San Francisco Bay Area
Computational scientist and molecular biologist with 20+ years of research experience applying quantitative, computational, and experimental approaches to complex biological problems. Background spans chromatin biology and enhancer regulation of gene expression, genome maintenance and homologous recombination, synthetic biology, and systems-level analysis of cellular adaptation and stress responses. Experienced in computational genomics, machine learning, scientific software development, molecular simulation, and high-performance computing, with proficiency in Python, C++, SQL/MySQL, scalable data-analysis pipelines, and reproducible scientific workflows. Skilled in quantitative and high-throughput assay design, automation, cellular engineering, and integrative analysis of large-scale biological datasets, including RNA-seq, functional genomics, and systems-level molecular data. Recognized for interdisciplinary thinking, collaborative problem solving, mentorship, and the ability to bridge molecular biology with emerging computational methodologies. Dedicated to applying rigorous science, software engineering, and data-driven approaches to translational and real-world scientific challenges.
Master of Molecular Science and Software Engineering (MSSE), UC Berkeley Master’s Student May 2025 – May 2026 • Applied machine learning, computational genomics, scientific software engineering, and high-performance computing approaches to molecular and translational biology coursework and projects. • Developed computational pipelines and interpretable machine learning models for RNA-seq analysis and cancer-associated gene program modeling using TCGA datasets. • Built scientific workflows in Python, C++, and HPC environments through coursework in parallel computing, molecular simulation, and data-intensive software engineering. • Collaborated with interdisciplinary teams spanning biology, software engineering, data science, and computational medicine, including a translational computational biology capstone project with Genialis.
• Led microbial strain engineering efforts to enhance protein expression and oxidative stress resilience in expression hosts, advancing synthetic biology projects for infant nutrition and sustainable bioproducts. • Created automation and code resources to improve scale, dimensionality, and accuracy of robotics-assisted DNA manipulation, strain transformation, and screening. • Organized and facilitated a biweekly seminar series, connecting the Davis site with outstanding speakers and topics from across the global Novonesis R&D network and the wider academic and biotech community.
• Developed quantitative bacterial biomarker assays, focusing on cell-free DNA in patient samples (saliva, CSF, and PBMCs). • Automated data processing pipelines in Python to analyze periodontal pathogen counts, amyloid levels, and dental pathology metrics, enhancing data accuracy and efficiency. • Streamlined laboratory sample management, analysis and visualization workflows, contributing to data integrity and interpretation.
Assistant Researcher in the Department of Cellular & Molecular Pharmacology, UCSF-Mission Bay (Keith Yamamoto laboratory)
• Post-doctoral fellow in the Department of Cellular & Molecular Pharmacology, UCSF-Mission Bay (Keith Yamamoto laboratory) • Investigated GR mechanisms in transcription regulation using CRISPR editing in cultured human cells and next-generation sequencing, revealing that individual GR-bound elements near dexamethasone-responsive FKBP5 are non-essential for regulation and that a glucocorticoid response element is likely a composite of multiple dispersed enhancers. • Developed Python scripts for automated analysis of CRISPR editing outcomes, improving the efficiency and accuracy of target genotype isolation. • Mentored postgraduates, undergraduates, and high school students, supporting a collaborative and training-oriented research environment.
• Post-doctoral researcher in the Department of Microbiology and Molecular Genetics (Wolf-Dietrich Heyer laboratory) • Conducted a high-throughput screen for small molecule inhibitors of human RAD54 in collaboration with the Small Molecule Discovery Center (UCSF). • Established biochemical and cell-based assays to exclude non-specific inhibitors and to identify lead molecules, converging on two inhibitory scaffolds that inhibit RAD54 by two different mechanisms. • Identified dihydroacridines as general compound class inhibitory to SWI2-SNF2 ATPases in vitro.