Machine Learning
Applying machine learning, image processing, and physics-informed models to rheology and soft matter data.
Using machine learning to identify and classify materials from their rheological signatures — treating the viscoelastic response of a material as a unique "fingerprint" for automated characterization.
Independent project · Ongoing
View on GitHubA software tool that computationally extracts raw rheological data arrays from historical PDF plots, built on image-processing pipelines and physics-informed neural networks (PINNs).
Independent project · Ongoing
View on GitHubBuilding image-processing pipelines for scientific imaging — classifying surface activity on anisotropic Janus particles from optical images, quantifying fluorescent dye penetration through polymer coatings via confocal microscopy, and extracting data arrays from scanned rheology plots.
Cross-project · Ongoing