Brian is a Software Engineer at MBARI working in the Information Engineering group of the Research and Development division. He has a bachelor’s degree in Biology from the University of Maryland, College Park and a master’s degree in Marine Science/Physical Oceanography from Moss Landing Marine Laboratories.

Brian has developed software systems supporting science at Moss Landing Marine Laboratories, the Naval Post-graduate School and at MBARI. Employed at MBARI since 1998, he has worked on numerous projects involving video and image analysis, video annotation, numerical analysis, user-interface development, and data-management systems.

Bitbucket | ClickUp | GitHub | JIRA

Canyon Head | MBARI Documentation | Staff Directory | Safari Books Online

Video Annotation Data Management (500055)

MBARI has collected over 27,000 hours of underwater video since 1984. Working alongside the research and video staff, Brian develops systems for assisting MBARI staff with managing and analyzing this video data. MBARI’s video management and annotation tools are open-source and freely available to other researchers.

MBARI provides a Deep-Sea Guide for identifying and characterizing deep-sea organisms. As the lead engineer, Brian provides support, development and updates to the Deep-Sea Guide.

For more information about running MBARI’s M3/VARS System, go to

Midwater Time Series (901221)

Between the surface of the sea and the ocean floor lies a vast fluid universe, Earth’s least-known environment. MBARI has sophisticated systems that have spent thousands of hours surveying and describing the deep waters of the ocean. In support of MBARI’s Midwater lab, Brian develops tools, technology, and analytical techniques for working with this large collection of data.

Video Technical Advisory Group (901603)

This group researches and evaluates new developments and innovations in deep learning and machine vision as applied to underwater image and video analysis.

Pelagic-Benthic Coupling (901618)

This project studies ecological responses of marine communities in extreme environments to changes in climate and carbon cycling. Brian provides tools for managing and analyzing the large amounts of imaging data collected for this research.

FathomNet (902002)

As the volume and velocity of ocean data increases, new tools and techniques need to be established to process and integrate this data. For image and video, the volume of data captured can quickly outpace researchers’ abilities to process and analyze them. Machine learning holds the promise of enabling fast sophisticated analysis of this data. However, a lack of high quality, well-curated imagery of deep-sea organisms limits the usefulness of current machine-learning techniques. To address this need, MBARI has built FathomNet, a database of expertly curated imagery that can be used for training machine learning systems.

Additional FathomNet resources:

– GitHub
– Medium
– Slack

VARS Annotation Assistance (902005)

MBARI has robust and sophisticated systems for managing and annotation video and imagery. However, analyzing video by hand is time-consuming and requires expertise in deep-sea animal identification. In 2022, we will begin using machine learning to assist our researchers with their video analysis. The machine learning pipelines will be integrated with our existing annotation systems, allowing researchers to rapidly search, correct, and enhance the results.

Telepresence (902100)

In order to protect the health of researchers during the COVID-19 pandemic, MBARI is developing telepresence capabilities. Telepresence allows researchers to remotely participate in research cruises from the safety of home.

Katija, K., Orenstein, E., Schlining, B., Lundsten, L., Barnard, K., Sainz, G., Boulais, O., Woodward, B. and Bell, K.C., 2021. FathomNet: A global underwater image training set for enabling artificial intelligence in the ocean. arXiv preprint arXiv:2109.14646.

Katija, K., Schlining, B., Lundsten, L., Barnard, K., Sainz, G., Boulais, O., Woodward, B. and Bell, K.C., 2021. FathomNet: An Open, Underwater Image Repository for Automated Detection and Classification of Midwater and Benthic Objects. Marine Technology Society Journal55(3), pp.136-137.

Boulais, O., Woodward, B., Schlining, B., Lundsten, L., Barnard, K., Bell, K.C. and Katija, K., 2020. FathomNet: An underwater image training database for ocean exploration and discovery. arXiv preprint arXiv:2007.00114.

Schlining, K., Von Thun, S., Kuhnz, L., Schlining, B., Lundsten, L., Stout, N.J., Chaney, L. and Connor, J., 2013. Debris in the deep: Using a 22-year video annotation database to survey marine litter in Monterey Canyon, central California, USA. Deep Sea Research Part I: Oceanographic Research Papers79, pp.96-105.

Paull, C.K., Schlining, B., Ussler, W.I.I.I., Lundste, E., Barry, J.P., Caress, D.W., Johnson, J.E. and McGann, M., 2010. Submarine mass transport within Monterey Canyon: Benthic disturbance controls on the distribution of chemosynthetic biological communities. In Submarine mass movements and their consequences (pp. 229-246). Springer, Dordrecht.

Schlining, B.M. and Stout, N.J., 2006, September. MBARI’s video annotation and reference system. In OCEANS 2006 (pp. 1-5). IEEE.

Paull, C.K., Schlining, B., Ussler III, W., Paduan, J.B., Caress, D. and Greene, H.G., 2005. Distribution of chemosynthetic biological communities in Monterey Bay, California. Geology33(2), pp.85-88.

Graybeal, J., Gomes, K., McCann, M., Schlining, B., Schramm, R. and Wilkin, D., 2003, June. MBARI’s SSDS: operational, extensible data management for ocean observatories. In 2003 International Conference Physics and Control. Proceedings (Cat. No. 03EX708) (pp. 288-292). IEEE.

Drazen, J.C., Goffredi, S.K., Schlining, B. and Stakes, D.S., 2003. Aggregations of egg-brooding deep-sea fish and cephalopods on the Gorda Escarpment: a reproductive hot spot. The Biological Bulletin205(1), pp.1-7.

Chavez, F.P., Pennington, J.T., Castro, C.G., Ryan, J.P., Michisaki, R.P., Schlining, B., Walz, P., Buck, K.R., McFadyen, A. and Collins, C.A., 2002. Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Progress in Oceanography54(1-4), pp.205-232.

Chavez, F.P., Strutton, P.G. and Schlining, B.M., 2001. Bio-Optical Measurements at Ocean Boundaries in Support of SIMBIOS. SIMBIOS Project 2000 Annual Report, p.51.

Schlining, B., 1999. Seasonal intrusions of equatorial waters in Monterey Bay and their effects on mesopelagic animal distributions (Master’s thesis, California State University, Stanislaus).