Chrysostomos聽Marasinou

Data Scientist

Los Angeles, California, United States
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Summary

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Chrysostomos Marasinou is a Data Scientist with eight years of experience blending machine learning, deep learning and software engineering, currently applying these skills to medical imaging and complex project-delivery data in Los Angeles. He progressed from a PhD in theoretical physics and computational quantum research to postdoctoral work at UCLA focused on individualized radiology diagnostics using deep and reinforcement learning, giving him a rare mix of mathematical rigor and applied ML. At Nodes & Links he models relational, dynamic systems to surface project risk and simulate scenarios for large infrastructure and defense clients, translating academic techniques into production impact. Comfortable communicating technical ideas publicly and collaborating across teams, he learns quickly and favors multiple modeling approaches to tackle hard problems. An understated strength is his ability to connect abstract theoretical concepts to pragmatic engineering solutions, speeding decision-making in high-stakes domains.
code8 years of coding experience
job6 years of employment as a software developer
bookBachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at University of Cyprus
bookDoctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at University of California, Los Angeles
languagesEnglish, Greek
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Stackoverflow

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1reputation
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0questions
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Github Skills (9)

computer-vision8
image-processing8
python8
algorithms7
scientific-computing5
scipy5
classification2
reinforcement-learning2
deep-reinforcement-learning2

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

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cmarasinou/WALLE-RL

Mar 2019 - May 2022

An example serving as an introduction to Reinforcement Learning
Contributions:9 pushes, 1 branch, 1 comment in 3 years 2 months
reinforcement-learningreinforcementservingdeep-reinforcement-learning
cmarasinou/cactus

Apr 2018 - Jul 2018

Contributions:73 pushes, 1 branch in 2 months
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