Sang's research and art practice focuses on robotic and computational tools that work together with human users. His vision proposes extreme synergies between machine tools and humans, with technology essentially becoming a natural extension of our hands. This way, he challenges the fear and criticism around AI and automation that they replace human endeavors, by showing how symbiotic machines can unlock new human explorations and aesthetics. The impact of his research spans from publications in top tier HCI conferences such as CHI, TEI, and NIME, journals including Leonardo and IEEE Pervasive Computing, to design awards and art exhibitions. Several of his work were awarded the Fast Company Innovation by Design Award, and have been shown in art exhibitions at SIGGRAPH ASIA, CHI, TEI, and more. His work A Flying Pantograph was included in the Otherly Space / Knowledge exhibition at the Asia Culture Center along with some of the most prominent new media artists today. In 2014, He was an artist-in-residence at Microsoft Research Studio 99 where he created Remnance of Form - an interactive light and shadow installation. His work has received extensive media coverage from BBC, WIRED, Discovery, Fast Company and so on, and he was invited to national and international events including Sebasi+Pan, TEDx events, Seoul Digital Forum, and more. He is starting at Georgia Tech Industrial Design as an assistant professor. He has helped Artmatr in the development of a machine painting technology and its creative use through collaboration with some of today's most prominent painters. He received his Ph.D. from MIT Media Lab in 2018. Prior to that, he was a software engineer at Samsung Electronics where he led the software development of eyeCan, an open-source DIY eye-mouse designed for people with motor disability. This project became the foundation of Samsung's C-LAB. He received his Bachelor and Master of Science from KAIST, focusing on 3D Computer Vision and Machine Learning.

In 1979 Dr. Lee conducted radiation research as an undergraduate assistant at the State University of New York at Buffalo, where he modeled and simulated the nongray particulate radiation in an isothermal cylindrical medium. At MIT, he designed high-performance fluidic amplifiers and fluid signal transmission systems and investigated analytically and experimentally the effects of temperature changes on fluid power control systems for flight backup control applications. Dr. Lee began at Tech in 1985 as an Assistant Professor.

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

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.).

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.