Computational Oceanography Lab

The Computational Oceanography Lab uses tools like artificial intelligence, big data, and theory to understand how the ocean works, and to predict how it will change in the future. We work with the algorithms that connect human technology with the natural world, with the idea of using computation to benefit humanity in an equitable way.

Ocean Forecasting

The ocean is changing more and more rapidly, and one tool for adapting to changes is to forecast them ahead of time. We recently launched the Tandy Center for Ocean Forecasting, which works with partners to develop and deploy real-time ocean forecasting tools for industry, conservation, resource management, communities, and education.

Decoding the Ocean

Ocean science is awash in Big Data, from environmental DNA to automated sensors to community science programs. Decoding this information requires a combination of new theory and algorithms as well as interdisciplinary thinking. We work across disciplines, using data ranging from satellite images to tweets, and combining physical, mathematical, and theoretical models to solve both fundamental and applied problems in ocean science.

A People’s Science

Putting science into the hands of people--through community science, education, and outreach--empowers people to understand and confront environmental problems that affect them. From advising NOAA on species management to giving climate talks in churches and community centers, our lab is dedicated to making science accessible and useful to the people that it matters most to. We work with many community science programs and are always interested in engaging with new ones.

Lab Alums

• Camille Ross - Predicting regions of North Atlantic right whale, Eubalaena glacialis, habitat suitability in the Gulf of Maine in 2050. Elementa: Journal of the Anthropocene
• Riley Karp - Environmental DNA database
• Arthur Makumbi - Ocean data portals
• Charlie Cobb - Phenological linkages in the Gulf of Maine
• Anna Yu - Forecasting moose-vehicle collisions
• Isabella Grasso - The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine. Ecosphere
• Victoria Chistolini - Species Distribution Model Development and Validation
• Karen Stamieszkin - Incorporating Marine and Coastal Phenology Into The National Climate Assessment
• Dakota Young - Finding dominant modes in ocean precipitation phenology
• Anna Kronauer - Modeling ocean ecosystem: Forecasting the abundance of gelatinous zooplankton in the Gulf of Maine
• Caitlin Keady - Investigating chlorophyll-a anomalies in the Southern Ocean and the Tasman Sea
• Daniel Seidman - A model of population dynamics for the life stages of Salpa fusiforma in the Gulf of Maine
• Jule Middleton - The functional dynamics of Emiliania huxleyi virus-host interactions across multiple scales. Limnol. Oceanogr.
• Malik McKnight - Development of the deep diapausial Calanus layer in the Gulf of Maine.