Pedro Fontanarrosa

Focus on developing and enhancing Python packages tailored for modeling, inference, and design of biological systems. Responsible for creating and adapting CI/CD pipelines to streamline the software development lifecycle for computational biology, while applying and advancing machine learning techniques—including Gaussian processes, neural networks, and VAR models—and Bayesian optimization methods to improve the design and analysis of complex biological systems.

Developed Genetic Design Automation (GDA) tools and mathematical models for synthetic biology. Focused on computational methods for genetic circuit simulation and design in a collaborative, interdisciplinary setting.

Conducted independent advanced research in Biomedical Engineering focused on developing computational models and Genetic Design Automation (GDA) tools. Performed mathematical modeling and simulation to guide experimental validation.

Curated and maintained the Synthetic Biology Open Language (SBOL) standard for representing biological designs. Coordinated change proposals and supported the development of software libraries and community documentation.

Performed supervised research focused on early-stage development of computational tools for genetic circuit design. Assisted in mathematical modeling and collaborative simulation projects.

Taught chemistry to high school students and coordinated Extended Essays in chemical research.

Taught science and mathematics to primary and high school students and organized Mathematical Ingenious Olympiads.

Conducted research in genetics and ecology, including maintaining Drosophila isogenetic lines, coordinating field expeditions, managing statistical programs and databases, and mentoring lab members.