Bin Hu
Scholar

Bin Hu

Google Scholar ID: xWRd2kgAAAAJ
University of Houston
Safe Learning and ControlHuman-AI CollaborationCybersecurityDistributed Optimization and Control
Citations & Impact
All-time
Citations
545
 
H-index
12
 
i10-index
15
 
Publications
20
 
Co-authors
16
list available
Resume (English only)
Academic Achievements
  • - Paper 'Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs' published in IEEE Transactions on Human-Machine Systems, 2025.
  • - Paper 'Toward Embedded LLM-Guided Navigation and Object Detection for Aerial Robots' accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA) Late Breaking Session.
  • - Paper 'FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search' accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA) Late Breaking Session.
  • - Paper 'Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods' accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation.
  • - Paper 'Quadrotor Fault-Tolerant Control at High Speed: A Model-Based Extended State Observer for Mismatched Disturbance Rejection Approach' published in IEEE Control System Letters and selected to present at the 2025 American Control Conference (ACC).
Research Experience
  • Assistant Professor at Cullen College of Engineering, University of Houston, Department of Electrical and Computer Engineering; leads the NAIL Lab in conducting cutting-edge research.
Background
  • Research interests include learning-based control, optimization, and machine learning, with applications spanning cybersecurity, autonomous robotics, human-machine automation, IoT systems, and vehicular networks. Directs the Networked Autonomous and Intelligent Learning (NAIL) Lab, which focuses on developing intelligent autonomous systems capable of learning, adapting, and operating safely in complex, dynamic environments.
Miscellany
  • Welcomes students interested in control, optimization, and machine learning, and/or their applications in robotics and autonomous vehicles to join his lab.