Sep 2026–Present
Postdoctoral Fellow, Electrical and Computer Engineering
Clemson University · Mentor: Prof. Adam Hoover
Curriculum Vitae
Md Hasibur Rahman
Postdoctoral Fellow, Electrical and Computer Engineering, Clemson University
Research Areas
Primary: Computer vision; spatiotemporal learning; small-object detection; multi-object tracking; motion and state estimation; multimodal sensor fusion; LiDAR-based 3D localization; edge AI.
Current postdoctoral interests: Wearable sensing; multimodal time series; human activity and event detection; biomedical AI.
Academic Appointments
Aug 2022–Aug 2026
Graduate Research Assistant, Computer Science
Missouri University of Science and Technology · Advisor: Prof. Sanjay Madria
Aug 2023–Aug 2026
Graduate Teaching Assistant, Computer Science
Missouri University of Science and Technology
Feb 2018–Jul 2022
Lecturer, Computer Science and Engineering
Bangladesh University of Business and Technology
Education
2026
Ph.D. in Computer Science, GPA 4.00/4.00
Missouri University of Science and Technology · Advisor: Prof. Sanjay Madria
Dissertation: Robust Detection and Tracking of Unmanned Aerial Vehicles in Challenging Aerial Environments.
2017
B.Sc. in Computer Science and Engineering, GPA 3.74/4.00
Rajshahi University of Engineering & Technology · Class rank: 7/120
Publications
Google Scholar →STARD-Net: SpatioTemporal Attention for Robust Detection of Tiny Airborne Objects from Moving Drones
M. H. Rahman and S. Madria · ACM Transactions on Spatial Algorithms and Systems, 12(1), 1–48.
KRAfT: Kalman Residual Diffusion with Formation Awareness for UAV Swarm Tracking
M. H. Rahman and S. Madria · International Conference on Pattern Recognition (ICPR), 15–30.
TARF: Track-Aware Reliability Fusion for 3D Drone Localization and Trajectory Estimation with Sparse Multi-LiDAR Point Clouds
M. H. Rahman and S. Madria · ACM SIGSPATIAL, accepted as a full paper.
V-USDT: Vision-Based UAV Swarm Detection and Tracking by Leveraging Swarm Formation Constraints
M. H. Rahman and S. Madria · IEEE MDM, 55–61.
An Augmented Dataset for Vision-based Unmanned Aerial Vehicles Detection and Tracking
M. H. Rahman and S. Madria · IEEE AIPR, 1–8.
Predict Student’s Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques
M. H. Rahman and M. R. Islam · ICEEE, 1–4.
Teaching Experience
Bangladesh University of Business and Technology: Instructor of record for Data Structures, Operating Systems, Data Mining, Internet of Things, and Computer Architecture. Supervised B.Sc. thesis and capstone projects and helped establish the department's IoT laboratory.
Missouri S&T: Supported Big Data and MATLAB courses through laboratory instruction, grading, office hours, debugging, and student mentoring.
Courses I can teach: Computer Vision, Artificial Intelligence, Machine Learning, Data Structures, Operating Systems, Data Mining, Internet of Things, Computer Architecture.
Datasets
MVAAOD: Augmented dataset for vision-based UAV detection and tracking.
UAVSwarm-W2C: Multi-UAV tracking dataset for temporal association, identity preservation, and formation-aware tracking. Dataset / KRAfT repository
Technical Skills
Programming: Python, C, C++, Java, C#, MATLAB, SQL, Bash.
Machine learning and vision: PyTorch, TensorFlow, OpenCV, scikit-learn, NumPy, Pandas, WEKA.
Sensing and deployment: LiDAR, Kalman filtering, sensor fusion, ONNX, TensorRT, NVIDIA Jetson, Arduino, Raspberry Pi.
Systems and data: Linux, Git, Hadoop, Spark, Hive, HBase, MongoDB, MySQL.
Honors & Research Support
Top 3 Best Poster, Pathways 2026 Symposium, NextGen Precision Health, 2026.
NAIRR Pilot Resource Allocation, access to A100/H100-class GPU resources for AI experiments.
First Runner-Up, IEEE Robo-droid Championship, 2015.