Program Overview

The PhD program consists of 60–72 credits, depending on academic background and pace. The online learning path is divided into four phases:

Core Courses

• Advanced Research Methods in Data Science
• Statistical Learning & Inferential Modeling
• Deep Learning Architectures & Applications
• Big Data Computing & Distributed Systems
• Ethical AI, Algorithmic Fairness & Governance
• Quantitative Analysis & Computational Statistics

Electives

• Natural Language Processing
• Cognitive Systems & Human-AI Interaction
• Reinforcement Learning
• AI for Healthcare & Biomedical Analytics
• Cloud Architecture for Machine Learning Systems

Before starting dissertation work, students complete:

• Doctoral Qualifying Examination (DQE)
• Research Proposal Evaluation
• Oral Defense of Proposed Topic

Under the guidance of a doctoral supervisor, students conduct original research.

Examples of recent research topics:

• Explainable AI for mission-critical decision systems
• Bias mitigation in high-stakes machine learning models
• AI-driven climate analytics & migration prediction
• Cognitive architectures for autonomous systems
• Large-scale distributed learning algorithms

Students must:

• Publish minimum 1–2 research papers in reputable journals
• Present at a virtual or in-person conference
• Complete and defend a doctoral dissertation before the committee