I work at the intersection of machine learning and molecular science, focusing on building models that help make sense of complex biological data. My current projects involve developing predictive tools for peptides and designing interpretable systems that bridge the gap between raw sequences and real-world applications.
I’m interested in creating tools that are both scientifically rigorous and practically useful—models that can be deployed, not just published. My approach combines computational techniques with a deep understanding of biological systems to uncover patterns and insights that drive innovation in biotech and pharma.
Experience
Graduate Research Assistant, UT Austin (2022 – Present) - Conducting research on machine learning models for peptide encoding, natural antibiotic discovery, and bioinformatics applications.
Co-Founder, BioML Society, UT Austin (2023 – Present) - Organized industry and academic talks, led a protein engineering hackathon, and taught computational biology lectures.
Research Technician, Arizona State University (2020 – 2021) - Analyzed large protein sequence datasets and supported lab research in bacterial defense systems.
Publications
Antibacterial Microcins are Ubiquitous and Functionally Diverse Across Bacterial Communities - Nature Communications (In Review)
Programming & Data Analysis: Expert in Python, R, and Shell scripting with hands-on experience in HPC environments, virtual screening pipelines, and full-stack data science workflows.
Software & Tools: Power user of PyTorch, PyTorch Lightning, Pandas, NumPy, ggplot2, git, SLURM, LaTeX, and more — efficient across both research and production settings.
Research Methods: Applied machine learning and deep learning with a focus on transformers, statistical modeling, HMMs, and scalable bioinformatics pipelines. Strong emphasis on interpretability and impactful data visualization.
Languages: Fluent in English, conversational in Spanish — comfortable collaborating across linguistic and cultural lines.
Education
PhD in Biochemistry, The University of Texas at Austin (2022 – Present)
Graduate Certificate in Computational Life Sciences, Arizona State University (2020 – 2021)
Bachelor of Science in Biochemistry, Arizona State University (2019 – 2020)
Bachelor of Arts in English (Creative Writing), Arizona State University, Barrett Honors College (2009 – 2013)
Research Communication
Public Testimony: Texas Senate Committee on Health and Human Services, Spring 2025 - Opposition to SB 1887, a proposed ban on mRNA vaccine immunization
Returning Lecturer: UT Austin Graduate Computational Biology Course, Spring 2025 — Teaching Language Models to Speak Biology
Project Pitch: Texas Health Catalyst, Winter 2025 — A machine learning framework for early prediction of post-stroke spasticity to guide intervention strategies.
Guest Speaker: AI LIVE, UT Austin's Year of AI Showcase, Fall 2024 — Interactive Workshop: AI & Eradicating Food Insecurity
Guest Lecture: UT Austin Graduate STEM Professional Development Course, Fall 2024 — Harnessing Language Models for Chemical Analysis and Therapeutic Discovery
Invited Speaker: UT Austin Biophysics Hang-Out, Spring 2024 — Development of Computational Methods for Discovery and Selection of Therapeutic Peptides
Guest Lecture: UT Austin Graduate Computational Biology Course, Spring 2024 — Teaching Language Models to Speak Biology
Poster Presentation: Interdisciplinary Life Sciences Graduate Program Annual Retreat, Fall 2023 — Chemical Language Model for Identification of Novel Drug Analogues(Best Poster Award)
Certifications, Awards & Honors
Phi Kappa Phi Honor Society, 2023
Best Poster Award, UT ILS Retreat, 2023
CITI Program: Conflicts of Interest Certification
CITI Program: Biomedical Responsible Conduct of Research Certification
