Robel Dagnew

Background

About Robel Dagnew

Computational Biologist and Data Scientist specializing in developing advanced algorithms for genomic analysis and precision medicine applications. With expertise in machine learning approaches for variant detection and NGS data analysis, I bridge the gap between complex biological data and actionable clinical insights. My technical foundation includes designing and implementing SVMcaller for copy number variation detection, developing Hidden Markov Models for analyzing long-read sequencing data, and creating optimized validation pipelines that improve the reliability of genomic findings. I excel at translating computational solutions into practical tools that enhance our understanding of human genetics and disease. I'm particularly passionate about applying computational approaches to advance liquid biopsy technologies, multimodal data integration for personalized treatment selection, and developing more sensitive methods for early cancer detection. My goal is to contribute to innovations that transform how we use genomic data to improve patient outcomes. Experienced in high-performance computing environments and cloud-based solutions, I combine algorithmic expertise with practical knowledge of genomic workflows. I thrive in collaborative, multidisciplinary environments where computational solutions directly impact human health.