Resume
Summary
Accomplished computational biochemist with over 5 years of experience translating in silico experimental techniques into real-world results. Accelerated adoption of generative AI and large language models (LLMs) in biological chemistry. Harnessed machine learning (ML), simulations, and statistical analysis in collaboration with cross-disciplinary teams to unravel complex molecular mechanisms. Seeking opportunities in building novel and impactful AI systems.
Education
PhD Candidate, Computational Biochemistry
University of Pennsylvania | GPA: 3.99
Bachelor of Science, Chemistry (Minor: Biochemistry)
University of Delaware | GPA: 3.72
Research Experience
Graduate Researcher
University of Pennsylvania, Lab of Dr. E. James Petersson
- Crafted custom LLMs to achieve state-of-the-art performance on biologic aggregation and protein stability; important findings that streamline the property prediction of proteins. Scaled LLM for inference on 64 million datapoints
- Trained students to use a novel LLM system for biological chemistry questions and answer tasks which demonstrably improved learning experiences. This technology has been adopted by faculty for recurrent use in all future classes
- Enhanced productivity by the creation of Python software packages to streamline experimental analysis, drug discovery, and protein simulations which resulted in 10 coauthored publications within 5 years
Selected Awards & Presentations
- Predoctoral Individual National Research Service Award (F31) - Awarded from the National Institute of Health (NIH) for an original proposal describing and prototyping an amyloid polymorphism determination assay (September 2024)
- University of Pennsylvania Dean's Scholar - Given to 20 students in the School of Arts and Sciences for outstanding academic achievement and intellectual promise (April 2024)
- Lectures on Generative AI in Chemistry - Designed and led seminars attended by over 50 professionals and scientists on utilizing ChatGPT in chemistry workflows, leading to increased awareness and implementation of generative AI within the Chemistry Department (May 2025)
Technical Skills
Programming & ML
Computational Chemistry
Chemistry
Biology
Selected Publications
- Perez, R. M.; Shimogawa M.; et al. Large Language Models for Education: ChemTAsk -- An Open-Source Paradigm for Automated Q&A in the Graduate Classroom. Comput. Educ.: Artif. Intel. Accepted DOI
- Li, X.; Perez, R. M.; Mach, R. H.; Giannakoulias, S.; Petersson, E. J. Machine Learning Prediction of Multiple Distinct High-Affinity Chemotypes for α-Synuclein Fibrils. Chem. Commun. 2026. DOI
- Li, X.; Perez, R. M.; et al. Accurate Prediction of Protein Tertiary and Quaternary Stability Using Fine-Tuned Protein Language Models and Free Energy Perturbation. Int. J. Mol. Sci. 2025, 26, 7125. DOI
- Perez, R. M.; Li, X.; Petersson, E. J.; Giannakoulias, S. AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model. J. Chem. Inf. Model. 2023. DOI