A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture together with the enormous data volumes collected to intelligently optimize agriculture outputs. We will consider the breadth and depth of challenges and possible approaches inherent to tackling the world’s largest optimization problem: optimizing global food production.
Erik leads The Climate Corporation Science and Research Organization, spanning research across research teams including Climatology, producing hyper-local weather forecasts, and Agronomic models, connecting hyper- local weather measurements to agronomic outcomes. Previously, Erik worked at several Bay Area start-ups solving large - scale Statistical Machine Learning problems. Erik holds a B.S. in Computer Science from Arizona State University and a PhD in Mathematics from University of Wisconsin - Madison.
Brian leads the The Climate Corporation's science & engineering teams. He has over 15 years of experience leading the development of large-scale systems in a variety of industries. Prior to The Climate Corporation, Brian worked at Orbitz where he was a Senior Architect responsible for the development of their distributed service platform and overall architecture. Before Orbitz, Brian worked at the investment bank UBS where he built global interest rate trading systems. He has contributed frequently to the open source community including having been the lead developer for the Jython project. He holds a B.S. in Finance from the University of Illinois at Champaign.
Open to All | Pizza will be provided!