Rosita Fu

Led data-driven curriculum landscape analysis for one of the nation’s largest childcare providers, assessing state-by-state alignment across 41 states. Highlighted at-risk sites and informed strategy on Pre-K and QRIS priorities for senior leadership.

I worked on predictive toxicology tools alongside computational toxicologists and software developers. - Built interactive metabolite predictor tool using existing ‘sygma’ package to predict the toxicological potential of various phthalates. - Analyzed clinical trial results reporting compliance over time. doi: 10.70542/rcj-japh-art-vr3aga/ - Scraped datasets from Chemical Effects on Biological Systems (CEBS) using API post requests - Created 500+ Anki flashcards on fossil fuels and hydrocarbon solvent toxicity in preparation for external partnership meetings. - Secured $100,000 Montgomery County Technological Innovation Grant.

I worked alongside economists and other researchers to inform evidence-based policy and evaluate program impact using statistical analysis and data visualization. Projects include working with Harris County’s Juvenile Justice System, visualizing childcare deserts, and evaluating early childhood intervention programs - Developed Python algorithms to process administrative data into analytical form, and ran simulations and regressions to evaluate effect of potential policy intervention. - Created interactive visuals, heatmaps, dashboards in Python, Holoviews/Panel, RShiny. - Designed compelling graphics in D3 and Figma. - Contributed to policy reports and shareholder presentations. - Documented code reproducibly in Github. - Maintained Linux server and resolved RShiny server issues. - Collected and documented data from a wide range of sources. - Mentored intern and tutored economics grad student.

During my undergrad years, I was an active teaching assistant for Justin Bois and Adam Wierman over the course of 6 semesters, with content focusing on the intersection of data science and computational biology, statistical methods, and networks. BE/Bi103a: Data Analysis in the Biological Sciences - Fall 2020, 2021 BE/Bi103b: Bayesian Inference in the Biological Sciences - Winter 2021, 2022 BE/Bi203: Introduction to Programming in the Biological Sciences - Summer 2022 CS144: Networks: Structure and Economics Sciences - Winter 2021

In my junior year, I interned for a biotech drug discovery startup and worked with a team of wet-lab and data scientists. - Designed and integrated a company-wide interactive dashboard for lab scientists to process the results of their experiments and inform future experimental design. This work accelerated the analysis of high-throughput photometry screens. - Analyzed large scale datasets on AWS (Pandas, Python, Dask, Scikit/Scikit Learn, Numpy). - Optimized Pandas code with time-complexity principles. - Familiarized with EC2, OOP principles, interactive plots, and computational chemistry Python packages. - Utilized Git software development practices in production-level code. - Contributed to shareholder presentation slides.