About Me
Bridging the gap between Artificial Intelligence and Biological Data Science.
Welcome to my digital space. I am a Machine Learning / Data Scientist and Computational Biologist with over 10 years of experience working at the intersection of artificial intelligence, biomedical data science, and precision medicine. My work focuses on developing advanced deep learning architectures — including Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) — to extract meaningful insights from complex, high-dimensional datasets.
I specialize in integrating heterogeneous data sources such as genomics, transcriptomics, protemoics, metabolomics, microbiome, and medical imaging data to improve disease prediction and uncover biologically relevant patterns. My projects include genotype-to-phenotype prediction using GNNs, antimicrobial resistance detection pipelines, scalable multi-omics integration frameworks, and synthetic medical image generation and segmentation for diagnostic automation.
Currently, I am developing multimodal deep learning models for cardiovascular disease (CVD) prediction, combining biological and clinical data to enhance risk stratification, interpretability, and translational impact. My research emphasizes reproducibility, scalability, and the practical deployment of AI systems in real-world biomedical settings.
Beyond healthcare, I have applied machine learning and deep learning methods across diverse domains, designing data-driven solutions for complex real-world problems. I am driven by building robust, adaptable AI systems that transform data into actionable insights across industries.
