Meeting summaries and links in reverse chronological order.
Gage-Cam is a low cost, custom built wireless web camera paired with a custom deep learning algorithm that allows for a computer vision method to measure water surface elevation (stage). This project is a joint venture between Web Informatics and Mapping and the New York Water Science Center. Today's topic will be a short presentation on Gage-cam's design, capabilities, and prototyping. This will be followed by an open forum discussion on the technology and engineering behind the sensor, emerging methods in AI and single board "Lite-Tech" based devices researched by NYWSC-AI.
Daniel Beckman holds a bachelors degree from the University of Colorado in Ecology and Evolutionary Biology and minors in Chemistry and Computer Science. He currently attends Graduate school at the University of Colorado School of Engineering and Applied Science where he studies Machine Learning and Artificial Intelligence. He has worked in data for almost two decades in a variety of fields including, counterintelligence, research & development, forensic chemistry, and genomics. Daniel joined the USGS in 2017 and WIM in 2018. Currently, he works in cloud integration.
Natalya Rapstine will give an overview of new USGS Tallgrass supercomputer designed to support machine learning and deep learning workflows at scale and deep learning software and tools for data science workflows.
Natalya Rapstine is a computational scientist in the Science Analytics and Synthesis (SAS), Advanced Research Computing (ARC) group of the Core Science Systems. She has a bachelor degree in Earth Science from Rice University and a MS in Statistics from Colorado School of Mines, and she has been with the USGS since 2016. Her expertise is in high performance computing, machine and deep learning applications for advancement of science at U.S. Geological Survey.