Summary
Ozan Sener is a machine learning researcher and engineer with 16 years of experience designing sample-efficient algorithms for large-scale systems at the intersection of theory and applied research. His work spans domain generalization, multi-task and meta-learning, and active learning, with roles from PhD research at Cornell and visiting work at Stanford to postdoctoral and research scientist positions at Intel Labs. Based in Munich, he combines rigorous theoretical grounding with practical system-building, often translating theoretical insights into production-relevant methods. He has a strong signal-processing and AI background (BS/MS from METU, PhD from Cornell) and a track record of tackling transfer-learning problems that improve real-world robustness across tasks. An avid researcher, he maintains an up-to-date portfolio of publications and projects at ozansener.net that reveal a consistent focus on scalable, principled ML solutions.
15 years of coding experience
11 years of employment as a software developer
BS, Electrical and Electronics Engineering, BS, Electrical and Electronics Engineering at Middle East Technical University
Doctor of Philosophy (PhD), Artificial Intelligence, Doctor of Philosophy (PhD), Artificial Intelligence at Cornell University