Yehor Molochynskyi Email & Phone Number

In short, B.S/M.S in Computer MathematicsProgramming Skills:- Primary: Python (with libraries numpy, pandas, scipy, matplotlib, sagemath, sympy), C++- Scientific computations: MATLAB, R- Experience with Java, Haskell, ErlangMachine Learning Experience:- Supervised Learning: Worked with regressions, decision trees/random forests, SVM, Naive Bayes, k-means clustering, and ensemble learning techniques (bagging, boosting, stacking) using scikit-learn; includes XGBoost- Unsupervised Learning: Clustering, anomaly detection, dimensionality reduction (PCA, t-SNE) using scikit-learn; experience with Q-learning in reinforcement learningDeep Learning Focus:- Computer Vision: Image formation and processing (OpenCV); projects in motion and depth estimation, exploring 3D reconstruction and image-based rendering- Techniques: CNNs (VGG, Inception, ResNet, YOLO, SSD, UNet), autoencoders (e.g., SegNet), NLP (RNN/LSTM), Transformers- Tools: TensorFlow (Keras), PyTorch- Other: Course on embedded devices (Raspberry Pi)Computer Science Proficiency: algorithms & complexities, data structures, dynamic programming. Soft computations (such as ANFIS and genetic algorithms, etc) are also worth mentioning.Mathematics Expertise: linear algebra, probability, statistics, optimization, numerical methods, computational geometry, time series, mathematical cryptographyAdditional Training:- High-performance computing course; experience with SKIT-5 supercomputer (using MPI/CUDA)- Two intensive practicums at Samsung R&D Institute Ukraine during B.S. program.