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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; scaled LLM for inference on 64 million datapoints
- Trained students to use a novel LLM system for biological chemistry Q&A tasks, adopted by faculty for recurrent use
- Constructed machine learning pipelines to predict ligand affinity toward alpha-synuclein fibrils
- Enhanced productivity by creating custom Python software packages for experimental analysis, drug discovery, and protein simulations
Selected Awards & Presentations
- NIH F31 Predoctoral Fellowship - Awarded for original proposal on amyloid polymorphism determination assay (Sept 2024)
- University of Pennsylvania Dean's Scholar - Given to 20 students for outstanding academic achievement (April 2024)
- Lectures on Generative AI in Chemistry - Seminars attended by 50+ professionals on ChatGPT in chemistry workflows (May 2025)
- Invited Lecture, Temple University - Deep learning lecture for biochemistry audience (April 2024)
Technical Skills
Programming & ML
PythonPyTorchTensorFlowHuggingFacescikit-learnLLMsTransformersDiffusion ModelsRAGFine-tuningMulti-GPU TrainingAWS
Computational Chemistry
Virtual ScreeningRosetta/PyRosettaPyMOLADMET PredictionMolecular Simulations
Wet Lab
LC-MSHPLCNMRProtein ExpressionMALDI-TOFPeptide Synthesis
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
- 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
- Li, X.; Perez, R. M.; et al. Accurate Prediction of Protein Tertiary and Quaternary Stability Using Fine-Tuned Protein Language Models. Int. J. Mol. Sci. 2025
- 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