David Carlson

About

My general research focus is on developing novel machine learning and artificial intelligence techniques that can be used to accelerate scientific discovery. I work extensively both on the fundamental theory and algorithms as well as translating them into scientific applications. I have extensive partnerships deploying machine learning techniques in environmental health, mental health, and neuroscience.

Education

• Ph.D. Duke University, 2015
• Yoh Family Associate Professor of Civil and Environmental Engineering
• Associate Professor of Civil and Environmental Engineering
• Associate Director in the Institute of AI Engineering
• Associate Professor in Biostatistics & Bioinformatics
• Associate Professor in the Department of Electrical and Computer Engineering
• Associate Professor of Computer Science
• Associate Professor of Biomedical Engineering
• Associate Professor in Neurobiology
• Faculty Network Member of the Duke Institute for Brain Sciences

Selected publications

• Avigdor T, Ho A, Moye M, Davalan W, Minato E, Hannan S, et al. Epilepsy surgery outcomes and their determinants: a systematic review and individual patient data meta-analysis. J Neurol Neurosurg Psychiatry. 2026 Jun 12;97(7):639u201348.
• Calhoun ZD, Bergin M, Carlson D. Probabilistic interpolation of crowdsourced meteorological data for higher-resolution gridded estimates of surface air temperature. Urban Climate. 2026 Jun 1;67.
• Abdelaal K, Walder-Christensen KK, Blount C, Williford K, Adams-Grimaldi M, Mague SD, et al. A widespread internal brain state for fentanyl withdrawal. 2026.
• K K, Flythe JE, Pun PH, Winkelmayer WC, Carlson D. Advanced artificial intelligence vs simpler models for 1-year death prediction among patients receiving hemodialysis. JAMIA Open. 2026 Apr;9(2):ooaf152.
• Goffinet J, Hanks C, Carlson DE. HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models. 2026.
• Talbot A, Keller CJ, Trevino C, Carlson DE, Dammer EB, Johnson ECB, et al. Generative Principal Component Regression via Variational Inference. IEEE transactions on signal processingu202f: a publication of the IEEE Signal Processing Society. 2026 Jan;74:1656u201370.
• Zhang H, Jiang Z, Zhang S, Tu L, Carlson D. Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data. Pattern Recognition. 2025 Dec 1;168.
• Hickman SHM, Kelp MM, Griffiths PT, Doerksen K, Miyazaki K, Pennington EA, et al. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research. Geoscientific Model Development. 2025 Nov 20;18(22):8777u2013800.
• Walder-Christensen KK, Goffinet J, Bey AL, Syed R, Benton J, Mague SD, et al. Sleep-Wake States Are Encoded across Emotion Regulation Regions of the Mouse Brain. eNeuro. 2025 Nov;12(11).
• Li K, Wood C, Nichols L, Calhoun ZD, Bhavsar NA, Carlson D. Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks. AJE Adv. 2025 Sep 24;