Early Career Awards

It is our great pleasure to announce this year’s Early Career Awards. The three awardees will give live plenary keynotes with Q&A on July 14, 15, and 16, respectively. Additional live Q&As with the awardees in Eastern time zones will be held on the following day.

Byron Boots
University of Washington

Byron Boots is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He received his PhD from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University in 2012. He joined the University of Washington as a postdoctoral researcher from 2012-2014, and was an Assistant Professor in the School of Interactive Computing at Georgia Tech from 2014-2019. His group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control. His work has been applied to a range of problems including localization and mapping, motion planning, robotic manipulation, and high-speed navigation. Byron has received several awards including the 2010 ICML Best Paper Award, the 2018 AISTATS Best Paper Award, the 2019 RSS Best Student Paper Award, and the IJRR Paper of the Year Award for 2018. He is also the recipient of the NSF CAREER Award (2018), the Amazon Research Award (2019), and the Outstanding Junior Faculty Research Award from the College of Computing at Georgia Tech (2019).

Luca Carlone
Massachusetts Institute of Technology

Luca Carlone is the Charles Stark Draper Assistant Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He received his PhD from the Polytechnic University of Turin in 2012. He joined LIDS as a postdoctoral associate (2015) and later as a Research Scientist (2016), after spending two years as a postdoctoral fellow at the Georgia Institute of Technology (2013-2015). His research interests include nonlinear estimation, numerical and distributed optimization, and probabilistic inference, applied to sensing, perception, and decision-making in single and multi-robot systems. His work includes seminal results on certifiably correct algorithms for localization and mapping, as well as approaches for visual-inertial navigation and distributed mapping. He is a recipient of the 2017 Transactions on Robotics King-Sun Fu Memorial Best Paper Award, the best paper award at WAFR’16, the best Student paper award at the 2018 Symposium on VLSI Circuits, the best paper award in Robotic Vision at ICRA'20, and he was best paper finalist at RSS’15. He is also the recipient of the Google Daydream (2019) and the Amazon Research Award (2020), and the MIT AeroAstro Vickie Kerrebrock Faculty Award (2020). At MIT, he teaches “Robotics: Science and Systems,” the introduction to robotics for MIT undergraduates, and he created the graduate-level course “Visual Navigation for Autonomous Vehicles”, which covers mathematical foundations and fast C++ implementations of spatial perception algorithms for drones and autonomous vehicles.

Jeannette Bohg
Standford University

Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several awards, most notably the 2019 IEEE International Conference on Robotics and Automation (ICRA) Best Paper Award, the 2019 IEEE Robotics and Automation Society Early Career Award and the 2017 IEEE Robotics and Automation Letters (RA-L) Best Paper Award.