Keyu Wang

I am a Master of Science in Data Science student at Harvard University (graduating Jan 2027). My research and engineering focus on trustworthy machine learning and AI interpretability. I currently serve as a Research Assistant at Harvard Business School and a Policy & Technical Fellow with the AI Safety Student Team (AISST).

Previously, I researched at Mila with Dr. Doina Precup, publishing a first-author paper at NeurIPS 2024 workshop on model provenance risks. In 2025, I interned at the Machine Learning and Data Science Unit at OIST and the Provable Responsible AI Lab at KAUST, where I co-led work on sycophantic behavior in LLMs and co-authored a paper on mechanistic interpretability.

Beyond academia, I’ve had two internships as a Data Scientist at Bell Canada, where I built ML solutions for business intelligence. I also worked as a Machine Learning Engineer at Moonarch, developing a document extraction pipeline that improved accuracy on large-scale startup profile analysis.

I hold a B.Sc. in Computer Science from McGill University, with a minor in Geographic Information Science. During my undergraduate, I was also supervised by Dr. Raja Sengupta (affiliations: GIScience, Agent-based modeling), working on map digitization, webmap data visualization for a historical geography prject about possible migrations taken by nobles in early imperial China.

Whether you are a student exploring ML, a collaborator curious about responsible AI, feel free to connect with me through the links below.