Md Al Siam

Contact:

AI/Commumnication Lab

ECE Department

406 Luther H. Foster Hall

Tuskegee University

Tuskegee, AL 36088

📮 msiam0229@tuskegee.edu

I am a Graduate Research Assistant at Tuskegee University, pursuing an MSc in Electrical Engineering with a focus on Computer Vision and Artificial Intelligence.

Currently, I am working on Representation Learning applications in Computer Vision, collaborating with the NSF AI Institute for Artificial and Natural Intelligence, advised by Dr. Dewan Fahim Noor and Dr. Mandoye Ndoye. My work aims to advance the field of self-supervised learning for practical computer vision applications.

I earned a B.Sc. in Computer Science and Engineering from Rajshahi University of Engineering and Technology where I worked with Prof. Dr. Md Al Mehedi Hasan, Abu Sayeed, and Prof. Dr. Jungpil Shin (University of Aizu, Japan). My junior and senior year thesis and internships focused on developing efficient deep learning models for gesture and gait recognition.

With a strong foundation in both theoretical research and practical software development, I have experience spanning from academic research to industry applications. Previously, I served as a Lecturer at Northern University Bangladesh, where I taught computer science courses and coached competitive programming teams. I also worked as a Software Engineer at Samsung R&D Institute Bangladesh, focusing on AI-powered calm technology R&D, and at Enosis Solutions, where my role focused on developing full-stack business-scale web applications.

Imagine a world where good things will be fun to do only beacuse they are cool

🎓 Seeking PhD opportunities (Fall 2026) in Artificial Intelligence, Multimodal AI Systems, Computer Vision, Self-supervised Learning, Human-Computer Interaction, Healthcare AI, and Vision-Language Models.

Currently based in the US. Let's connect if there's potential alignment.

research interests

Self-Supervised and Data-Efficient Representation Learning; Robust and Trustworthy Systems; Multimodal Learning across Vision, Language, and Sensor Modalities; Human-Centered AI for Healthcare Applications

technical skills

Programming: Python, C/C++, Java, C#, SQL
AI/ML: PyTorch, TensorFlow, Keras, OpenCV
Web: Django REST Framework, JavaScript, TypeScript, ASP.NET, HTML, CSS, MSSQL
Others: Linux, LaTeX, Git/GitHub, AWS (EC2, S3), Azure, Jira

latest news

Jan 30, 2026	📝 Our paper “Layer-Wise Feature Analysis for Self-Supervised SAR Target Recognition: Identifying Optimal Representations Across Data Regimes” has been accepted for presentation at SoutheastCon 2026 (Track 4: Signal and Image Processing)!
Dec 30, 2025	🎉 Our paper “Advancing SAR Target Recognition Through Hierarchical Self-Supervised Learning with Multi-Task Pretext Training” has been published in Sensors (MDPI)! Check it out here.
Nov 18, 2025	Presented my research progress of last year at the NSF AI Institute for Artificial and Natural Intelligence Annual Retreat 2025 at Columbia University!

selected publications

Layer-Wise Feature Analysis for Self-Supervised SAR Target Recognition: Identifying Optimal Representations Across Data Regimes

Md Al Siam, Dewan Fahim Noor, and Mandoye Ndoye

In 2026 SoutheastCon, 2026

Accepted for presentation at SoutheastCon 2026 - Track 4: Signal and Image Processing

DOI Bib

@inproceedings{siam2026layerwise,
  author = {Siam, Md Al and Noor, Dewan Fahim and Ndoye, Mandoye},
  booktitle = {2026 SoutheastCon},
  title = {Layer-Wise Feature Analysis for Self-Supervised SAR Target Recognition: Identifying Optimal Representations Across Data Regimes},
  year = {2026},
  volume = {},
  number = {},
  pages = {},
  keywords = {Self-supervised learning;Synthetic aperture radar;Target recognition;Feature analysis;Layer-wise analysis;SAR ATR},
  doi = {},
  note = {Accepted for presentation at SoutheastCon 2026 - Track 4: Signal and Image Processing},
  note_show = {true}
}

Advancing SAR Target Recognition Through Hierarchical Self-Supervised Learning with Multi-Task Pretext Training

Md Al Siam, Dewan Fahim Noor, Mandoye Ndoye, and 1 more author

Sensors, 2025

DOI Bib Code

@article{siam2025advancing,
  title = {Advancing SAR Target Recognition Through Hierarchical Self-Supervised Learning with Multi-Task Pretext Training},
  author = {Siam, Md Al and Noor, Dewan Fahim and Ndoye, Mandoye and Khan, Jesmin Farzana},
  journal = {Sensors},
  volume = {26},
  number = {1},
  pages = {122},
  year = {2025},
  publisher = {MDPI},
  doi = {10.3390/s26010122},
  url = {https://www.mdpi.com/1424-8220/26/1/122},
  url_show = {true}
}

Self-Supervised Learning for SAR Target Recognition with Multi-Task Pretext Training

Md Al Siam and Dewan Fahim Noor

In SoutheastCon 2025, 2025

Selected among top 5 finalists for the best paper award

DOI Bib Code

@inproceedings{10971440,
  author = {Siam, Md Al and Noor, Dewan Fahim},
  booktitle = {SoutheastCon 2025},
  title = {Self-Supervised Learning for SAR Target Recognition with Multi-Task Pretext Training},
  year = {2025},
  volume = {},
  number = {},
  pages = {1207-1213},
  keywords = {Training;Support vector machines;Deep learning;Target recognition;Training data;Self-supervised learning;Multitasking;Data models;Synthetic aperture radar;Synthetic data;self-supervised learning;synthetic aperture radar;automatic target recognition;target classification;SAR ATR},
  doi = {10.1109/SoutheastCon56624.2025.10971440},
  url = {https://ieeexplore.ieee.org/document/10971440},
  note = {Selected among top 5 finalists for the best paper award},
  url_show = {true},
  note_show = {true}
}

Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique

F. A. Abir, M. A. Siam, A. Sayeed, and 2 more authors

Sensors, 2021

DOI Bib

@article{abir2021deep,
  title = {Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique},
  author = {Abir, F. A. and Siam, M. A. and Sayeed, A. and Hasan, M. A. M. and Shin, J.},
  journal = {Sensors},
  volume = {21},
  number = {24},
  pages = {8407},
  year = {2021},
  publisher = {MDPI},
  doi = {10.3390/s21248407},
  url = {https://www.mdpi.com/1424-8220/21/24/8407},
  url_show = {true}
}