Research Interests
My research focuses on developing intelligent systems at the intersection of artificial intelligence and control theory, with applications in healthcare diagnostics and industrial automation. I'm particularly interested in creating transparent, robust AI solutions that can be effectively deployed in real-world settings.
Research Domains
AI in Medical Diagnostics
Developing explainable deep learning models for early detection of diseases through multi-modal imaging analysis (ultrasound, mammography, MRI).
Industrial Automation
Designing intelligent control systems for manufacturing processes, with focus on predictive maintenance, quality control, and adaptive production lines.
Explainable AI
Creating transparent AI systems that provide interpretable explanations for their decisions, crucial for healthcare and industrial applications.
Adaptive Control Systems
Developing self-tuning controllers that can adapt to changing system dynamics and uncertainties in real-time industrial environments.
Multi-modal Data Fusion
Architectures for combining heterogeneous data sources (images, sensors, time-series) to improve decision-making in both medical and industrial contexts.
Robust AI Systems
Ensuring AI models maintain performance across different domains and under varying conditions through domain adaptation and transfer learning.
Featured Research Projects
Multi-modal AI for Breast Cancer Detection
Ongoing2024 - Present
Developing deep learning models that integrate ultrasound, mammography, and MRI scans with explainable AI techniques to enhance early breast cancer detection accuracy and clinical trust.
Transformer-based Deep Learning for Rapid Stroke Diagnosis
Completed2023 - 2025
The project's objectives include establishing an international network, knowledge exchange, and supporting researchers and students affiliated to collaborated teams. The research is expected to have a significant impact on improving skills of young researchers.