Mohammad Muntasir Rahman, Ph.D.
AI / ML Researcher · Computer Vision · Contactless Cardiovascular Monitoring
AI/ML researcher with 10+ years of experience developing computer vision, multimodal deep learning, and AI systems. Specialized in contactless physiological monitoring (SCG, rPPG, ECG, GCG), 3D perception, and video-based cardiovascular analytics using deep learning and physiological signal processing.
21+
Journal Articles
11+
Conference Papers
1
Patent
10+
Years Experience
About
Postdoctoral Researcher at Lehigh University working on AI for multimodal cardiovascular sensing using computer vision and deep learning. I design and deploy machine learning systems for healthcare, specializing in computer vision, deep learning, and physiological signal processing (SCG, rPPG, ECG). My focus is building real-world AI systems for contactless cardiovascular monitoring.
Technical Skills
Experience
Postdoctoral Researcher - Lehigh University, PA
Aug 2025 - Present
Developed ML systems for contactless cardiovascular monitoring using video-based SCG and rPPG.
Postdoctoral Associate - Mississippi State University, MS
Jul 2022 - Aug 2025
Developed computer vision-based seismocardiography (SCG) systems for extracting cardiac vibrations from ordinary chest videos.
Faculty in the Department of Computer Science & Engineering - Islamic University, Kushtia, Bangladesh
Jul 2019 - Jun 2022 | Apr 2010 - Aug 2015
Taught Machine Learning, Computer Vision, AI, and Image Processing courses. Supervised graduate and undergraduate research projects in applied machine learning and computer vision.
CAS-TWAS Research Fellow - Univerity of Chinese Academy of Sciences, Beijing, China
Sep 2015 - Jul 2019
Developed multi-modal deep learning framework for 3D object detection using RGB-D images.
Selected ML Projects
VisionSCG
Computer vision-based framework for extracting seismocardiographic signals from standard chest videos.
rPPG Spatial Modeling
Spatial grid-based remote photoplethysmography system for robust heart-rate estimation under motion and illumination variation.
Multimodal Fusion AI
Multimodal deep learning framework for 3D object detection.
Neonatal SCG Analysis
Signal processing pipeline for analyzing neonatal cardiac vibration patterns to extract early-stage physiological and hemodynamic insights.
Selected Publications
M. M. Rahman, and A. Taebi, (2026)
Heart Rate Monitoring From Smartphone Neck Videos Using Remote Photoplethysmography
ASME Journal of Engineering and Science in Medical Diagnostics and Therapy, 9(2): 021009
View Paper
M. M. Rahman, and A. Taebi, (2025)
Contactless heart rate and heart rate variability estimation from neck videos
47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark
M. M. Rahman, B. Kakavand, W. V. Wurm, W. L. Holman, M. R. Movahed, and A. Taebi, (2025)
From Video to Vital Signs: A New Method for Contactless Multichannel Seismocardiography
npj Cardiovascular Health 2, 1
M. M. Rahman, J. Cook, and A. Taebi, (2023)
Non-contact Heart Vibration Measurement Using Computer Vision-based Seismocardiography
Scientific Reports 13, 11787
M. M. Rahman, and A. Taebi, (2023)
Reconstruction of 3-Axis Seismocardiogram from Right-to-left and Head-to-foot Components Using A Long Short-Term Memory Network
2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA
Y. Tan, M. M. Rahman, Y. Yan, J. Xue, L. Shao, and K. Lu, (2021)
Fine-Grained Categorization From RGB-D Images
IEEE Transactions on Multimedia, vol. 24, pp. 917-928
M. M. Rahman, Y. Tan, J. Xue, L. Shao, and K. Lu, (2019)
3D object detection: Learning 3D bounding boxes from scaled down 2D bounding boxes in RGB-D images
Information Sciences, vol. 476, pp. 147-158
M. M. Rahman, Y. Tan, J. Xue, L. Shao, and K. Lu, (2017)
RGB-D Object Recognition with Multimodal Deep Convolutional Neural Networks
IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China
Contact
Email: shohan6 REMOVE_THIS[at] yahoo [dot] com
Email