Bin Hu

Assistant Professor
Office: 301, ENGR4 (SAB2) at Sugar Land
Phone: (713)743-5366
Engineering Technology | Electrical and Computer Engineering
Cullen College of Engineering
University of Houston
14000 University Blvd.
Sugar Land, Texas 77479-0800

Dr. Bin Hu directs the Networked Autonomous and Intelligent Learning (NAIL) Lab. The NAIL Lab focuses on cutting-edge research in learning-based control, optimization, and machine learning, with applications spanning cybersecurity, autonomous robotics, human-machine automation, IoT systems, and vehicular networks. Our lab develops intelligent autonomous systems that can learn, adapt, and operate safely in complex, dynamic environments.

Learn more about our NAIL Lab team, facilities, research demonstrations, and outreach activities.

Multiple Openings

We have multiple openings for students who are interested in control, optimization and machine learning, and/or their applications in robotics and autonomous vehicles. If you are interested, please send your CV and transcript to Dr. Hu via email.

News

Sep 01, 25	The NAIL Lab has moved to new facilities at Engineering Building 4 (SAB2), Room 122, University of Houston Sugar Land campus! Check our NAIL Lab and Contact pages to visit our lab and explore our expanded research space.
Jun 30, 25	Our paper Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs has been published in IEEE Transactions on Human-Machine Systems, 2025.
Apr 20, 25	Our paper “Toward Embedded LLM-Guided Navigation and Object Detection for Aerial Robots” has been accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA) Late Breaking Session, Atlanta, USA. [Video Demo] [Poster]
Apr 20, 25	Our paper FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search has been accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA) Late Breaking Session, Atlanta, USA. [Video Demo] [Poster]
Jan 27, 25	Our paper Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods has been accepted for presentation at the 2025 IEEE International Conference on Robotics and Automation (ICRA), May 19-23, 2025, Atlanta, USA. (Acceptance Rate: 38.67%).
Jan 16, 25	Our paper Quadrotor Fault-Tolerant Control at High Speed: A Model-Based Extended State Observer for Mismatched Disturbance Rejection Approach has been published in IEEE Control System Letters and selected to present in the 2025 American Control Conference (ACC), to be held in DENVER, Colorado, USA, from July 7-10th, 2025.

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Selected Publications

IEEE THMS

Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs

Katherine R Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, and Bin Hu

IEEE Transactions on Human-Machine Systems (IEEE THMS) , 2025

📊 Graph 🔖 Bib 📕 PDF

@article{garcia2025human,
  title = {Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs},
  author = {Garcia, Katherine R and Chen, Jing and Xiao, Yanru and Mishler, Scott and Wang, Cong and Hu, Bin},
  journal = {IEEE Transactions on Human-Machine Systems},
  volume = {55},
  number = {4},
  pages = {499--507},
  year = {2025},
  doi = {10.1109/THMS.2025.3573173},
}

ICRA

Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods

Richie R Suganda^★, Tony Tran^♦, Miao Pan, Lei Fan, Qin Lin, and Bin Hu

In Proc. IEEE Int. Conf. on Robotics and Automation (Acceptance Rate: 38.67%) (ICRA) , 2025

📊 Graph 🔖 Bib 📕 PDF 🚀 Project

@inproceedings{suganda2024distributed,
  title = {Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods},
  author = {Suganda★, Richie R and Tran♦, Tony and Pan, Miao and Fan, Lei and Lin, Qin and Hu, Bin},
  booktitle = {Proc. IEEE Int. Conf. on Robotics and Automation (Acceptance Rate: 38.67%)},
  year = {2025},
  address = {Atlanta, US},
}

IEEE TAC

Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian Channels

Bin Hu, and Tua A Tamba

IEEE Transactions on Automatic Control (IEEE TAC) , 2022

📊 Graph 🔖 Bib 📕 PDF

@article{hu2022optimal,
  title = {Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian Channels},
  author = {Hu, Bin and Tamba, Tua A},
  journal = {IEEE Transactions on Automatic Control},
  year = {2022},
  publisher = {IEEE},
}

ICDCS

Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running

Cong Wang, Bin Hu, and Hongyi Wu

In 2022 IEEE International Conference on Distributed Computing Systems (Acceptance Rate: 19.9%) (ICDCS) , 2022

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@inproceedings{wang2022energy,
  title = {Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running},
  author = {Wang, Cong and Hu, Bin and Wu, Hongyi},
  booktitle = {2022 IEEE International Conference on Distributed Computing Systems (Acceptance Rate: 19.9%)},
  year = {2022},
}

AUTOM

Stochastic stability analysis for Vehicular Networked Systems with State-dependent bursty fading channels: A self-triggered approach

Bin Hu

Automatica (AUTOM) , 2021

📊 Graph 🔖 Bib 📕 PDF

@article{hu2021stochastic,
  title = {Stochastic stability analysis for Vehicular Networked Systems with State-dependent bursty fading channels: A self-triggered approach},
  author = {Hu, Bin},
  journal = {Automatica},
  volume = {123},
  pages = {109352},
  year = {2021},
  publisher = {Pergamon},
}

IEEE THMS

Automation error type and methods of communicating automation reliability affect trust and performance: An empirical study in the cyber domain

Jing Chen, Scott Mishler, and Bin Hu

IEEE Transactions on Human-Machine Systems (IEEE THMS) , 2021

📊 Graph 🔖 Bib

@article{chen2021automation,
  title = {Automation error type and methods of communicating automation reliability affect trust and performance: An empirical study in the cyber domain},
  author = {Chen, Jing and Mishler, Scott and Hu, Bin},
  journal = {IEEE Transactions on Human-Machine Systems},
  volume = {51},
  number = {5},
  pages = {463--473},
  year = {2021},
  publisher = {IEEE},
}