David Carlson

Assistant 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

  • Assistant 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: (919) 668-9680
  • Email Address: david.carlson@duke.edu


  • Ph.D. Duke University, 2015

Research Interests

Machine learning, predictive modeling, health data science, statistical neuroscience

Courses Taught

  • CEE 690: Advanced Topics in Civil and Environmental Engineering
  • CEE 702: Graduate Colloquium
  • CEE 780: Internship
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 899: Special Readings in Electrical Engineering
  • EGR 393: Research Projects in Engineering
  • ME 555: Advanced Topics in Mechanical Engineering

In the News

Representative Publications

  • 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. “Brain-wide oscillatory network encodes an aggressive internal state.” Cold Spring Harbor Laboratory, December 7, 2022. https://doi.org/10.1101/2022.12.07.519272.
  • Jiang, Z., T. Zheng, M. Bergin, and D. Carlson. “Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery.” Science of Remote Sensing 5 (June 1, 2022). https://doi.org/10.1016/j.srs.2022.100052.
  • Mague, Stephen D., Austin Talbot, Cameron Blount, Kathryn K. Walder-Christensen, Lara J. Duffney, Elise Adamson, Alexandra L. Bey, et al. “Brain-wide electrical dynamics encode individual appetitive social behavior.” Neuron 110, no. 10 (May 18, 2022): 1728-1741.e7. https://doi.org/10.1016/j.neuron.2022.02.016.
  • Bey, Alexandra L., Kathryn K. Walder-Christensen, Jack Goffinet, Elise Adamson, Noah Lanier, Stephen D. Mague, David Carlson, and Kafui Dzirasa. “6.28 Identifying Networks Underlying Sleep Disruption in Autism Spectrum Disorder Mouse Models.” In Journal of the American Academy of Child & Adolescent Psychiatry, 60:S167–S167. Elsevier BV, 2021. https://doi.org/10.1016/j.jaac.2021.09.101.
  • Dunn, Timothy W., Jesse D. Marshall, Kyle S. Severson, Diego E. Aldarondo, David G. C. Hildebrand, Selmaan N. Chettih, William L. Wang, et al. “Geometric deep learning enables 3D kinematic profiling across species and environments.” Nat Methods 18, no. 5 (May 2021): 564–73. https://doi.org/10.1038/s41592-021-01106-6.