Dr. Alexander Dudchenko is a postdoc at Stanford University in the Department of Civil and Environmental Engineering. His current work focuses on application of machine learning methods for estimating performance metrics of membrane processes and developing neural networks for on-line control and optimization of water treatment technologies. His previous work as a postdoc at Carnegie Mellon University focused on elucidating the mechanisms responsible for fouling resistance of chemically heterogeneous surfaces with nanoscale structures and developing methods for studying non-ideal heat transfer in membrane distillation. He earned his Ph.D. in Chemical and Environmental Engineering from University of California, Riverside, where he leveraged the unique conductive and surface energy properties of nanomaterials to improve high salinity water, produced water, and wastewater treatment processes. His commitment to solving and innovating water treatment process was recognized by the Young Membrane Scientist Award from the North American Membrane Society and the NSF Integrative Graduate Education and Research Traineeship Program fellowship. He earned his B.S. in Chemical Engineering from University of California, Riverside where he performed research on photocatalysts for water treatment applications.