Interdisciplinary applied researcher with expertise in computer vision, pattern recognition, interpretable machine learning, and high-dimensional imaging data analysis. I specialize in developing computational methods for pattern recognition, dimensionality reduction, feature extraction, and explainability in complex datasets such as high-dimensional imaging datasets and Imaging Mass Spectrometry (IMS). My research experience includes statistical machine learning, facial recognition, dimension reduction, deep learning frameworks, signal processing, and spectral omics, reflects a strong foundation in both theoretical model development and applied research. 

My scientific interests focus on developing tools to improve machine learning explainability, customize data interpretation, pattern recognition, application-domain-aware dimension reduction, and feature extraction, with applications in biomedical, environmental, and industrial sciences.