About Me

I am a machine learning researcher with over a decade of experience developing AI-driven solutions in healthcare, neurotechnology, and biomedical engineering. My expertise spans deep learning, neural signal processing, and large-scale data analytics, with a proven record of translating research into real-world impact—including patented brain–computer interface (BCI) rehabilitation systems.

I have led multidisciplinary teams, mentored graduate students and engineers, and built strong collaborations across academia, industry, and clinical partners to advance the frontiers of AI in neuroscience and healthcare.

Core Expertise

• Machine Learning & AI: Deep learning (CNNs, RNNs, Transformers), classical algorithms (SVM, Random Forest, clustering), advanced time-series modeling, and feature engineering for EEG/ECG data.
• Programming & Tools: Expert in Python (NumPy, pandas, scikit-learn, TensorFlow, PyTorch, MNE), MATLAB, C/C++; proficient with Git, Linux, and cloud-based development environments.
• Data Science: Comprehensive experience in data preprocessing, statistical analysis, data visualization (Matplotlib, Seaborn), scalable data pipelines, and SQL/database integration.
• Domain Expertise: Brain–computer interfaces (BCIs), biomedical signal processing, neural engineering, healthcare analytics, and human–robot interaction.

Education

• Ph.D. in Electronic Engineering
  Specialization: Machine Learning & Neural Signal Processing
  Yeungnam University, South Korea (2006–2010)

• M.Sc. in Electronic Engineering
  Yeungnam University, South Korea (2004–2006)

• B.Sc. in Information Technology
  Tashkent University of Information Technologies, Uzbekistan (2000–2004)

Certifications & Affiliations

• IEEE Senior Member – Honored for significant professional achievements in electrical and computer engineering
• US Patent 9,081,890 – Co-inventor of an EEG-based rehabilitation training system for neurotechnology applications
• Open-Source Maintainer – Lead developer of EEG-PyTorch and P3Net BCI deep learning toolkits on GitHub
• YouTube Educator (2.9K+ subscribers): Creator of educational content on machine learning, brain–computer interfaces, microcontrollers, and Python for neural data analysis
  YouTube Playlists

Selected Publications

• B. Abibullaev, A. Keutayeva, and A. Zollanvari, “Deep Learning in BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications,” IEEE Access, 2023. Link
• B. Abibullaev, Kassymzhomart Kunanbayev, and A. Zollanvari, “Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects,” IEEE Transactions on Human-Machine Systems, 2022. Link
• B. Abibullaev and A. Zollanvari, “A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021. Link
• B. Abibullaev and A. Zollanvari, “Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces,” IEEE Journal of Biomedical and Health Informatics, 2019. Link