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
  • Assistant Professor in Biostatistics & Bioinformatics
  • Associate Professor of Computer Science
  • Assistant 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

  • STA 613: Statistical Methods for Computational Biology
  • 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 540: Statistical Methods for Computational Biology

In the News

Representative Publications

  • 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.
  • Carson, William E., Samantha Major, Harshitha Akkineni, Hannah Fung, Elias Peters, Kimberly L. H. Carpenter, Geraldine Dawson, and David E. Carlson. “Model selection to achieve reproducible associations between resting state EEG features and autism.” Sci Rep 14, no. 1 (October 25, 2024): 25301. https://doi.org/10.1038/s41598-024-76659-5.
  • Walder-Christensen, Kathryn K., Jack Goffinet, Alexandra L. Bey, Reah Syed, Jacob Benton, Stephen D. Mague, Elise Adamson, et al. “Sleep-wake states are encoded across emotion-regulation regions of the mouse brain.” Cold Spring Harbor Laboratory, September 15, 2024. https://doi.org/10.1101/2024.09.15.613104.
  • 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 11, no. 8 (August 13, 2024): 845–50. https://doi.org/10.1021/acs.estlett.4c00296.
  • Isaev, Dmitry, Samantha Major, Kimberly L. H. Carpenter, Jordan Grapel, Zhuoqing Chang, Matias Di Martino, David Carlson, Geraldine Dawson, and Guillermo Sapiro. “Use of Computer Vision Analysis for Labeling Inattention Periods in Eeg Recordings With Visual Stimuli.” Springer Science and Business Media LLC, August 10, 2024. https://doi.org/10.21203/rs.3.rs-4637470/v1.