Research

My research focuses on developing and applying scientific machine learning methods to model, analyze, and interpret engineering and physical systems. I combine physics-based understanding with data-driven approaches to improve predictive reliability and interpretability across multiple domains.

Research Themes

 Active Research

vWF Polymer Dynamics & Scientific Machine Learning

My primary doctoral research applies scientific ML to the conformational dynamics of von Willebrand Factor (vWF) polymer chains under shear flow. vWF undergoes a critical coil-to-stretch transition at physiological shear rates, and understanding this transition is essential for thrombosis and hemostasis modeling.

I develop latent-space representations from simulation trajectories to capture globular-to-extended conformational states, using autoencoders and dimensionality reduction to identify whether the latent geometry predicts flow-induced extension without requiring explicit conformation tracking.

vWF Polymers Shear Flow Coil-to-Stretch Latent Space Autoencoders PyTorch UMAP / t-SNE
 Methods

Scientific Machine Learning for Physical Systems

A core theme of my work is integrating physical constraints, conservation laws, and domain knowledge into machine learning frameworks to enhance model generalization and interpretability beyond purely data-driven methods.

This includes simulation-driven dataset construction, physics-informed feature engineering, and scalable computational workflows that bridge classical modeling approaches with modern learning architectures.

Physics-Informed ML Neural Networks Dimensionality Reduction Simulation-Based
 Prior Research

Energy Systems Modeling & Techno-Economic Analysis

I have conducted research on the modeling and techno-economic evaluation of distributed energy systems, with attention to rural and community-scale applications. This work involves optimal system sizing, cost modeling, and performance assessment of distributed energy assets.

The goal is to support informed design decisions that balance technical performance, economic feasibility, and long-term sustainability.

Energy Systems Techno-Economic Distributed Generation Cost Modeling
 Prior Research

Thermal & Thermochemical Systems

My research background also includes design and analysis of thermal and thermochemical systems, including biomass-based energy conversion technologies and heating systems. This work combines analytical modeling with experimental design and performance evaluation — pyrolytic system design, advanced biomass cookstoves, and feasibility analysis of ground-source heat pump systems.

Pyrolysis Biomass Heat Pump Thermal Analysis ANSYS Fluent
 Philosophy

Integration of Data, Models, and Experiments

Across all projects, I aim to integrate data-driven analysis with physics-based models and experimental insights. My research philosophy emphasizes reproducibility, interpretability, and the translation of computational results into practical engineering understanding.

Detailed outcomes are described on the Projects and Publications pages.