Office of Naval Research (ONR) Project: Graduate Researcher on maritime autonomy (2021–25), developing early intent recognition and threat-aware decision support in naval contexts. On-water Deployment Collaborator with Huntington Ingalls Industries on autonomous naval systems.

• Developing ML models for real-time prediction of vessel behavior under sparse, noisy, and adversarial conditions.
• Collaborating on simulation-driven experimentation, dataset design, and integration of ML/DL models into maritime situational awareness frameworks.
• Contributed to pending collaborative external grant proposal (ONR and ONR Global) with my PI and Flinders University, drafting technical sections on temporal intent modeling and simulation-supported evaluation.

Assisted instruction in CS 477/677: Analysis of Algorithms, CS 422/622: Introduction to Machine Learning, and CS 491/691: LLMs and Multimodal AI, supporting student learning in algorithmic reasoning and applied ML.

• Led review sessions and held weekly office hours for conceptual guidance, problem-solving support, and individualized clarification.
• Designed and graded homework, exams, and programming tasks, delivering constructive feedback.

Responsibilities include:

• Building ICONIQ (AI-driven experience recommendations) and AIJINTSEE (marketing automation) with FastAPI, PostgreSQL/pgvector, and integrated LLM/DL pipelines.
• Leading and mentoring a 4-student team (2 M.S., 2 B.S.) on architecture, coding standards, and experiment design.
• Standing up ML orchestration, data flows, and inference endpoints for productionization.

Undergraduate courses: Structured Programming, Data Structure, Database, Digital Logic Design, Artificial Intelligence & Neural Networks, Software Engineering, Object-Oriented Analysis and Design.

• Designed lecture materials, assignments, quizzes, and examinations aligned with learning outcomes.
• Supervised project-based learning activities focusing on design patterns, testing, and documentation.
• Served as Course Coordinator, contributing to course scheduling and curriculum alignment.

Dissertation on Generative & Explainable Learning for Intent Recognition in Multi-Agent Systems. Supervised by Prof. Monica Nicolescu and cosupervised by Prof. Mircea Nicolescu. Presented 2 papers at IEEE CASE, 2 forthcoming and 1 under-review conferences/journals.

Courses included:

• Autonomous Mobile Manipulation
• Robotics for Humaity
• Stochastic Deep Learning

GPA: 3.9/4.0

Thesis on Threatmap: A Framework for Enhancing Security Awareness and Decision-Making for Naval Agents. Supervised by Prof. Monica Nicolescu and cosupervised by Prof. Mircea Nicolescu. Presented 1 papers at IEEE Conference on Games, 1 Simulation Conference and published at 3 other conferences/journals.

Courses included:

• Introduction to Machine Learning
• Fundamentals of Deep Learning
• Mass Detection in Mammograms
• Advanced Bioinformatics

GPA: 3.31/4.0

Thesis on Automated method to segment retinal blood vessels from color fundus photographs. Supervised by Prof. G M Atiqur Rahaman and co-supervised by Dr. Sajib Saha