Research Activities and Interests
Research Activities and Interests
My research profile reflects a strong commitment to advancing knowledge at the intersection of data science, artificial intelligence, and human-centered computing. Through peer-reviewed publications, applied research projects, and interdisciplinary collaborations, I focus on developing methodologically sound and practically impactful solutions to complex, data-driven problems.
My work spans both theoretical investigation and applied experimentation, addressing real-world challenges in areas such as smart energy systems, affective computing, and neurotechnology. By combining statistical learning, machine learning, and signal processing techniques, I aim to contribute research that is both academically rigorous and societally relevant.
I have actively participated in interdisciplinary research initiatives involving academia–industry collaboration, with a particular focus on:
Smart energy systems and smart meter analytics, including load profiling, clustering, and forecasting
Healthcare and neurotechnology, especially EEG-based brain–computer interface (BCI) systems
Automotive and industrial data analytics, applying statistical modeling and machine learning for performance optimization
These collaborations have strengthened my ability to work across domains, translate research questions into deployable solutions, and communicate effectively with both technical and non-technical stakeholders.
My research activities are characterized by a continuous pursuit of innovation, reproducibility, and methodological robustness. They include:
Designing and implementing machine learning and deep learning models for structured and unstructured data
Conducting time-series analysis, clustering, and forecasting on large-scale real-world datasets
Developing EEG/BCI-based experimental systems, integrating signal acquisition, preprocessing, feature extraction, and evaluation
Contributing to applied research projects that bridge academic inquiry with industrial needs
Preparing manuscripts and research artifacts suitable for peer-reviewed conferences and journals
Through these activities, I seek to contribute to the development of scalable, explainable, and ethically grounded AI systems.
My research interests lie at the intersection of machine learning, signal processing, and human-centered intelligent systems, with a particular focus on:
Brain–Computer Interfaces (BCI) and EEG Signal Processing
Cognitive state modelling, attention detection, and neurofeedback systems
Affective Computing and Emotion Recognition
Speech-based and multimodal emotion recognition using deep learning
Machine Learning for Time-Series and Physiological Data
Modeling complex temporal patterns in real-world datasets
Deep Learning and Representation Learning
Feature learning for audio, biosignals, and multimodal data
Human-Centered AI Systems
Designing adaptive, interpretable, and user-aware intelligent systems
Applications of AI in Healthcare and Neurotechnology
Especially in cognitive and behavioral analysis