Research-oriented. Concentrating in AI engineering and applied ML at scale.
A semester at UC Davis through TUM's exchange program. Discovered the parts of CS I'd later orient my master's around.
Foundations in systems, algorithms, and theory. Closed with a thesis at the Chair of Physics-Enhanced ML — 16% runtime gain on CoolMuc-4.
Data-Driven Selection of Algorithmic Configurations — RF + k-means + PCA driving HPC particle-simulation tuning. 16% speedup over legacy approaches.
Top 5% of exchange cohort. Algorithms, AI, systems.
TUM CS · 2021 — 2025. Thesis grade 1.7.
Selected by Deloitte — closed program for high-potential interns.
Won TUM's Scientific Computing practical, 60% faster than runner-up across 8 teams.
Competitive nomination to UC Davis based on academic standing.