Nataliia Kholodna

A hard-working, driven and curious student from Lviv Polytechnic National University,

Ukraine is eager to assist the company as a Intern | Junior Data Scientist by generating data-driven solutions, discovering hidden trends and applying cutting-edge machine learning technologies to build intelligent models.

Skilled in machine learning, data analysis and visualization, programming with 2+ years of experience in academic project work and Kaggle challenges.

Interested in both short- and long-term offers for junior-level Data Science positions or internships, open to relocation.

PUBLICATIONS AND CONFERENCES

IEEE TCSET 2022: Two-Stage AES Encryption Method Based on Stochastic Error of a

Neural Network In cooperation with the supervisor Prof. Roman Peleschak conducted and communicated a research on the current trends and recent developments in cryptography.

Suggested and described a novel method of data encryption and decryption using synthesized Advanced Encryption Standard and feed-forward neural networks.

Developed and carried out tests on the applicability of the proposed cryptographic system in Python.

Explored the influence of number of hidden layers, chosen activation functions,

learning rate on average prediction loss, training time, deciphering error rate.

CEUR Workshop Proceedings, 2021: A Machine Learning Model for Automatic Emotion

Detection from Speech

Developed a system for monitoring public emotions from publications in social networks using machine learning.

Tested the developed system with collected real-world data and correlated received results to the specific events in the life of society, the population of a geographical region (India) and certain community (computer game fans).

Compared performance of classical machine leaning (logistic regression, naive

Bayes, random forest etc.) and deep-learning (LSTMs, convolutional neural networks) approaches.

Selected the best-performing pipeline in terms of text pre-processing (tokenization,

stemming, lemmatization etc.), methods of text vectorization or word embedding

(tf–idf, Word2Vec, GloVe etc.), feature engineering (e.g. semantic scores from AFINN,

ANEW dictionaries), machine learning model, its parameters and architecture.

EDUCATION

Bachelor of Science: System Analysis (Data Science Specialization), Lviv Polytechnic National University, Lviv

September 2018 — Present 

Average score per autumn semester 2022 - 93/100 (5/5)

COURSES

Data Science Fundamentals, DataRoot University The Basics of Statistics, Part 1-2-3, Stepik

Data Analysis in R, Stepik

Data Science Math Skills, Introduction to Machine Learning, Coursera