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Wu-Jung Lee

Senior Oceanographer






I am interested in the use of sound — by both human and animals — to observe and understand the environment. My research spans two primary areas: acoustical oceanography, where I develop and apply active acoustic sensing techniques to infer properties of the ocean interior; and animal echolocation, where I combine experimental and computational approaches to understand the closed-loop sensorimotor feedback in echolocating bats and dolphins. In both areas, I focus on two fundamental aspects for achieving high confidence active acoustic sensing: 1) sampling – what can we do to collect better information? and 2) inference – how do we make reliable interpretation of echo information? Under these overarching themes, I am working to expand acoustic sensing capability for marine ecosystem monitoring at large temporal and spatial scales, and use echolocating animals as biological models to inspire adaptive sampling strategies in an active acoustic context.

Department Affiliation



B.S. Electrical Engineering and Life Sciences, National Taiwan University, 2005

Ph.D. Oceanographic Engineering, Massachusetts Institution of Technology/Woods Hole Oceanographic Institution Joint Program in Applied Ocean Physics and Engineer, 2013

Wu-Jung Lee's Website



2000-present and while at APL-UW

Accelerating marine ecological research using OOI echo sounder data

Lee, W.-J., "Accelerating marine ecological research using OOI echo sounder data," in Ocean Observatories Initiative (OOI) Science Plan: Exciting Science Opportunities Using OOI Data, by OOI Facility Board, Narragansett, RI, 2021, 134 pp.

1 Jan 2021

Compact representation of temporal processes in echosounder time series via matrix decomposition

Lee, W.-J., and V. Staneva, "Compact representation of temporal processes in echosounder time series via matrix decomposition," J. Acoust. Soc. Am., 148, 3429-3442, doi:10.1121/10.0002670, 2020.

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1 Dec 2020

The recent explosion in the availability of echosounder data from diverse ocean platforms has created unprecedented opportunities to observe the marine ecosystems at broad scales. However, the critical lack of methods capable of automatically discovering and summarizing prominent spatio-temporal echogram structures has limited the effective and wider use of these rich datasets. To address this challenge, a data-driven methodology is developed based on matrix decomposition that builds compact representation of long-term echosounder time series using intrinsic features in the data. In a two-stage approach, noisy outliers are first removed from the data by principal component pursuit, then a temporally smooth nonnegative matrix factorization is employed to automatically discover a small number of distinct daily echogram patterns, whose time-varying linear combination (activation) reconstructs the dominant echogram structures. This low-rank representation provides biological information that is more tractable and interpretable than the original data, and is suitable for visualization and systematic analysis with other ocean variables. Unlike existing methods that rely on fixed, handcrafted rules, this unsupervised machine learning approach is well-suited for extracting information from data collected from unfamiliar or rapidly changing ecosystems. This work forms the basis for constructing robust time series analytics for large-scale, acoustics-based biological observation in the ocean.

Echo statistics associated with discrete scatterers: A tutorial on physics-based methods

Stanton, T.K., W.-J. Lee, and K. Baik, "Echo statistics associated with discrete scatterers: A tutorial on physics-based methods," J. Acoust. Soc. Am., 144, doi:10.1121/1.5052255, 2018.

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6 Dec 2018

When a beam emitted from an active monostatic sensor system sweeps across a volume, the echoes from scatterers present will fluctuate from ping to ping due to various interference phenomena and statistical processes. Observations of these fluctuations can be used, in combination with models, to infer properties of the scatterers such as numerical density. Modeling the fluctuations can also help predict system performance and associated uncertainties in expected echoes. This tutorial focuses on "physics-based statistics," which is a predictive form of modeling the fluctuations. The modeling is based principally on the physics of the scattering by individual scatterers, addition of echoes from randomized multiple scatterers, system effects involving the beampattern and signal type, and signal theory including matched filter processing. Some consideration is also given to environment-specific effects such as the presence of boundaries and heterogeneities in the medium. Although the modeling was inspired by applications of sonar in the field of underwater acoustics, the material is presented in a general form, and involving only scalar fields. Therefore, it is broadly applicable to other areas such as medical ultrasound, non-destructive acoustic testing, in-air acoustics, as well as radar and lasers.

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In The News

Echolocation is nature’s built-in sonar. Here’s how it works.

National Geographic, Liz Langley

From beluga whales to bats and even to humans, many animals make sounds that bounce back from objects to help with navigation and hunting.

3 Feb 2021

Big data and fisheries acoustics

ICES (International Council for the Exploration of the Seas) News

Big data is one of the next steps in the evolution of fisheries acoustics. These data provide unprecedented observations of the aquatic environment but with this abundance of data comes the costs of storage, access and discoverability, processing and analysis, and interpretation.

15 Sep 2020

Scientists unravel the ocean's mysteries with cloud computing

UW Information Technology, Elizabeth Sharpe

The OOI Cabled Array is delivering data on a scale that was previously not possible. More than 140 instruments are working simultaneously.

That’s why oceanographers teamed up with data and research computing experts to organize a unique event at the University of Washington in late August 2018 to help ocean scientists learn the computational tools, techniques, data management and analytical skills needed to handle this massive amount of data.

8 Nov 2018

More News Items

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center