SPEAKERS
iNVITED sPEAKERS and Panelists
kEYNOTE
Bio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988, and a Ph.D. degree from the California Institute of Technology, Pasadena, in 1992. From 1992 until 2021, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University, attaining the rank of full professor in 2001. Additionally, from 2004-2007, he served as department chair. Starting in January 2019, he moved to the National University of Singapore, where he is serving as Head of the Mechanical Engineering Department, where he has hired 14 new professors. Chirikjian’s research interests include robotics, applications of group theory in state estimation, information-theoretic inequalities, and applied mathematics more broadly. He is a 1993 National Science Foundation Young Investigator and a 1994 Presidential Faculty Fellow. In 2008 he became a fellow of the ASME and in 2010 he became a fellow of the IEEE. From 2014-15, he served as a program director for the US National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2. (2009, 2011). In 2016, an expanded edition of his 2001 book was published as a Dover book under a new title, Harmonic Analysis for Engineers and Applied Scientists.
iNVITED tALKS
Bio: Jen Jen Chung is an Associate Professor in Mechatronics within the School of Information Technology and Electrical Engineering at The University of Queensland. Her current research interests include perception, planning and learning for robotic mobile manipulation, algorithms for robot navigation through human crowds, informative path planning and adaptive sampling. Prior to working at UQ, Jen Jen was a Senior Researcher in the Autonomous Systems Lab (ASL) at ETH Zürich from 2018-2022 and was a Postdoctoral Scholar at Oregon State University researching multiagent learning methods from 2014-2017. She completed her Ph.D. on information-based exploration-exploitation strategies for autonomous soaring platforms at the Australian Centre for Field Robotics in the University of Sydney. She received her Ph.D. (2014) and B.E. (2010) from the University of Sydney.
Bio: Kostas Bekris is an Associate Professor of Computer Science at Rutgers University in New Jersey and an Amazon Scholar with the Amazon Robotics AI team since 2019. He is working in algorithmic robotics, where his group is developing algorithms for robot planning, learning and perception especially in the context of robot manipulation problems. Applications include logistics and manufacturing with a focus on taking advantage of novel soft, adaptive mechanisms. His research has been supported by NSF, DHS, DOD and NASA, including a NASA Early Career Faculty award.
Bio: Shuran Song is an Assistant Professor in the Department of Computer Science at Columbia University. Before that, she received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST. Her research interests lie at the intersection of computer vision and robotics. Song’s research has been recognized through several awards including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and finalist at RSS, ICRA, CVPR, and IROS. She is also a recipient of the NSF Career Award, Sloan Foundation fellowship as well as research awards from Microsoft, Toyota Research, Google, Amazon, and JP Morgan.
Bio: Kapil Katyal is an Applied Science manager at Amazon Robotics leading a perception team for robot manipulation. Prior to Amazon, Kapil spent 15 years at Johns Hopkins University Applied Physics Lab serving roles including a principal scientist, section supervisor and an assistant research professor. He received a B.S. degree in Computer Science from the Pennsylvania State University (University Park, PA), an M.S. degree in Electrical Engineering from Columbia University (New York, NY) and a Ph.D. degree in Computer Science from the Johns Hopkins University (Baltimore, MD).
Bio: Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, the 2022 RSS Early Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.
Academic Panelists
Kostas Bekris
Jens Kober
Gregory Chirikjian
Bio: Ian Abraham is an Assistant Professor in Mechanical Engineering at Yale University. His research group is focused on developing real-time optimal control methods for robotic search and exploration, and data-efficient learning. Previously he was a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University in the Biorobotics Lab. He received his PhD. and M.S. degrees in Mechanical Engineering from Northwestern University and the B.S. degree in Mechanical and Aerospace Engineering from Rutgers University. During his Ph.D. he also worked at the NVIDIA Seattle Robotics Lab where he worked on robust model-based control for large parameter uncertainty.
His research interest lies at the intersection of robotics, optimal control, machine learning, and artificial intelligence with a focus on active sensing and learning. His work has been featured in Robotics Weekly with his student for developing Spot Mini, an open-source, cost-effective quadrupedal robot. He is also the recipient of the 2019 King-Sun Fu IEEE Transactions on Robotics Best Paper award, the Northwestern Belytschko Outstanding Research award for his dissertation, and the NSF CAREER award.
Industry Panelists
Bio: Kapil Katyal is an Applied Science manager at Amazon Robotics leading a perception team for robot manipulation. Prior to Amazon, Kapil spent 15 years at Johns Hopkins University Applied Physics Lab serving roles including a principal scientist, section supervisor and an assistant research professor. He received a B.S. degree in Computer Science from the Pennsylvania State University (University Park, PA), an M.S. degree in Electrical Engineering from Columbia University (New York, NY) and a Ph.D. degree in Computer Science from the Johns Hopkins University (Baltimore, MD).
Bio: Shir Kozlovsky is a researcher at the Bosch Center for AI (BCAI), where her research focus is at the intersection of machine learning and robotics, including topics such as reinforcement learning, Deformable Linear Objects, computer vision, and multimodal learning for robotic manipulation.
Prior to BCAI, Shir led research in collaboration with Siemens AI and Simulation team, developing and implementing a simulation environment for robotics manipulation with integrated RL techniques for the industrial robotic environment. Shir also led an R&D team, developing an autonomous ducted fan drone (VITO) for the Israeli special defense department.
Bio: Ted Xiao is a Senior Research Engineer at Google DeepMind on the Robotics team. His work focuses on scaling robot learning, foundation models, and reinforcement learning. Prior to joining Google, Ted obtained his MS and BS in Electrical Engineering and Computer Science at UC Berkeley, where he was advised by Claire Tomlin and Sergey Levine.
