- Researching suggested models on Papers With Code - Using Transformer architecture models for 3D point cloud segmentation - Updating existing model architecture (PyTorch) - Evaluating and comparing the quality of models - Integrating ML models into a C++ desktop client providing ML package supporting required API

Studying machine learning to be capable of addressing tasks related to machine learning in my future job. I am already familiar with: - classical machine learning algorithms: LinearRegression, LogisticRegressio, SVM, DecisionTree, RandomForest, PCA, t-SNE - deep-learning models: FCNN, RNN, CNN, Transformer - frameworks: NumPy, scikit-learn, PyTorch

Responsibilities: • Developing endpoint security software • Windows kernel drivers and services development • Windows reverse engineering • Analysing user- and kernel-mode crash dumps • UEFI applications development, using EDK 2 and EfiLib • Linux applications development Tools and technologies: Microsoft Azure, Visual Studio, Windows, C/C++, MSVC, STL, Multithreading, WTL, ATL, Windows Services, Windows Performance Counters, VmWare, Virtualbox, MsSqlServer, WDK, WinDbg, Sysinternals utilities, Svn, Git, UEFI, EDK 2, Trusted Platform Module, OpenSSL, Windows File System Minifilter, Windows Filtering Platform, QEMU, GTK+, LVGL, CMake, GDB, Qt Creator, WSL

Responsibilities: • Fixing bugs in endpoint desktop client • Analysing user-mode crash dumps • Analysing client logs Tools and technologies: Jira, Visual Studio, Windows, C/C++, MSVC, STL, Multithreading, VmWare, Virtualbox, Sysinternals utilities, GitResponsibilities: • Fixing bugs in endpoint desktop client • Analysing user-mode crash dumps • Analysing client logs Tools and technologies: Jira, Visual Studio, Windows, C/C++, MSVC, STL, Multithreading, VmWare, Virtualbox, Sysinternals utilities, Git

Факультет компьютерных систем и сетей, Программное обеспечение информационных технологий

IT Academy, Machine Learning Engineer

Udemy, Machine Learning Engineer