A. Personal Statement

My career trajectory reflects a passionate commitment to leveraging cutting-edge information technology for tangible impact, beginning as a software developer and progressively embracing leadership roles to successfully drive multiple project launches. This foundation in practical execution has been complemented by a strong entrepreneurial drive, notably demonstrated as the co-founder, President, and CEO of National Health Data Science Lab, a scientific startup focused on healthcare. In this leadership capacity, I oversee strategic direction and operational execution, further solidifying my accountability and vision.

My academic background includes a Master's Degree in Information Technology with a specialization in Software Engineering, providing a robust theoretical and practical framework for my work. My dedication to advancing the field is evident through my active membership in prestigious professional organizations such as the Institute of Electrical and Electronics Engineers (Senior IEEE Membership), the Association for Computing Machinery (ACM Professional Membership) and the Association for the Advancement of Artificial Intelligence (AAAI Membership).

I possess demonstrated scientific research experience since 2024, with a core interest in Data Science, Big Data, Data Mining, Artificial Intelligence (AI), and Machine Learning (ML). My contributions in these areas are already formally recognized through peer-reviewed publications.

These publications highlight my capabilities in applying advanced AI and ML techniques to complex data problems, specifically within forecasting and predictive modeling. My goal is to continue pushing the boundaries of data science and AI, particularly where it intersects with real-world applications that demand innovative solutions. My combined expertise in software engineering, research, and leadership, coupled with my entrepreneurial experience, positions me to make significant contributions to pioneering projects in these domains.

B. Positions, Scientific Appointments, and Honors

Positions and Scientific Appointments

2024 – Present   Scientific Researcher, National Health Data Science Lab

2022 - 2024   Graduate Researcher, Kharkiv National University of Radio Electronics

С. Research Contribution and Accomplishments 

1. My early research contributions focus on the application of artificial intelligence and machine learning algorithms for forecasting Bitcoin prices, with an emphasis on boosting models and sentiment analysis techniques. Through two peer-reviewed publications, I explored how predictive analytics can help address the volatility of cryptocurrency markets. In my first publication, “Bitcoin Price Prediction Using the Boosting Algorithm,” I investigated the effectiveness of boosting-based models in capturing       nonlinear dependencies in Bitcoin price movements. The study involved comprehensive data preprocessing, feature engineering, model training, and hyperparameter optimization. Despite inherent market volatility, the results demonstrated that boosting algorithms — particularly when properly tuned — offer robust performance and reliable insight into price trends. This work highlights the potential of ensemble learning methods to support investment decision-making and strategic financial forecasting in digital asset markets.

a.  Anton Naumov; Afanasieva Irina, Onyshchenko Konstantin; “Bitcoin Price Prediction Using the Boosting Algorithm”; The 2nd International scientific and practical conference “Science and society: modern trends in a changing world” (January 22-24, 2024) MDPC Publishing, Vienna, Austria. 2024. ISBN: 978-3-954754-01-4 https://sci-conf.com.ua/wp-content/uploads/2024/01/SCIENCE-AND-SOCIETY.-MODERN-TRENDS-IN-A-CHANGING-WORLD-22-24.01.24.pdf#page=197

2. In my second publication, “A Study of the Effectiveness of Using Information Technology Based on Artificial Intelligence for Bitcoin Price Forecasting,” I extended this research by incorporating natural language processing (NLP) techniques and sentiment analysis from financial news to improve prediction accuracy. Utilizing tools such as TextBlob, VADER, Flair, spaCy, and BERT in combination with LSTM neural networks, I evaluated the impact of sentiment-derived features on model performance. The findings suggest that integrating sentiment metrics with time series data significantly enhances predictive precision, underscoring the importance of multidimensional data sources in financial modeling.

a.  Anton Naumov; Victoria Vysotska; Kirill Smelyakov; Valentina Shtanko; “A Study of the Effectiveness of Using Information Technology Based on  Artificial Intelligence for Bitcoin Price Forecasting”; 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT); 2024; DOI: 10.1109/csit65290.2024.10982646  

Together, these studies contribute to the growing body of interdisciplinary research at the intersection of artificial intelligence, finance, and data science. They provide practical frameworks for applying machine learning techniques to high-volatility financial instruments, and lay the groundwork for future investigations into AI-driven economic forecasting systems.