Scholar
Behzad Khamidehi
Google Scholar ID: ubjLs70AAAAJ
University of Toronto
Citations & Impact
Citations
263
H-index
9
i10-index
9
Publications
20
Co-authors
9
list available
Publications
Resume (English only)
Academic Achievements
- 1. [Jul. 2022] Our paper 'Dynamic Resource Management for Providing QoS in Drone Delivery Systems' is accepted to IEEE International Conference on Intelligent Transportation (ITSC 2022).
- 2. [Jan. 2022] Our paper 'Reinforcement Learning-aided Safe Planning for Aerial Robots to Collect Data in Dynamic Environments' is accepted to IEEE Internet of Things Journal.
- 3. [Jun. 2021] Our paper 'Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Network of Aerial Robots' is accepted to IEEE International Conference on Intelligent Transportation (ITSC 2021).
- 4. [Dec. 2020] Our paper 'Trajectory Design for the Aerial Base Stations to Improve Cellular Network Performance' is accepted to IEEE Transactions on Vehicular Technology.
- 5. [Aug. 2020] Our paper 'Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning' is accepted to IEEE GLOBECOM 2020.
- 6. [Jul. 2020] Our paper 'No Cross-Validation Required: An Analytical Framework for Regularized Mixed-Integer Problems' is accepted to IEEE Communication Letters.
- 7. [Jan. 2020] Our paper 'A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint' is accepted to IEEE ICC 2020.
Research Experience
- 1. Path Planning and Fleet Management for UAVs
- 2. Reinforcement Learning-aided Safe Planning for Aerial Robots to Collect Data in Dynamic Environments
- 3. Dynamic Resource Management for Providing QoS in Drone Delivery Systems
- 4. Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Network of Aerial Robots
Background
- A research associate in the Electrical and Computer Engineering Department at the University of Toronto, Canada. Research interests include Fleet Management and Multi-Agent Path Planning for Autonomous Robots, Sequential Decision-Making, Reinforcement Learning, and Optimization Theory.
