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About

R. Walters is a leading figure in time-domain astronomy, specializing in the development of machine learning and software infrastructure to handle the data deluge from modern sky surveys. Their major contributions center on automating the classification and follow-up of transient astrophysical events. Walters pioneered the use of deep learning for astronomical image analysis, most notably through the "braai" convolutional neural network for real-bogus classification at the Zwicky Transient Facility (ZTF), a tool cited over 158 times for its efficiency in separating genuine astrophysical transients from artifacts. They also co-developed the GROWTH Marshal, a dynamic science portal cited 155 times that has become an essential platform for coordinating global follow-up observations of transients. Further refining data quality, Walters created the "byecr" and "contsep" modules for the SEDMachine to remove cosmic rays and contaminating light. With a career spanning from early blazar variability studies to cutting-edge AI applications, Walters’ work is foundational to the high-throughput, automated discovery that defines modern time-domain astronomy.