Andrey Kurenkov
PHD Student at Stanford University
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolAndrey Kurenkov is a CS PhD student at Stanford SVL specializing in the intersection of computer vision and robotics, advised by Silvio Savarese, with 11 years of engineering and research experience spanning industry and academia. He brings a rare combination of systems and ML expertise—ranging from building REST and time-series services at Oracle to developing perception and simulation tooling for robot learning, including contributions to the iGibson environment for realistic interactive scenes. A double bachelor in EE and CS from Georgia Tech and an MS from Stanford, he’s fluent in low-level embedded work, ROS/C++ nodes, and large-scale ML experiments with TensorFlow. He has an active research publication record and editorial experience as lead editor of The Gradient, signaling both technical depth and clear science communication. Notably, his work often bridges practical engineering (microservices, data aggregation) with robot-centric scene understanding and object placement analysis.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Science; Bachelor of Science, Dual Major in Electrical Engineering; Computer Science, 3.88/4.0, Bachelor of Science; Bachelor of Science, Dual Major in Electrical Engineering; Computer Science, 3.88/4.0 at Georgia Institute of TechnologyMaster of Science - MS, Computer Science, 3.87, Master of Science - MS, Computer Science, 3.87 at Stanford University
Russian
Stackoverflow
11reputation
4kreached
1answer
0questions
Github Skills (10)
simulations10
simulation10
pybullet10
python10
3d9
3ds9
json9
yaml7
instagram6
ruby-on-rails6
Programming languages (12)
Github contributions (5)
StanfordVL/iGibson
Jan 2021 - May 2021
A Simulation Environment to train Robots in Large Realistic Interactive Scenes
Role in this project:
Back-end Developer
Back-end DeveloperContributions:49 commits, 1 issue in 4 months
Contributions summary:Andrey contributed scripts focused on analyzing and querying object placement within the iGibson environment. They developed scripts to determine stable object rotations and generate placement probabilities. Additionally, they modified existing scripts and added new data utilities related to scene and object processing, specifically for clutter generation and scene analysis. Their work primarily involved manipulating 3D object data and integrating it into the simulation environment.
simulationrobot-simulator3d-scenes
StanfordVL/ac-teach
Oct 2019 - Mar 2020
Code for the CoRL 2019 paper AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
Contributions:3 commits, 2 pushes, 1 branch in 5 months
actor-criticbayesian
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.