Amlan Talukder, Ph.D. | National Institute of Environmental Health Sciences Skip Navigation Amlan Talukder, Ph.D. Close the left navigation Add Health Assessment and Translation Amlan Talukder, Ph.D. Staff Scientist Tel 984-287-4518 [email protected] Amlan Talukder, Ph.D., is a Staff Scientist in Integrative Health Assessments Branch (IHAB) at the Division of Translational Toxicology (DTT) at NIEHS. He specializes in generative artificial intelligence (AI), machine learning, and data-driven scientific discovery. His research focuses on designing and deploying AI-powered tools using advanced Retrieval-Augmented Generation (RAG), agentic AI frameworks, vector databases, knowledge graphs, and large language models (LLMs) to address public health questions. Amlan received his Ph.D. in Computer Science from University of Central Florida in 2021 and his B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology in 2011. He has extensive experience developing end-to-end machine learning research pipelines, deep learning architectures (convolutional neural networks-CNNs, recurrent neural networks-RNNs, Transformers, Autoencoders), and scalable analytics systems. His research spans sleep electroencephalogram analysis, electronic health records, neurological and chronic disease modeling, and bioinformatics-driven genomic data analysis. Talukder’s research integrates AI and biomedical research to build intelligent systems that translate complex scientific data into actionable insights. He is particularly interested in: AI-driven scientific discovery, human health data modeling, explainable and trustworthy AI systems, and scalable AI infrastructure for research environments. Amlan brings strong software engineering expertise in full-stack web development. He builds products to leverage health research data and enable discovery through production-ready AI systems, interactive dashboards, and web applications using python, JavaScript, Shiny, PostgreSQL, MongoDB, and other programming languages. Recent Publications Li L, Talukder A, Yeung D, Li Y, Umbach D, Gilmore J, Fan Z. Comparison of overnight trends in relative power for specific frequency bands, sleep stages, and brain regions between patients with depressive disorder and matched control subjects. Psychiatry research. Neuroimaging 2025 Aug;351:112021. Abstract Li L, Talukder A, Yeung D, Li Y, Umbach D, Gilmore J, Fan Z. Comparison of overnight trends in relative power for specific frequency bands, sleep stages, and brain regions between patients with depressive disorder and matched control subjects. Psychiatry research. Neuroimaging. 2025 Aug Yeung D, Talukder A, Shi M, Umbach D, Li Y, Motsinger-Reif A, Hwang J, Fan Z, Li L. Differences in brain spindle density during sleep between patients with and without type 2 diabetes. Computers in biology and medicine 2025 Jan;184:109484. Abstract Yeung D, Talukder A, Shi M, Umbach D, Li Y, Motsinger-Reif A, Hwang J, Fan Z, Li L. Differences in brain spindle density during sleep between patients with and without type 2 diabetes. Computers in biology and medicine. 2025 Jan Anandanadarajah N, Talukder A, Yeung D, Li Y, Umbach D, Fan Z, Li L. Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data. Signals 2024 Dec;5(4):690-704. Abstract Anandanadarajah N, Talukder A, Yeung D, Li Y, Umbach D, Fan Z, Li L. Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data. Signals. 2024 Dec Talukder A, Li Y, Yeung D, Shi M, Umbach D, Fan Z, Li L. OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics. Computers in biology and medicine 2024 Aug;178:108777. Abstract Talukder A, Li Y, Yeung D, Shi M, Umbach D, Fan Z, Li L. OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics. Computers in biology and medicine. 2024 Aug Yeung D, Talukder A, Shi M, Umbach D, Li Y, Motsinger-Reif A, Fan Z, Li L. Differences in sleep spindle wave density between patients with diabetes mellitus and matched controls: implications for sensing and regulation of peripheral blood glucose. medRxiv : the preprint server for health sciences 2024 Apr 12 [Epub ahead of print]. Abstract Yeung D, Talukder A, Shi M, Umbach D, Li Y, Motsinger-Reif A, Fan Z, Li L. Differences in sleep spindle wave density between patients with diabetes mellitus and matched controls: implications for sensing and regulation of peripheral blood glucose. medRxiv : the preprint server for health sciences. 2024 Apr 12 More Recent Publications from PubMed Back to Top Last Reviewed: March 09, 2026