Scholar
Kyle Hollins Wray
Google Scholar ID: vHYn9XIAAAAJ
Northeastern University
Citations & Impact
Citations
1,347
H-index
19
i10-index
35
Publications
20
Co-authors
31
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Recipient of the ICAPS Best Dissertation Award (2020), runner-up Victor Lesser Distinguished Dissertation Award (2020), Nissan Patent Research Award (2020 and 2018), and Outstanding Graduate Research Award (2016). Serves as an Associate Editor for the IEEE Robotics and Automation Letters (RA-L) journal, as well as an Area Chair for both the AAAI Conference on Artificial Intelligence (AAAI) and the International Joint Conference on Artificial Intelligence (IJCAI). Published multiple papers, including one on Rao-Blackwellized particle filters for online POMDP planning accepted to ICRA 2025, and two papers on multiagent semi-MDP approach to coordinate medevac helicopters and ships presented and published at IAAI 2025 and the Air Medical Journal, which won an IAAI Innovative Application Award!
Research Experience
- Currently a Research Assistant Professor at Northeastern University in the Khoury College of Computer Sciences and the Institute for Experiential Robotics, and a Vice President (VP) of Autonomy at Luminous Robotics. Previously, he was a Director of Engineering at Robust AI, where he led the fleet intelligence team and successfully raised a $20 million Series A-1 round. He was also a Principal Researcher at the Nissan Advanced Technology Center - Silicon Valley, leading the creation of autonomous vehicle decision-making systems and hybrid electric vehicle energy management systems.
Education
- Earned his doctorate in computer science from the University of Massachusetts Amherst, advised by Shlomo Zilberstein; earned bachelor's and master's degrees in computer science, engineering, and mathematics from the University of Massachusetts Amherst and The Pennsylvania State University.
Background
- Research interests include decision-making, modeling, planning, and (deep) reinforcement learning in the fields of both artificial intelligence and robotics. His work spans the design and analysis of theoretical models, the creation of efficient algorithms, and the practical applications of these decision-making approaches.
Miscellany
- Interests include creating autonomous robots that can plan, make decisions, navigate, and coordinate in the real world.
