systems (VaCAS) and the VT Discovery Analytics Center (DAC). From 2010-2012, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed academic computer science institute located on the University of Chicago campus. He received his M.S. and Ph.D. degrees from Carnegie Mellon University in 2007 and 2010 respectively, advised by Tsuhan Chen. In past, he has held visiting positions at the Machine Learning Department at CMU, CSAIL MIT, Microsoft Research, and Facebook AI Research.

Judy Hoffman is an assistant professor in the School of Interactive Computing at Georgia Tech, a member of the Machine Learning Center, and a Diversity and Inclusion Fellow. Her research lies at the intersection of computer vision and machine learning with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness. She has received numerous awards including the Samsung AI Researcher of the Year Award (2021), the NVIDIA female leader in computer vision award (2020), AIMiner top 100 most influential scholars in Machine Learning (2020), MIT EECS Rising Star in 2015, and is a recipient of the NSF Graduate Fellowship. In addition to her research, she co-founded and continues to advise for Women in Computer Vision, an organization which provides mentorship and travel support for early-career women in the computer vision community. Prior to joining Georgia Tech, she was a research scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016 after which she completed postdocs at Stanford University (2017) and UC Berkeley (2018).

David Hu is a fluid dynamicist with expertise in the mechanics of interfaces between fluids such as air and water. He is a leading researcher in the biomechanics of animal locomotion. The study of flying, swimming and running dates back hundreds of years, and has since been shown to be an enduring and rich subject, linking areas as diverse as mechanical engineering, mathematics and neuroscience. Hu's work in this area has the potential to impact robotics research. Before robots can interact with humans, aid in minimally-invasive surgery, perform interplanetary exploration or lead search-and-rescue operations, we will need a fundamental physical understanding of how related tasks are accomplished in their biological counterparts. Hu's work in these areas has generated broad interest across the fields of engineering, biology and robotics, resulting in over 30 publications, including a number in high-impact interdisciplinary journals such as Nature, Nature Materials, Proceedings of the National Academy of Sciences as well as popular journals such as Physics Today and American Scientist. Hu is on editorial board member for Nature Scientific Reports, The Journal of Experimental Biology, and NYU Abu Dhabi's Center for Center for Creative Design of Materials. He has won the NSF CAREER award, Lockheed Inspirational Young Faculty award, and best paper awards from SAIC, Sigma Xi, ASME, as well as awards for science education such as the Pineapple Science Prize and the Ig Nobel Prize. Over the years, Hu's research has also played a role in educating the public in science and engineering. He has been an invited guest on numerous television and radio shows to discuss his research, including Good Morning America, National Public Radio, The Weather Channel, and Discovery Channel. His ant research was featured on the cover of the Washington Post in 2011. His work has also been featured in The Economist, The New York Times, National Geographic, Popular Science and Discover His laboratory appeared on 3D TV as part of a nature documentary by 3DigitalVision, "Fire ants: the invincible army," available on Netflix.

My research integrates my work in complex fluids and granular media and the biomechanics of locomotion of organisms and robots to address problems in nonequilibrium systems that involve interaction of matter with complex media. For example, how do organisms like lizards, crabs, and cockroaches cope with locomotion on complex terrestrial substrates (e.g. sand, bark, leaves, and grass). I seek to discover how biological locomotion on challenging terrain results from the nonlinear, many degree of freedom interaction of the musculoskeletal and nervous systems of organisms with materials with complex physical behavior. The study of novel biological and physical interactions with complex media can lead to the discovery of principles that govern the physics of the media. My approach is to integrate laboratory and field studies of organism biomechanics with systematic laboratory studies of physics of the substrates, as well as to create mathematical and physical (robot) models of both organism and substrate. Discovery of the principles of locomotion on such materials will enhance robot agility on such substrates