Curriculum Vitae
Professional Summary
Machine learning researcher with 10+ years of experience in neural signal processing, brain-computer interfaces, and deep learning for non-stationary temporal data. Research focused on invariant representation learning — developing AI systems that generalize reliably under distribution shift, subject variability, and real-world deployment constraints. Insights from neural and physiological signals transfer directly to medical monitoring, wearable devices, and industrial time-series systems.
Track record includes competitive grant funding as PI and Co-PI, supervision of PhD, MSc, and undergraduate researchers, nearly a decade of undergraduate teaching at the Associate Professor level, and peer-reviewed publications in IEEE Transactions on Human-Machine Systems, IEEE Journal of Biomedical and Health Informatics, and IEEE Access.
Academic & Research Appointments
| Position | Institution | Location | Period |
|---|---|---|---|
| Associate Professor | Nazarbayev University, School of Engineering and Digital Sciences | Astana, Kazakhstan | 2022–present |
| Assistant Professor | Nazarbayev University, Robotics & Mechatronics Department | Astana, Kazakhstan | 2015–2021 |
| Visiting Professor | University of Houston, Dept. of Electrical & Computer Engineering | Houston, TX | 2018 |
| NIH Postdoctoral Fellow | University of Houston | Houston, TX | 2014–2015 |
| Research Scientist | Samsung Medical Center | Seoul, South Korea | 2013–2014 |
| Senior Research Scientist | DGIST, Robotics Research Division | Daegu, South Korea | 2012–2013 |
| Postdoctoral Fellow | DGIST, Robotics Research Division | Daegu, South Korea | 2010–2012 |
Education
| Degree | Institution | Location | Years |
|---|---|---|---|
| Ph.D., Electronic & Computer Engineering | Yeungnam University | South Korea | 2006–2010 |
| M.S., Electronic Engineering | Yeungnam University | South Korea | 2004–2006 |
Research & Technical Expertise
Machine Learning & AI
Deep learning (CNNs, Transformers, hybrid architectures), representation learning for time-series data, domain adaptation and subject-independent modeling, self-supervised and multimodal learning, model calibration and uncertainty quantification, temporal sequence modeling for long physiological recordings.
Programming & Frameworks
PyTorch, TensorFlow, scikit-learn, MNE-Python, Python, MATLAB, C/C++
Research Infrastructure
Reproducible ML pipelines, Git, Docker, HPC, cloud computing environments.
Application Domains
Clinical EEG & intracranial EEG, brain-computer interfaces, biomedical signal processing, multimodal physiological data.
Teaching
Designed and delivered undergraduate and graduate courses integrating mathematical foundations with hands-on implementation. Average teaching evaluations: 4.5 / 5.0.
Courses taught: Signals and Systems · Microcontrollers with Laboratory · Digital Logic Circuit Design · Machine Learning with Applications (PyTorch-based) · Brain-Machine Interfaces (Graduate Seminar) · Neural Signal Processing · Deep Learning
Students I have mentored have appeared as co-authors on peer-reviewed publications, including work in IEEE Access and IEEE Transactions on Human-Machine Systems. I view research co-authorship as a natural extension of the teaching mission and one of the most effective ways to develop scientific thinking in early-career students.
Full teaching philosophy → Teaching Statement
Grants & Funding
Principal Investigator or Co-Principal Investigator on competitive research grants totaling over $1.1M, including NIH-funded work on EEG-based motor decoding for stroke rehabilitation (University of Houston, with Prof. Jose Luis Contreras-Vidal).
Patents
2 granted patents in neural signal processing and brain-computer interface technology.
Selected Publications
For the complete list, see Publications and Google Scholar.
2023: Abibullaev, B., Keutayeva, A., & Zollanvari, A. Deep Learning in BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications. IEEE Access. [Link]
2022: Abibullaev, B., Kunanbayev, K., & Zollanvari, A. Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects. IEEE Transactions on Human-Machine Systems. [Link]
2021: Abibullaev, B. & Zollanvari, A. A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces. IEEE Transactions on Systems, Man, and Cybernetics: Systems. [Link]
2019: Abibullaev, B. & Zollanvari, A. Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces. IEEE Journal of Biomedical and Health Informatics. [Link]
Academic Service & Leadership
Established and led a Brain-Computer Interface research laboratory at Nazarbayev University. Supervised doctoral, master’s, and undergraduate researchers. Reviewer for international journals and conferences in machine learning and biomedical engineering. IEEE Senior Member.