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Thomas Schweizer
I am passionate about leveraging Machine Learning and Data Science to build reliable, sustainable, and ethical software that prioritizes the users' needs.
At the University of Washington, I research ways to empower developers to write better and safer code by leveraging source code analysis and machine learning techniques on the code's version history.
HIGHLIGHTS
- JUL 2024 Joined Meta as a Software Engineer, Machine Learning 🎉
- JAN 2024 Deployed automated code reviewer powered by LLM (code-review-assistant.app).
- AUG 2023 Submitted our paper titled "Evaluating Untangling Tools" to ICSE 2024.
- JUL 2023 Won the first place at the ISSTA ACM Student Research Competition for my research on evaluating commit untangling tools.
- MAR 2023 Won the Fixie.AI hackathon by building an AI agent using LLMs that draft email replies direclty in your Gmail inbox.