I am a Machine Learning Researcher focused on the development of intelligent methods for biomedical signal analysis, statistical learning, and scientific computing.
My research combines machine learning, deep learning, signal processing, and probabilistic modeling to design robust, interpretable, and reproducible AI systems capable of extracting meaningful information from complex biomedical data.
My current interests include representation learning, uncertainty-aware AI, multimodal learning, and explainable machine learning for healthcare applications.
| Core Field | Research & Application Focus |
|---|---|
| Machine Learning | Supervised/Unsupervised Learning, Ensemble Methods, Statistical Modeling |
| Deep Learning | CNNs, RNNs, Transformers, Attention Mechanisms, Foundation Models |
| Biomedical AI | Advanced Biosignal Analysis (EEG, ECG, EMG) & Clinical Decision Support |
| Signal Processing | Time-Series Forecasting, Automated Feature Extraction, Spectral Analysis |
| Statistical Learning | Bayesian Inference, Gaussian Processes, Probabilistic Architectures |
| Representation Learning | Self-Supervised Learning, Contrastive Learning, Multimodal Alignment |
| Trustworthy AI | Explainable AI (XAI), Model Interpretation, Uncertainty-Aware Machine Learning |
| Scientific Computing | High-Performance Computing, Reproducible Workflows, Open Science |
Machine Learning • Biomedical Signal Processing • Scientific Computing
- AI Modeling: Designed, trained, and evaluated machine learning and deep learning architectures for complex classification and regression tasks.
- Biosignal Engineering: Developed end-to-end signal processing pipelines and automated feature extraction workflows for biomedical datasets.
- Reproducible Research: Built robust, scalable, and fully documented scientific computing workflows utilizing Git and open-source ecosystems.
- Statistical Rigor: Performed rigorous experimental evaluations, statistical analyses, and validation metrics on multidimensional data.
- Technical Communication: Authored comprehensive research documentation, structured technical reports, and collaborated in multidisciplinary scientific clusters.
Python PyTorch TensorFlow Scikit-Learn NumPy SciPy Pandas Statistical Modeling Time-Series Analysis Linux Deployment
