Eshed Ohn-Bar Recognized with College of Engineering Early Career Research Excellence Award
Photo by Jackie Ricciardi for BU
Hariri Institute Junior Faculty Fellow Eshed Ohn-Bar has received the Boston University College of Engineering (ENG) Early Career Research Excellence Award for 2025.
Ohn-Bar, an assistant professor of electrical and computer engineering, leads the Human-to-Everything (H2X) Lab at BU, which develops intelligent technologies with robust autonomy and real-time assistance capabilities. His research spans machine intelligence, computer vision, and systems for human-machine interaction (particularly for accessibility).
At Hariri Institute, Ohn-Bar is core faculty of the AI and Education Initiative , a cross-disciplinary initiative that uses AI to inform learning and teaching and builds use-inspired AI technologies. He is also affiliated faculty of the cross-disciplinary Artificial Intelligence Research (AIR) initiative, which aims to create intelligent systems that reliably make decisions, reason about data, and communicate with humans.
Ohn-Bar was the co-leader of the Optimal Bio-Inspired Design of Holistic Rehabilitation Systems Focused Research Program (FRP). This work advances a theory-informed software and hardware framework for adaptive systems, including intuitive wearable robots. Ohn-Bar was co-organizer of the FRP’s Reinforcement Learning Research Symposium at Hariri Institute.
Ohn-Bar was also core faculty on the Teaching Machines Human-Like Intelligence FRP. This research focused on creating convergence around foundational research in AI, and strengthened collaboration among researchers involved in cutting-edge development of AI methods and projects. Ohn-Bar was instrumental in organizing the FRP’s Bridging AI and Disability Research Symposium at Hariri Institute.
Ohn-Bar is a principal investigator on the National Science Foundation grant Towards Robust and Perceptual Inclusive Mobile Robots. This research tackles foundational advancements in benchmarks, models, and techniques for closing the perception-to-action loop in the context of inclusive navigational and mobility systems. The framework broadly engages individuals with disabilities, students, developers, and educators to produce shared tools for training the next generation of engineers concepts needed to tackle multifaceted problems at the intersection of machine learning, perception, and accessibility.
Learn more about Ohn-Bar’s work on his GitHub page here: https://eshed1.github.io/
View recorded talks from the Reinforcement Learning Research Symposium on our YouTube channel here.
View recorded talks from the Bridging AI and Disability Research Symposium on our YouTube channel here.