Ph.D. candidate in Chemical Engineering at the University of Missouri, working at the intersection of materials science, microscopy characterization, and scientific machine learning.

Data-safe public demo of an auditable TEM particle-analysis workflow for catalyst microscopy images. The repository demonstrates synthetic microscopy data generation, manifest-based provenance, scale-aware measurements, review overlays, testing, and release-safety checks.

Public materials-informatics demo for literature metadata collection, material record normalization, composition feature generation, and small regression baselines for Mn-based phosphor research.

• AI-assisted materials characterization
• Scientific image analysis for TEM/STEM and microscopy data
• Materials informatics and literature-guided data curation
• Machine learning workflows for small, noisy scientific datasets
• Research software engineering for reproducible scientific pipelines

Python, PyTorch, scikit-learn, XGBoost, pandas, NumPy, computer vision, image segmentation, materials characterization, TEM/STEM, XPS, electrochemistry, and catalyst research.