• Conducted atomistic and coarse-grained level simulations to explore peptide aggregation pathways, investigate biomaterials, and assess small molecule inhibitors.• Developed Python and Bash tools for analyzing and visualizing large datasets, successfully identifying trends in simulation results.• Collaborated with experimental research teams to troubleshoot ongoing issues and discover new research opportunities.• Presented simulation results effectively at meetings, symposiums, and conferences, ensuring accessibility for diverse audiences.• Conducted literature reviews, formulated hypotheses, and designed experiments to address key research questions.• Assisted in the writing of grant proposals, contributing to funding applications and project proposals.

• Investigated the effects of small molecules on peptide aggregation related to neurodegenerative diseases using coarse-grained simulations.• Collaborating with researchers at the University of California, Santa Barbara to prepare publications based on findings.

• Explored and developed various machine learning architectures, including LSTMs, Transformers, and Neural ODEs, for virtual pharmacokinetic/pharmacodynamic (PK/PD) analysis.• Identified the optimal model to relate dose concentration to virtual patient response.

• Performed principal component analysis to identify correlations between process variables (e.g., steam amount, reel speed, refining power) and paper quality metrics (e.g., basis weight, Concora Corrugating Medium Test (CMT)).• Analyzed the effects of alkalinity, temperature, and water hardness of pulp on the presence of stickies—tacky substances from adhesives and small debris affecting paper quality.

Summary: Advanced a method for calculating particle sizes in turbid media.