Md Abu Sayed
  • Bio
  • Papers
  • Projects
  • Talks
  • News
  • Experience
  • Teaching
  • Experience
  • News & Updates
    • NavySim 2.0 accepted for IEEE Transactions on Games
    • Joining the University of Miami as a Postdoctoral Research Scholar
    • Presented two papers at IEEE CASE 2025
    • Feature-aware deep learning paper forthcoming at IEEE FMLDS 2025
    • ThreatMap presented at HMS 2024
    • NavySim presented at IEEE Conference on Games 2024
  • Projects
    • NavySim: Multi-Vessel Simulation Engine
    • Cardiovascular AI: Multimodal Imaging (IVUS/OCT & Angiography) & Agentic Decision Support
    • Joint Intent & Trajectory Prediction (MTITP GAN)
    • Maritime Trajectory Prediction & State Estimation
    • Role Shifting in Human–Robot Collaboration
    • ThreatMap: Maritime Situational Awareness
    • Intent Recognition for Maritime Autonomy
    • Breast Cancer Detection via Multimodal Fusion with GCN
    • Retinal Image Analysis & Local Haar Pattern (LHP) Descriptor
  • Publications
    • NavySim 2.0: Enhanced Multi-Vessel Simulation and Analysis Engine for Advanced Naval Research
    • Early Classification of Intentions for Maritime Domains Using Deep Learning Model
    • Feature-Aware Deep Learning for Maritime Intent Recognition
    • Proactive Maritime Threat Prediction: Vessel Intent Classification with LSTMs and Transformers Using a Sliding Window Approach
    • NavySim: A Multi-Vessel Simulation and Analysis Engine for Naval Domains
    • Keep Sailing: An Investigation of Effective Navigation Controls and Subconscious Learning in Simulated Maritime Environment
    • RAM: Resource Allocation for Multi-agent Maritime Environment
    • Threatmap: A Framework for Enhancing Security Awareness and Decision-Making for Naval Agents
    • Maritime Dynamic Resource Allocation and Risk Minimization Using Visual Analytics and Elitist Multi-Objective Optimization
    • An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods
    • A semi-supervised approach to segment retinal blood vessels in color fundus photographs
    • Retinal blood vessel segmentation: A semi-supervised approach
  • Recent & Upcoming Talks
    • Feature-Aware Deep Learning for Maritime Intent Recognition
    • Early Intent Recognition for Maritime Domains
    • ThreatMap: Enhancing Security Awareness for Naval Agents
    • NavySim: A Multi-Vessel Simulation Engine for Naval Domains
  • Teaching
    • CS 422/622: Introduction to Machine Learning
    • CS 491/691: LLMs and Multimodal AI
    • CS 477/677: Analysis of Algorithms
    • CSE 111: Structured Programming
    • CSE 121: Digital Logic Design
    • CSE 211: Data Structures
    • CSE 311: Artificial Intelligence & Neural Networks
    • CSE 313: Database Systems
    • CSE 321: Object-Oriented Analysis and Design
    • CSE 411: Software Engineering
  • Projects
  • CS 422/622: Introduction to Machine Learning
  • CS 491/691: LLMs and Multimodal AI
  • CS 477/677: Analysis of Algorithms
  • CSE 111: Structured Programming
  • CSE 121: Digital Logic Design
  • CSE 211: Data Structures
  • CSE 311: Artificial Intelligence & Neural Networks
  • CSE 313: Database Systems
  • CSE 321: Object-Oriented Analysis and Design
  • CSE 411: Software Engineering
Teaching
CSE 311: Artificial Intelligence & Neural Networks

CSE 311: Artificial Intelligence & Neural Networks

Covered uninformed/informed search, constraint solving, basic probabilistic reasoning, and feedforward/convnet basics. Created projects that paired algorithm design with experiments and written analysis.

Last updated on Jul 31, 2021
← CSE 211: Data Structures Jul 31, 2021
CSE 313: Database Systems Jul 31, 2021 →

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