Associate Professor of Civil and Environmental Engineering
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.
Appointments and Affiliations
- Associate Professor of Civil and Environmental Engineering
- Assistant Professor in Biostatistics & Bioinformatics
- Assistant Professor in the Department of Electrical and Computer Engineering
- Assistant Professor of Computer Science
- Faculty Network Member of the Duke Institute for Brain Sciences
Contact Information
- Office Location: Hudson Hall, Durham, NC 27705
- Office Phone: +1 919 668 9680
- Email Address: david.carlson@duke.edu
Education
- Ph.D. Duke University, 2015
Research Interests
Machine learning, predictive modeling, health data science, statistical neuroscience
Courses Taught
- ME 555: Advanced Topics in Mechanical Engineering
- EGR 393: Research Projects in Engineering
- ECE 899: Special Readings in Electrical Engineering
- ECE 494: Projects in Electrical and Computer Engineering
- ECE 392: Projects in Electrical and Computer Engineering
- COMPSCI 394: Research Independent Study
- COMPSCI 393: Research Independent Study
- CEE 780: Internship
- CEE 702: Graduate Colloquium
- CEE 690: Advanced Topics in Civil and Environmental Engineering
In the News
- For Many Urban Residents, It’s Hotter Than Their Weather App Says (Jun 27, 2024 | Pratt School of Engineering)
- Eyes in the Sky Bring Good News on Trash Burning in the Maldives (Jul 14, 2023 | Pratt School of Engineering)
- Students Find Interdisciplinary Exploration and Connection in Winter Breakaway Courses (Jan 21, 2021)
- David Carlson: Engineering and Machine Learning for Better Medicine (Jan 9, 2018 | Duke Research Blog)
- David Carlson: Generating Scientific Understanding from Machine Learning (Aug 23, 2017 | Pratt School of Engineering)
Representative Publications
- Hughes, Dalton N., Michael Hunter Klein, Kathryn Katsue Walder-Christensen, Gwenaëlle E. Thomas, Yael Grossman, Diana Waters, Anna E. Matthews, et al. “A widespread electrical brain network encodes anxiety in health and depressive states.” BioRxiv, June 30, 2024. https://doi.org/10.1101/2024.06.26.600900.
- Walder-Christensen, Kathryn, Karim Abdelaal, Hunter Klein, Gwenaëlle E. Thomas, Neil M. Gallagher, Austin Talbot, Elise Adamson, et al. “Electome network factors: Capturing emotional brain networks related to health and disease.” Cell Rep Methods 4, no. 1 (January 22, 2024): 100691. https://doi.org/10.1016/j.crmeth.2023.100691.
- Calhoun, Z. D., M. S. Black, M. Bergin, and D. Carlson. “Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat.” Environmental Science and Technology Letters, January 1, 2024. https://doi.org/10.1021/acs.estlett.4c00296.
- Calhoun, Zachary D., Frank Willard, Chenhao Ge, Claudia Rodriguez, Mike Bergin, and David Carlson. “Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference.” Scientific Reports 14, no. 1 (January 2024): 540. https://doi.org/10.1038/s41598-023-50981-w.
- Grossman, Yael S., Austin Talbot, Neil M. Gallagher, Gwenaëlle E. Thomas, Alexandra J. Fink, Kathryn K. Walder-Christensen, Scott J. Russo, David E. Carlson, and Kafui Dzirasa. “A widespread oscillatory network encodes an aggressive internal state.” Cold Spring Harbor Laboratory, December 7, 2022. https://doi.org/10.1101/2022.12.07.519272.