PROJECTS

A Python/Dash web app for exploring aquatic biodiversity, combining species data with depth and size visualizations, taxonomic trees, and media (images and audio), with API calls to external sources. It is meant as a scientifically grounded yet engaging tool for visualizing patterns like deep-sea gigantism

Developing end-to-end pipelines for real-time gravitational wave detection in real LIGO noise, including models, data processing, and sensitivity analyses. Also building a federated-learning approach to improve detection sensitivity across observatories while preserving data privacy.

Interactive map exploring how global music hits spread across national charts over time (spanning years 2017-2021) based on Spotify data.

Developed an Android app for CS427 Software Engineering, including user login, custom themes, personalized city lists, dynamic weather/map data, and LLM-generated weather insights. Served as both manager and developer on the project.

Developed transformer-based models for forecasting higher-order gravitational wave modes and evaluating predictive fidelity across hundreds of thousands of test waveforms, including hundreds of NR waveforms.

Exploratory ecological modeling using differential equations to simulate conditions leading to gigantism in deep-sea organisms. Includes interactive plot (simulations to follow).

Early-stage project exploring how data complexity affects uncertainties in normalizing flow models applied to astrophysical distributions. Presented at the ML for Physical Sciences Workshop at NeurIPS 2024.

Studied fermionic mass bounds in compact objects and their potential role as dark matter candidates — combining analytic models with computational estimates.

Tested and analyzed the spatial and temporal resolution of the V0+ prototype detector for the ALICE experiment at CERN. Demonstrated the detector meets design goals for timing and amplitude uniformity under various beam conditions.