Operations Research / Data Science Intern at SWISS Air Lines (2025-11 – 2026-04)
- Built data pipelines to preprocess, validate, combine, and analyze large-scale operational data used for planning and decision-making.
- Developed quantitative and optimization approaches to support recovery and planning decisions in a large-scale transportation network.
- Partnered with operational stakeholders to translate analytical findings into actionable recommendations and evaluate alternative strategies.
Master's Thesis: ML-Enhanced Optimization for Supply Chain Network Design at MIT (2025-03 – 2025-09)
Intelligent Logistics Systems, Center for Transportation and Logistics
- Developed an end-to-end ML pipeline using Graph Neural Networks to predict decisions in large-scale network design problems.
- Generated and processed experimental datasets, engineered features, trained classification models, and conducted validation, benchmarking, and error analysis.
- Integrated GNN predictions into a MILP optimization pipeline, reducing solve time by 60–70% while keeping solutions within 2% of optimality on average.
Supply Chain Planner Intern at Merck KGaA (2024-09 – 2025-03)
- Analyzed demand, inventory, production, and capacity data to monitor operational KPIs, identify supply risks, and investigate root causes of performance deviations.
- Built automated analytical workflows and dashboards using Python, SAP, Palantir Foundry, and Excel, improving recurring monitoring and decision support.
- Worked with Production, Quality, Planning, Procurement, and Logistics teams to translate quantitative findings into operational actions.
Project: Social Media Sentiment & Polarization Analysis at EPFL (2024-02 – 2024-07)
- Analyzed 400K+ social media posts to investigate sentiment dynamics, political polarization, engagement patterns, and shifts in online behavior.
- Applied NLP, change-point detection, anomaly detection, and data visualization to identify temporal and behavioral patterns in large-scale user-generated data.
Project: Measuring the Impact of Cinema on U.S. Naming Trends at EPFL (2023-02 – 2023-07)
- Combined movie and U.S. baby-name datasets and developed a regression-based influence metric to quantify shifts in naming trends following movie releases.
- Used exact matching to control for observed confounders and compare naming outcomes across movies with different popularity levels and character attributes.
- Applied hypothesis testing, confidence intervals, and difference-in-differences analyses to investigate movie and character effects, communicating findings through an interactive data story.
