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Michael Kaess is an Associate Professor with a Courtesy Appointment at Carnegie Mellon University, where he has been contributing significant research in the field of mobile robot autonomy. His work addresses the challenges faced by robots operating in controlled factory environments, focusing on how they perceive their surroundings effectively. His research emphasizes the development of efficient inference techniques that connect linear algebra concepts with probabilistic graphical models for 3D mapping and localization. Prior to his current position, Kaess was a research scientist and postdoctoral associate at the Massachusetts Institute of Technology (MIT) in John Leonard's Marine Robotics Lab. He earned his Ph.D. in Computer Science from the Georgia Institute of Technology in 2008, under the supervision of Frank Dellaert.
Admission is extremely competitive with no strict GPA cut-offs; holistic review is used.