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)

Imagine navigating a library holding not books, but 27,000 hours of underwater footage – that’s the challenge MBARI researchers face. Spearheading efforts alongside the team and MBARI’s video lab, Brian designs innovative systems to tame this ocean of data. His tools don’t just streamline internal workflows; they democratize deep-sea exploration by being freely available to other researchers worldwide. Through his open-source efforts, Brian empowers scientists everywhere to unlock the secrets hidden within every frame of MBARI’s vast video archive, propelling our understanding of the ocean’s hidden depths.

Unlocking the mysteries of the deep sea is bit easier thanks to MBARI’s Deep-Sea Guide, an invaluable resource for identifying and characterizing its enigmatic inhabitants. 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

Benthic Biology & Ecology (901007) and Carbon Flux Ecology (902111)

Brian plays a role in two of MBARI’s core research programs analyzing still images. The first studies the vibrant diversity and ecological functions of deep-sea coral and sponge communities, vital residents of the Monterey Bay National Marine Sanctuary. The second program, unraveling the complex journey of biological carbon, tracks its transformation from the surface sunlit waters down through the ocean’s depths to the benthic communities of the seafloor

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.

FathomNet (902002) and Ocean Vision AI (701165)

As the ocean reveals its secrets through cameras and sensors, researchers face a mounting challenge: a data deluge. Imagine sifting through countless hours of video footage, many frames teeming with fascinating marine life. Processing and analyzing this ocean of information quickly outpaces researchers’ abilities, hindering our understanding of this vital ecosystem.

This is where machine learning offers a glimmer of hope. Its algorithms can learn to identify patterns and extract insights from vast datasets, promising fast and sophisticated analysis of ocean imagery and video. But there’s a snag: machine learning needs reliable teachers. Without high-quality, well-curated data, these algorithms are like students given blurry textbooks – their results are muddled and unreliable.

Enter FathomNet, MBARI’s solution to this data dilemma. FathomNet is a treasure trove of expertly labeled underwater imagery, meticulously curated by scientists who know the ocean like the back of their hand. Each image in this database is painstakingly annotated, providing reliable data to train machine learning models.

FathomNet is more than just a collection of pictures; it’s a training ground for the future of ocean exploration. By feeding these high-quality images to machine learning algorithms, MBARI is helping to develop next-generation tools that can analyze ocean data with unprecedented speed and accuracy. Imagine autonomous underwater vehicles navigating coral reefs, automatically identifying and tracking marine life, or deep-sea cameras sending back real-time insights as they explore the ocean’s hidden depths.

Additional FathomNet resources:

– GitHub
– Medium
– Slack

Piscivore Cam (902304)

Imagine a camera capturing the underwater ballet of a hungry school of tuna, their streamlined forms slicing through the blue waters of Monterey Bay. This isn’t science fiction; it’s the reality of the Piscivore Camera, a system developed by MBARI to study elusive marine predators in their natural habitat. Deployed on autonomous underwater robots or hitching a ride on robotic sailboat, the Piscivore collects a massive trove of video data. But sifting through hours of footage is like searching for a needle in a haystack – until machine learning steps in. MBARI’s algorithms scan the video with superhuman speed, pinpointing predators with uncanny accuracy. Brian tackles the next challenge: wrangling this deluge of information. He crafts tools that organize the video footage and streamline the interpretation of the AI’s findings, transforming the vast treasure trove of data into insights for researchers, unlocking the secrets of these magnificent creatures of the deep.

Expedition Database Modernization (902403)

Picture a lush garden, verdant and vital, nurtured through sun, rain, and the dedicated care of a skilled gardener. Just like that garden endures changing seasons and evolving needs, so too does software require constant care and adaptation. This is especially true for critical platforms like MBARI’s expedition database, the backbone of research ship activities for decades.

Think of the expedition database as a meticulously organized treasure trove. It holds every detail of past voyages, from the chief scientist and mission goals to the ship’s instantaneous position, ROV movements, logistical notes, and even high-frequency environmental data like salinity, temperature, and pressure. It’s a historical tapestry woven with data, recording the whispers of the ocean captured during thousands of expeditions.

But like any garden, even the most meticulously maintained needs an occasional refresh. Technology blossoms rapidly, and MBARI’s expedition database now requires a two-pronged approach: future-proofing for ongoing maintenance and evolving to meet the changing needs of researchers. This isn’t simply about replacing aging tools; it’s about revitalizing the garden, planting new seeds of innovation, and ensuring that generations of researchers can continue to harvest the fruits of past expeditions and chart new courses of discovery.

Marine Imaging Workshop (902404)

In 2024, MBARI will be hosting the 5th Marine Imaging Workshop from October 7th to 10th. Marine imaging is a major method in the science, policy and public understanding of the world’s oceans. The topic is developing rapidly, driven by the technological evolution and increasing application of marine imaging in all oceans. Images of all types are used to explore unseen ocean habitats, to motivate designation of marine conservation areas, for assessing environmental baselines and monitoring of human impacts and to communicate ocean narratives.

The international Marine Imaging Workshops assemble scientists and engineers from different disciplines to push the boundaries of marine imaging. Biologists, geologists, engineers, computer scientists and end-users will discuss topics ranging from the start to finish of marine image and video analysis. Topics include imagery collection, processing, still/video annotation, machine learning, image data management and much more.. 

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).