Md Abu Sayed

About Me

Welcome to the personal website of Md Abu Sayed, a Ph.D. candidate in Computer Science and Engineering at the University of Nevada, Reno (UNR). His research develops machine learning systems for predictive maritime autonomy, intent recognition in multi-agent systems, and cross-domain AI—spanning simulation (NavySim), threat visualization (ThreatMap), temporal deep learning, generative sequence modeling, and human–robot collaboration. His work is funded by the Office of Naval Research and collaborators include Huntington Ingalls Industries and Flinders University.

Before his Ph.D., Sayed was a Lecturer at The Millennium University, Bangladesh, teaching core and advanced computing courses. He earned his B.S. from Khulna University (thesis on retinal vessel segmentation) and M.S. from UNR (thesis on ThreatMap for naval security awareness).

Beyond academia, he serves as Council Member of the Graduate Student Association (Chair, Awards Committee), was Vice President of the International Students Club (organized Night of All Nations with 600 participants), and is Co-Lead of Google Developer Group Campus (co-organized DevFest Reno).

Feature-Aware Deep Learning for Maritime Intent Recognition

Presenting our feature-aware deep learning models for maritime intent recognition, achieving ~97% accuracy on seven maritime behaviors using only initial trajectory segments.

Nov 2, 2025

Presented two papers at IEEE CASE 2025

Presented work on early intent classification and proactive maritime threat prediction at the IEEE Conference on Automation Science and Engineering.

Aug 25, 2025

Early Intent Recognition for Maritime Domains

Two papers accepted and presented by coauthors on deep learning for maritime intent recognition—early classification of vessel intentions and proactive threat prediction using LSTMs and transformers with a sliding-window approach.

Aug 17, 2025

NavySim 2.0 accepted for IEEE Transactions on Games

Our enhanced multi-vessel simulation engine has been accepted for publication in IEEE ToG.

Feb 1, 2025

Feature-aware deep learning paper forthcoming at IEEE FMLDS 2025

Paper on feature-aware maritime intent recognition accepted for the IEEE International Conference on Future Machine Learning and Data Science.

Jan 15, 2025

ThreatMap presented at HMS 2024

Presented our maritime situational awareness framework at the International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation.

Sep 24, 2024

ThreatMap: Enhancing Security Awareness for Naval Agents

Presented ThreatMap, a framework that fuses sensor coverage, vulnerability fields, and CPA-based threat estimates for interpretable real-time maritime risk visualization.

Sep 18, 2024

NavySim presented at IEEE Conference on Games 2024

Presented our multi-vessel naval simulator at IEEE CoG in Yokohama, Japan.

Aug 28, 2024

NavySim: A Multi-Vessel Simulation Engine for Naval Domains

Presented NavySim, our Unity-based multi-vessel naval simulator with real-time threat heatmaps and HMM-based intent recognition for maritime training and research.

Aug 5, 2024

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Oct 27, 2023