Anchorage Office
Research Biologist
Dan Wexler, B.S.
Remote Sensing
Machine Learning
Scientific Programming
Dan is a geologist and computer scientist specializing in environmental remote sensing studies. His experience includes monitoring sea surface temperature trends using thermal satellite imagery, leveraging machine learning to detect and classify natural landscape disturbances, and assessing harbor seal habitat in Alaska with computer vision. He has a strong background working with imagery from a variety of satellite sensors, collecting and processing high-accuracy GNSS survey data, and programming to build models and conduct statistical analyses. Dan received a B.S. in geophysics and a B.A. in computer science from Brown University.
Select Publications
Ahmed, A., Fox-Kemper, B., Watkins, D.M., Wexler, D. and Wilhelmus, M.M., 2025. Estuarine temperature variability: Integrating four decades of remote sensing observations and in-situ sea surface measurements. Remote Sensing of Environment, 322, p.114643.
Daubar, I.J., Dundas, C.M., McEwen, A.S., Gao, A., Wexler, D., Piqueux, S., Collins, G.S., Miljkovic, K., Neidhart, T., Eschenfelder, J. and Bart, G.D., 2022. New craters on Mars: An updated catalog. Journal of Geophysical Research: Planets, 127(7), p.e2021JE007145.
Wagstaff, K.L., Daubar, I.J., Doran, G., Munje, M.J., Bickel, V.T., Gao, A., Pate, J. and Wexler, D., 2022. Using machine learning to reduce observational biases when detecting new impacts on Mars. Icarus, 386, p.115146.