Ozan Sener

Munich, Bavaria, Germany
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Summary

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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.
code16 years of coding experience
job11 years of employment as a software developer
bookBS, Electrical and Electronics Engineering, BS, Electrical and Electronics Engineering at Middle East Technical University
bookDoctor of Philosophy (PhD), Artificial Intelligence, Doctor of Philosophy (PhD), Artificial Intelligence at Cornell University
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Stackoverflow

Stats
106reputation
1kreached
3answers
0questions
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Github Skills (42)

multi-task-learning10
multi-objective-optimization10
convolutional-neural-networks9
pathfinding6
database6
react6
data-mining6
opencv6
inference6
machine-learning6
expectation-maximization6
java6
graph-algorithms6
monitoring6
matlab6

Programming languages (4)

C++RustPHPPython

Github contributions (5)

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ozansener/RecipeWatch

Sep 2014 - Jan 2016

Contributions:52 commits, 1 push in 1 year 3 months
Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"
Contributions:2 reviews, 5 commits, 3 PRs in 2 years 2 months
multi-objective-optimizationmulti-task-learning
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