Presenter

Michael Kaess is an Associate Professor in the Robotics Institute at Carnegie Mellon University (CMU). His research focuses on probabilistic methods for robot perception, in particular efficient algorithms for navigation, mapping and localization. Prior to joining CMU, he was a Research Scientist and a Postdoctoral Associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). He received the Ph.D. and M.S. degrees in computer science from the Georgia Institute of Technology. In 2020 he received the inaugural Robotics Science and Systems (RSS) Test of Time Award. He was one of the two runner-ups for the 2012 Volz dissertation award for the best U.S. Ph.D. thesis in robotics and automation, and also received six finalist awards and one best paper award at IROS and ICRA. He has served as Associate Editor for both IEEE Robotics and Automation Letters and IEEE Transactions on Robotics.