Софія

ABOUT ME
I’m Sofiia, a 20-year-old Computer Science student at Lviv Polytechnic National
University, now in my fourth year. My specialization is Computational Intelligence and
Smart Systems. I’ve served as our group leader since the first term, which taught me
ownership and clear communication. I’m disciplined (my day runs on a Notion plan) and
genuinely curious — I like understanding things end-to-end and down to the details.
Outside class, I stay active (basketball, volleyball) and enjoy astronomy and physics.

TECHNICAL SKILLS
Languages Experience : Python, C#/C++, SQL

Oliiarnyk Sofiia Tech Stack Actively Used: VSCode, Visual Studio, PyCharm, MSSQL, MySQL, Jupyter
Notebook(Anaconda)
Data Scientist Familiar: Git, Docker
Business Understanding of business logic, workflows, and system structure
[открыть контакты](см. выше в блоке «контактная информация») Modeling
EDUCATION
Lviv, Ukraine
Lviv Polytechnic National University 📍
Lviv, Ukraine | 2022 – 2026 (expected)
[открыть контакты](см. выше в блоке «контактная информация») Bachelor’s Degree in Computer Science (Smart Systems and Computational
Intelligence)
ONLINE PROFILES Department of Automated Control Systems

github.com/MrSioser Cisco Networking Academy 📍 Online | May 2024
[открыть контакты](см. выше в блоке «контактная информация») CCNA: Introduction to Networks
Instructor: Vyacheslav Ledentsov
kaggle.com/mrsioser
EXPERIENCE
LANGUAGE Database Fundamentals (Lviv Polytechnic, 2023)
Completed a university-level course focused on relational databases using Microsoft
Ukrainian - Native SQL Server (MSSQL).
English - Upper-Intermediate Additionally, built personal mini-projects using MySQL and MongoDB to deepen
practical understanding.

Course Project – Theories & Methods of Computational Intelligence (Lviv Polytechnic, 2025)
Applied regression, classification, and clustering techniques using libraries such as scikit-learn, pandas, numpy and
matplotlib.
Developed and trained perceptron and multilayer perceptron (MLP) models with TensorFlow.
Designed and assessed simple convolutional neural networks (CNN) for image classification tasks using torch.
Investigated Transformer models and their applications in natural language processing (NLP).
Studied core principles of reinforcement learning (RL) through OpenAI Gym simulations with gymnasium.
Carried out preprocessing of Ukrainian and English text datasets, including tokenization, stop-word filtering, and
normalization.

Course Project - Information Systems Design (Lviv Polytechnic, 2025)
Designed a full-scale information system using UML, DFD, IDEF0/IDEF3, AllFusion Process Modeler, and ERwin tools.

PET-PROJECTS
1.User Spending Prediction – Conducted (EDA) to identify key spending patterns using pandas, NumPy, and Matplotlib. Built
and compared regression, classification, and ensemble models in scikit-learn (Linear/Logistic Regression, Decision Tree,
Random Forest, Gradient Boosting) and evaluated results using R², MAE, MSE, and Accuracy metrics.
2.Opossum Clustering Analysis – Performed (EDA) and applied (PCA) using pandas, NumPy, and scikit-learn. Implemented
and compared K-Means, DBSCAN, and Fuzzy C-Means — to group opossums. Evaluated clustering quality using Silhouette
Score, Davies–Bouldin Index, and Calinski–Harabasz Score, visualizing results with Matplotlib and Seaborn
3.Name Matching with Fuzzy Logic – Performed EDA and developed a fuzzy logic–based system in Python to evaluate
similarity between names under uncertainty. Utilized fuzzywuzzy, soundex, and Damerau–Levenshtein distance algorithms
integrated scikit-fuzzy to compute composite similarity probabilities.
4.Heart Disease Prediction – Developed a single-layer Perceptron in Python using PyTorch and NumPy with StandardScaler
preprocessing. Trained the model using Sigmoid activation and evaluated performance for Accuracy and Loss.
5.Image Classification with CNN – Developed a custom Convolutional Neural Network in PyTorch for multi-class image
recognition. Implemented convolutional and pooling layers from scratch, used Adam optimizer and CrossEntropyLoss, and
evaluated model performance using Accuracy and Loss, visualizing predictions and training progress.
6.Development of Neural Networks for NLP – Processed Ukrainian text data using spaCy for linguistic analysis, then
generated BERT embeddings using Hugging Face Transformers and compared sentence similarity with cosine similarity.
Additionally explored GloVe word vectors for semantic comparison between words.
7.House Prices Prediction (Kaggle) – Performed comprehensive EDA with missing-value imputation, outlier removal, and
feature engineering (e.g., TotalSF, Age). Encoded categorical features use one-hot encoding, applied log transformation
to normalize target distribution, and trained an XGBoost Regressor tuned for performance. Model evaluation was based on
the Root Mean Squared Logarithmic Error (RMSLE) metric, achieving high accuracy on validation data.