I’m a Master's student in Computer Science at ETH Zürich, specializing in Machine Intelligence and Data Management. My expertise lies at the intersection of computer science and statistics, where I apply my skills in machine learning, and big data technologies to solve real-world problems and create effective solutions.
I have practical experience in data engineering, machine learning, and forecasting, including contributions to the Darts forecasting library and developing tools for industries such as logistics and energy.
Feel free to connect if you’re interested in collaborating! I’m eager to explore new challenges, particularly in Data Science, Quantitative Finance, or Big Data Engineering roles.
Programming Languages
Python • R • C++ • Java • Typescript
Frameworks & Libraries
PyTorch • NumPy • Pandas • Optuna • Plotly • Hugging Face • tidyverse
Data Management
SQL • Spark • Hadoop • HBase • MongoDB • Neo4j
Other Tools
Git • Docker • Bash • MLOps • CI/CD • GitHub Actions • Notion • Jira
Focus Areas
Forecasting • Visualization • Explainability • Causal Inference • Natural Language Processing (NLP) • Graph Neural Networks (GNNs)
Languages
German (Native) • English (Proficient) • French (Elementary)
ETH Zürich
Master of Science in Computer Science (2022 - 2024)
Major: Machine Intelligence | Minor: Data Management
GPA: 5.46 / 6.0
ETH Zürich
Bachelor of Science in Computer Science (2018 - 2021)
Sep 2021 - Aug 2022 | Zürich, Switzerland
- Developed and implemented a route planning tool using PySpark and Palantir Foundry, optimizing logistics for a newspaper company.
- Core contributor to the Darts library, optimizing model efficiency while reviewing pull requests, suggesting enhancements, and improving documentation.
- Collaborated with clients to deliver high-impact data solutions in energy and manufacturing sectors.
Master’s Thesis (Feb 2024 - Aug 2024)
Paper (confidential - under submission) | Smartvote
Identified 11 vulnerabilities in the swiss voting advice application Smartvote with some allowing for more than 3.5x visibility gains for individual parties. Proposed 10 mitigations to significantly reduce or eliminate these vulnerabilities. Findings are being adopted in Smartvote’s redesign for the next elections.
Technologies: Python, Pandas, D-Tale, SciPy, Optuna, Plotly, LaTex, Notion
Semester Project (Sep 2023 - Dec 2023)
Report | DataComp Website
Ranked 4th out of 12 teams in the small track of the DataComp Challenge, an ML benchmark where the goal was to filter a CommonCrawl image-text dataset to train a CLIP model evaluated on 38 zero-shot downstream tasks, using a combination of cross-modality filtering and content alignment.
Technologies: Python, PyTorch Lightning, SLURM (Cluster), CLIP (Contrastive Language-Image Pretraining)
Interactive ML Project (Feb 2023 - Jul 2023)
Interactive Demo | Paper | GitHub
Developed an interactive dashboard to predict basketball game outcomes based on in-game stats and explain predictions using SHAP values. Users could modify team statistics to explore what-if scenarios.
Technologies: Python, scikit-learn, SHAP, Flask, Javascript, React, Gitlab Pipelines
Contributor (Sep 2021 - Aug 2022)
Documentation | Paper | GitHub
Core contributor to the open-source time series forecasting library Darts by Unit8. Optimized the most popular regression forecasting models by vectorizing computations achieving a speedup of up to 400x.
Technologies: Python, PyTorch, scikit-learn, Matplotlib, Git, GitHub Actions
pip install darts
Bachelor’s Thesis (Feb 2021 - Aug 2021)
Report
Developed a model that enables constant-time approximate shortest path distance queries on road networks, achieving an average mean relative error of less than 10%.
Technologies: Python, NetworkX, PyTorch Geometric, (Hyperbolic) Graph Convolutional Networks ((H)GCNs)
Winterthur, Switzerland
Surat, India
Dongguan, China
Advanced Machine Learning Projects (2023)
Achieved 1st place twice and 7th place once out of over 100 teams in practical projects for the Advanced Machine Learning course at ETH Zürich. The course was competitively graded, meaning that grades were interpolated between the passing baseline score (grade 4) and the best-performing team (grade 6).
- Tabular Regression: Predicting age from brain scans — 1st place
- Timeseries Classification: Classifying heart rhythm patterns from ECG signals — 7th place
- Video Segmentation: Segmentation of mitral valve from ECG videos — 1st place
Mathematics Kangaroo Switzerland (2015)
Ranked 40th out of 5,787 students (top 0.7%) in Switzerland’s largest mathematics competition, which tests problem-solving and analytical skills through a series of math challenges.
- Sports: Squash, badminton, gym, padel tennis, table tennis
- Interests: Coding, astrophotography, music, chess, geoguessr, learning new things
- Social Activities: Board game night with friends, enjoying good conversations over dinner