PhD Researcher – Generative AI & Molecular Dynamics
TextClassification
Used PCA, VAE, and Random Forest models to label and classify binding states and conformational phenotypes in large molecular dynamics text datasets. Focused on annotating metastable states, disorder–order transitions, and binding mechanisms in intrinsically disordered proteins using ML techniques. Participated in entity-level annotation of data spanning holo and apo forms to support AI/ML-based modeling workflows. • Built latent state labels for simulation data for AI-driven biomolecular mechanism discovery
• Classified binding spectra and conformational disorder by ML annotation
• Curated and validated simulation datasets for downstream AI training
• Used ML-based label output to refine experimental design and model outcomes
Used PCA, VAE, and Random Forest models to label and classify binding states and conformational phenotypes in large molecular dynamics text datasets. Focused on annotating metastable states, disorder–order transitions, and binding mechanisms in intrinsically disordered proteins using ML techniques. Participated in entity-level annotation of data spanning holo and apo forms to support AI/ML-based modeling workflows. • Built latent state labels for simulation data for AI-driven biomolecular mechanism discovery • Classified binding spectra and conformational disorder by ML annotation • Curated and validated simulation datasets for downstream AI training • Used ML-based label output to refine experimental design and model outcomes
2023 - Present