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Prof. Dr. Abhinav Valada

Postal address:

Albert-Ludwigs-Universität Freiburg
Technische Fakultät
Robot Learning Lab
Georges-Köhler-Allee 080
D-79110 Freiburg i. Br., Germany

Office: 080-002
Phone: +49 761 203-8025
Fax: +49 761 203-8007

Abhinav Valada is an Assistant Professor and Director of the Robot Learning Lab at the University of Freiburg, Germany. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.

He received his PhD in Computer Science (summa cum laude) from the University of Freiburg working with Prof. Wolfram Burgard in the Autonomous Intelligent Systems group and he subsequently also worked as a postdoctoral research scientist. Before coming to Freiburg, he co-founded and worked as the Director of Operations of Platypus LLCfrom 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, USA. He has also previously worked at the National Robotics Engineering Center in Pittsburgh from 2013 to 2014 and at the Field Robotics Center of CMU from 2011 to 2013. He received his MS degree in Robotics from Carnegie Mellon University in 2013 and his BTech degree in Electronics and Instrumentation from VIT University in 2010.

His research lies at the intersection of robotics, machine learning and computer vision with a focus on tackling fundamental robot perception, state estimation and planning problems using learning approaches to enable robots to reliably operate in more complex domains and diverse environments. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real-world. His approach is to design deep learning algorithms that facilitate transfer of information through self-supervised multimodal and multitask learning by exploiting complementary features and cross-modal interdependencies. These techniques in turn enable robots to perceive more robustly and reason about the environment more effectively.

Abhinav Valada is an Associate Editor for the IEEE Robotics and Automation Letters, IEEE International Conference on Robotics and Automation, and IEEE/RSJ International Conference on Intelligent Robots and Systems. He regularly serves as an Area Chair and in the Program Committees of several top conferences such as Robotics: Science and Systems (RSS), Conference on Robot Learning (CoRL), AAAI Conference on Artificial Intelligence (AAAI), and European Conference on Artificial Intelligence (ECAI).

His Erdös number is at most 4.

Detailed Curriculum Vitae