Project Ideas for Doctor of Philosophy in Data Science, AI, and High-Dimensional Statistical Learning

Designing fair classification algorithms that preserve equity across racial and income subgroups

Scalable graph learning techniques for modeling influence and misinformation in social networks

Building interpretable deep learning models for clinical event prediction using electronic health records

Multimodal data fusion for real-time pedestrian risk detection in autonomous driving systems

Developing causal inference frameworks for policy impact evaluation with observational data

Adaptive anomaly detection in high-frequency financial market data streams

Large-scale recommendation systems using contrastive learning and graph embeddings

Training robust neural networks that generalize under distributional shift and adversarial inputs

Efficient approximate nearest neighbor search in large-scale image and video databases

Privacy-preserving federated learning architectures for hospital collaborations on rare disease modeling

Reinforcement learning for personalized education systems adapting to learner cognitive profiles

Semi-supervised learning on medical imaging datasets with limited labels and high dimensionality

Spatiotemporal modeling for urban infrastructure demand prediction using mobility and weather data

Bayesian deep generative models for synthetic tabular data generation under privacy constraints

AI-driven audit systems for detecting discriminatory patterns in public sector resource allocation

Hierarchical clustering algorithms optimized for large-scale genomic data analysis

Graph neural networks for protein–drug interaction prediction in pharmaceutical R&D pipelines

Cross-lingual document embedding methods for low-resource humanitarian text analysis

Optimizing online experimentation platforms using Thompson sampling and contextual bandits

Detection of early-stage Alzheimer’s using longitudinal speech pattern analysis and deep learning