Dr. Saurav Nayak is a clinical biochemist with a strong interest in integrating laboratory science with data-driven approaches to enhance diagnostic and research workflows. His work focuses on four major domains: Artificial Intelligence and Machine Learning, Environmental Exposure and Health, Metabolomics and Spectroscopy, and Biostatistics.

He applies machine learning methods to biomedical data for tasks such as pattern recognition, quality control, and support for clinical interpretation. In parallel, his research examines environmental exposures, including pollutants and xenobiotics, to understand their biochemical and clinical implications. Dr. Nayak’s work in metabolomics and spectroscopy is directed towards developing practical, accessible, and scalable analytical approaches, particularly suited for routine use and resource-limited settings. Biostatistics forms the methodological foundation of his research, guiding study design, data management, analysis, and interpretation. His broader objective is to develop integrated and reproducible workflows that combine analytical, statistical, and computational techniques, generating solutions applicable to both clinical diagnostics and population-level health research.