Alexey Kurakin
Product Manager at Robotic Therapeutics FZ-LLC
Belgrade Metropolitan Area Serbia
Summary
🤩
Rockstar🎓
Top SchoolAlexey Kurakin is a product manager with nine years of experience building B2B and consumer-facing technology products across mobile security, imaging, and robotics, currently leading end-to-end development of a wellness robotic product from concept to pre-sale in Belgrade. He has led cross-functional teams at Samsung Knox and Canon EMEA, balancing technical oversight of software, ML and computer vision with user-centered strategy and investor-facing stakeholder management. Comfortable in both corporate and startup environments, he blends marketing roots and full-stack development training from freeCodeCamp to translate user insights into experiment-driven roadmaps. An active contributor to ML tooling, he has hands-on experience improving adversarial example tooling in the popular cleverhans project, underscoring a practical understanding of ML security and benchmarking.
9 years of coding experience
13 years of employment as a software developer
Full Stack Web Development Certification Computer Software Engineering, Full Stack Web Development Certification Computer Software Engineering at freeCodeCampBachelor's degree Marketing/Managerial Economics, Bachelor's degree Marketing/Managerial Economics at Vitebsk State Technological University
English
Stackoverflow
33reputation
202reached
1answer
3questions
Github Skills (13)
machine-learning10
benchmarking10
benchmark10
adversarial-attacks10
security10
python9
tensorflow8
angular-directives6
javascript6
qt6
angular6
serialization6
pickle6
Programming languages (3)
Github contributions (5)
cleverhans-lab/cleverhans
Jun 2017 - Jul 2019
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Role in this project:
ML Engineer
ML EngineerContributions:57 commits, 68 PRs, 31 pushes in 2 years
Contributions summary:Alexey contributed to the adversarial competition dev toolkit, likely to assist in constructing attacks, building defenses, and benchmarking. Their work included adding baseline defenses, fixing bugs in the competition code, and updating checkpoint locations. The changes involved modifications to the run scripts and example files to improve the competition framework.
benchmarkingrobustnessadversarial-machine-learningsecurityadversarial
google-research/dp-imagenet
Jan 2022 - Mar 2022
Contributions:9 commits, 4 pushes, 2 comments in 2 months
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