Hi, I'm Andrea Onorato
Msc in Computer Engineering
I love solving business problems using data and AI technologies. Just finished my work at Nestlè and looking for full-time opportunities in Switzerland!
Msc in Computer Engineering
I love solving business problems using data and AI technologies. Just finished my work at Nestlè and looking for full-time opportunities in Switzerland!
Currently based in Lausanne. Just finished working at Nestlé with Data & AI and collaborating with DTU Compute Lab with Prof Bolander as an AI Researcher focusing on Multimodal LLM integration with robotics. I graduated at the Technical University of Denmark (DTU) and completed an exchange at l'École Polytechnique Fédérale de Lausanne (EPFL)
When I'm not working, you'll find me playing table tennis, soccer, piano, hiking, or exploring my passion for watches and cars.
Analyzing transformer attention in vision-language models to understand how humans and robots focus on the same objects, enabling shared (joint) attention for better interaction and object understanding.
Designed and implemented a real-time newsfeed pipeline to aggregate IT and cybersecurity articles from RSS feeds and Reddit, automatically filtering and ranking relevant incidents for professionals
Comprehensive data analysis study exploring software engineer salaries, tax implications, and savings potential across Europe, helping professionals make informed career decisions.
Full-stack web application where users can post ride offers or requests with flexible scheduling, designed for community-driven transportation sharing. (Password: losanna)
Python framework analyzing transaction costs (slippage, spread, market impact) in high-frequency trading using historical limit order book data.
A curated collection of compact Python tools for file format conversion, data extraction, automated reporting, and ETL helpers. Designed for efficiency and reusability in data engineering workflows, fast scripts that solve common data processing problems with minimal code.
Developed an end-to-end data analysis and machine learning project using the CMU Movie dataset to study factors influencing box office performance. Performed data cleaning, feature engineering, and exploratory analysis, and trained interpretable ML models to identify key drivers of commercial success. Communicated results through clear visualizations and a structured data story.
I'm always interested in new opportunities and exciting projects. Whether you have a question or just want to say hi, feel free to reach out!