David Carlson

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

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.