Position: Data Scientist
Category: Data Science
English level: intermediate
Years of experience: 7


Development and support of autonomous navigation systems for drones using ROS,
OpenCV, PCL, MRPT;
Development and modernization of SLAM algorithms;
Development recognition and objects classification systems using Tensorflow,
Torch, Cafe;
Development of embedded computer vision systems based on the Yocto-project;
Creation and modification of neural networks for computer vision based on NCC,
Darknet, ResNet, SSD, MobileNet and others;
Developing motion planners using OMPL.
Experienced in implementation of edge cutting scientific development:
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching,
Real-Time Self-Adaptive Deep Stereo,
Video Action Transformer Network
Relational Action Forecasting,
Long-Term Feature Banks for Detailed Video Understanding
A Multitask Learning Approach to Action Quality Assessment,
MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation,
TopNet: Structural Point Cloud Decoder,
FlowNet3D: Learning Scene Flow in 3D Point Clouds,
SSN: Learning Sparse Switchable Normalization via SparsestMax,
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous
Driving Scenarios,
Pose2Seg: Detection Free Human Instance Segmentation,
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching.
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM,
An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM.