Position: Data Scientist/ML Engineer
Category: Data Science
English level: upper
Years of experience: 0.5

As a result of work on the current project, an effective method of classifying electroencephalogram signals has been developed.

Have 2 publications in the field of number theory. The papers were published in esteemed journal affiliated with my institution. 

“STATISTICAL AND ALGEBRAIC PROPERTIES OF THE DISTRIBUTION
OF PRIMITIVE ROOTS IN SETS OF CONSECUTIVE PRIMES”, No 36 (112)
(2022): Electrotechnic and Computer Systems

“SYSTEMATIC ANALYSIS OF THE DISTRIBUTION OF PRIMITIVE ROOTS
OF PRIME NUMBERS IN ARTIN’S CONJECTURE ”, No 36 (112) (2022):
Electrotechnic and Computer Systems

And the publication related to the project I'm currently working on.

“EEG Emotion Classification Using 3-Dimensional Convolutional Neural Networks”, viXra:2306.0037

Acquired professional DeepLearning.AI TensorFlow Developer certification and in preparation for the TensorFlow Developer exam.
From 8/2022 to 6/2023:
Conducted in-depth research on the statistical and algebraic properties of the distribution of primitive roots in sets of consecutive primes.
Collected and analyzed extensive data sets related to prime numbers, including calculating primitive roots and examining their distribution patterns.
Assisted in the preparation and revision of research papers, including detailed analysis, interpretation of results, and discussion of theoretical implications.
Utilized statistical analysis techniques to evaluate the observed patterns and assess the validity of Artin's Conjecture.
Utilized capabilities of Pandas, Numpy, Matplotlib and Numba for efficient calculations which led to interesting results.

Currently, I am involved in an ambitious project that focused on emotion classification based on EEG data. This project aims to harness the power of EEG technology to develop innovative methods for accurately classifying emotions, paving the way for new insights in cognitive science and human-computer interaction.

We proposed a methodology for classifying EEG signals based on signal processing using wavelet transform and superlet transform. The wavelet transform is used to decompose the EEG signal into frequency components, which are then used as features for classification. The proposed approach is evaluated using the publicly available “GAMEEMO” EEG dataset, which has been annotated by valence and emotional arousal.

Theoretical part can be found in the extended abstract:
 “EEG Emotion Classification Using 3-Dimensional Convolutional Neural Networks”, viXra:2306.0037