Zürich Metropolitan Area
I'm a Master's student in Data Science at ETH Zürich, driven by a passion for applying data to solve real-world challenges through a geographic lens. My interests lie at the intersection of geospatial analysis, statistics, urban planning, and data visualization. I’m particularly motivated by opportunities to use spatial data and analytics to inform decision-making and contribute to more sustainable, livable communities.
Digital Twin of Swiss Mobility
• Devised a methodology to adapt a Markov chain prediction model for my department’s operational context, overhauling the process of training and validating transition matrices. These matrices forecast customer charge-off rates by predicting future delinquency progression over several statements, empowering proactive risk management and reducing losses. • Exported the aforementioned methodology and workflow as a Python package for use in quarterly model monitoring cycles.
• Designed and developed interactive web applications using Mapbox GL JS and Turf.js, empowering both clients and colleagues to make data-driven decisions through easy-to-use spatial analysis tools. • Streamlined transit reach modeling and analysis efforts by developing an automation script that converts GTFS files into a Network Dataset, resulting in more efficient and accurate transit network analysis.
Excellence Research Internship Program • Collaborated with Prof. Mathias Lerch in the Urban Demography lab to conduct in-depth exploratory spatial analysis using R and various packages such as sf, raster, and ggplot, utilizing a combination of raster and microdata to analyze demographic trends over different temporal periods. • Developed a methodology to categorize and assign sets of census polygons to functional urban areas in developing countries, resulting in a more accurate representation of demographic data, to be used in a future research publication.
• Developed efficient workflows and methods using ArcGIS Pro to automate the identification and removal of extraneous line segments, reducing the runtime of water distribution model simulations and improving overall performance. • Conducted data analysis using Python to compare and validate the accuracy of predicted discharge flow against calculated discharge flow, as well as identify deviations in discharge flow during water break events. This analysis helped to improve the reliability and accuracy of the discharge flow predictions. • Coordinated with regional planners to review and process development applications to address concerns regarding water services. • Fulfilled cartographic requests, including a print and digital map of the Regional Water Network to be used across regional operations and developed a script using Python to update the map based on the regional SDE