Optimal Transport
Wasserstein metrics, unbalanced optimal transport, Hellinger–Kantorovich geometry, and computational optimal transport.
👋 About Me
I am a mathematician and data scientist interested in computational methods that combine geometry, probability, statistics, and machine learning. My work emphasizes optimal transport and Wasserstein geometry, particularly for manifold learning, dimensionality reduction, distance recovery, clustering, and scientific data analysis.
🔬 Research Interests
Machine Learning
Geometric learning, clustering, classification, structure-preserving methods, and interpretable computational models.
Dimensionality Reduction
Manifold learning, neighbor embeddings, nonlinear representations, and visualization of complex data.
Distance-Matrix Recovery
Low-rank recovery, Nyström approximation, CUR-based methods, and efficient reconstruction from limited measurements.
Federated Learning
Privacy-aware learning, self-adaptive clients, distributed optimization, and heterogeneous data.
Scientific Data Analysis
Computational methods for imaging, healthcare, network, biological, and geospatial datasets.
📄 Featured Publications
Recovering Wasserstein Distance Matrices from Few Measurements
Proceedings of IEEE ICASSP 2026
Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
Manuscript
Empowering Clients: Self-Adaptive Federated Learning for Data Quality Challenges
IEEE EDGE 2025
💼 Academic Experience
Assistant Professor of Mathematics
University of North Texas at Dallas
Graduate Research and Teaching Assistant
The University of Texas at Arlington · 2019–2026
Lecturer
North South University, Bangladesh · 2022
Graduate Teaching Assistant
Western Kentucky University · 2015–2017
👨🏫 Teaching
My teaching interests include Linear Algebra, Calculus, Probability and Statistics, Data Science, Differential Equations, Numerical Methods, and scientific computing. I emphasize clarity, active learning, productive struggle, and meaningful connections between theory and applications.
Linear Algebra
Vector spaces, transformations, and applications
Calculus
Conceptual understanding and problem solving
Statistics
Data analysis, probability, and inference