Dear Colleagues, The 2018 Ocean Sciences Meeting <http://osm.agu.org/2018/> will take place 11-16 February 2018 in Portland, Oregon. The meeting is an important venue for scientific exchange across broad marine science disciplines, with sessions on all aspects of oceanography. We kindly invite you to submit an abstract <https://agu.confex.com/agu/os18/is/papers/index.cgi?sessionid=28080> to the Ocean Sciences 2018 Session entitled *Machine learning in biologial oceanography*. A description of the session can be found here <https://agu.confex.com/agu/os18/preliminaryview.cgi/Session28080>and at the end of this message. Dedicated to the applications of machine learning in biological oceanography the session will be a great opportunity to discuss and contrast the use of machine learning techniques in the wider realm of biological oceanography with its particular use for detection, classification and localization of marine mammals sounds, but also for automated visual detection, classification, recognition and identification. Please consider submitting and attending the meeting. Abstracts are due by 6 September 2017. with our best regards, *Eric Coughlin Orenstein* (Primary Chair), University of California - San Diego, Scripps Institution of Oceanography, San Diego, CA, USA *Jessica Luo* (Co-chair), National Center for Atmospheric Research, USA*John Burns* (Co-chair), University of Hawai'i, Hawai'i Institute of Marine Biology, Papaikou, HI, USA*Ludwig Houegnigan *(Co-chair), Polytechnic University of Catalonia, Department of Signal Theory and Communications, Barcelona, Spain ——————————— Session ID: 28080 Session Title: Machine learning in biologial oceanography Topic Area: Ocean Data Management Session Description: Recent technological advances in instrumentation and computing have allowed scientists across all disciplines to collect an unprecedented amount of data. Biological oceanographers in particular are now faced with vast datasets that stymie traditional analysis methods. Scientists are increasingly leveraging machine learning (ML) techniques to process and analyze these information rich datasets. ML algorithms are designed to learn from one dataset to make accurate predictions about a new, independent one. While specific application domains might be quite different, the ML approaches used for analysis are often very similar. This session therefore aims to (1) identify new ML methods or applications, (2) examine overlap in disciplines applying similar ML techniques, (3) facilitate discussion and interdisciplinary collaborations among ML practitioners in the ocean science community, and (4) identify gaps and specific needs for oceanographers using ML. The session chairs welcome any submission detailing work on ML methods for ecological data analysis and inference in aquatic systems. The session is intended to have a broad scope and we invite abstracts from diverse fields such as imaging, acoustics, genomics, and modeling.