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
- Associate Professor in Biostatistics & Bioinformatics
- Associate Professor of Computer Science
- Associate Professor in the Department of Electrical and Computer Engineering
- Faculty Network Member of the Duke Institute for Brain Sciences
Contact Information
- Office Location: Hudson Hall, Durham, NC 27705
- 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 493: Projects in Electrical and Computer Engineering
- ECE 392: Projects in Electrical and Computer Engineering
- COMPSCI 393: Research Independent Study
- CEE 780: Internship
- CEE 702: Graduate Colloquium
- CEE 690: Advanced Topics in Civil and Environmental Engineering
- CBB 700: Internship
- CBB 591: Independent Study
In the News
- Ten Faculty Named 2025 Bass Chairs (Apr 22, 2025 | Duke Today)
- For Many Urban Residents, It’s Hotter Than Their Weather App Says (Jun 27, 2024…
- Eyes in the Sky Bring Good News on Trash Burning in the Maldives (Jul 14, 2023 …
- Students Find Interdisciplinary Exploration and Connection in Winter Breakaway …
- David Carlson: Engineering and Machine Learning for Better Medicine (Jan 9, 201…
Representative Publications
- Zhang, H., Z. Jiang, S. Zhang, L. Tu, and D. Carlson. “Scale-free and unbiased transformer with tokenization for cell type annotation from single-cell RNA-seq data (Accepted).” Pattern Recognition 168 (December 1, 2025). https://doi.org/10.1016/j.patcog.2025.111724.
- Zhou, Yanjie, Feng Zhou, Fengjun Xi, Yong Liu, Yun Peng, David E. Carlson, and Liyun Tu. “Efficient few-shot medical image segmentation via self-supervised variational autoencoder.” Medical Image Analysis 104 (August 2025): 103637. https://doi.org/10.1016/j.media.2025.103637.
- Calhoun, Zachary, Mike Bergin, and David Carlson. “Big, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks,” May 21, 2025. https://doi.org/10.5194/icuc12-491.
- Carlson, David E., Ricardo Chavarriaga, Yiling Liu, Fabien Lotte, and Bao-Liang Lu. “The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.” Journal of Neural Engineering 22, no. 2 (March 2025). https://doi.org/10.1088/1741-2552/adbfbd.
- Jain, V., A. Mukherjee, S. Banerjee, S. Madhwal, M. H. Bergin, P. Bhave, D. Carlson, et al. “A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow.” Atmospheric Environment 338 (December 1, 2024). https://doi.org/10.1016/j.atmosenv.2024.120798.