Undergraduate Researcher | UC Merced
Presenters: Axel Muñiz Tello, Prerana Somarapu, Kathy Chau, Aizen Baidya, and Trevor Oh
Presented an overview of the Data Science Society at UC Merced to staff scientists at the Data Science Summer Institute (DSSI). The presentation covered why DSS was created, our mission to develop practical data science skills, and how the organization is structured. We discussed the skills gap faced by undergraduates interested in data science and how DSS addresses it through hands-on projects, workshops, and applied learning. This presentation was important for building stronger connections between DSS and campus research staff. It positioned DSS as a bridge between undergraduate students and applied data science efforts at UC Merced.
Presenters: Axel Muñiz Tello, Melanie Gomez-Bustos, Wesley Hur, Eddie Lizarzaburu
This research investigates the application of computer vision techniques to synthetic data for training models aimed at automating laboratory processes. By integrating simulation-based datasets with machine learning methods, we seek to enhance the reliability and scalability of automated experimental workflows. The project provided hands-on experience in data science, computational modeling, and algorithmic design, while fostering collaboration with domain experts. Through this work, we demonstrate the potential of synthetic training data to accelerate innovation in automated scientific research environments.
Presenters: Axel Muñiz Tello
This research explores disease transmission dynamics using Kinetic Monte Carlo (KMC) simulations within an agent-based SIR framework. By modeling populations on lattice structures with both restricted and unrestricted movement, it examines how spatial structure and randomness influence infection and recovery patterns. The study highlights how spatial constraints and stochastic effects shape epidemic behavior, offering insights for improving future disease control strategies.