Date(s) - 03/31/2021
4:30 pm - 5:30 pm

Please use this zoom link for the event.

An example of a temperature section collected by a glider showing the zig-zag sampling, and examples of temperature filaments extending across depth.

Please join us for a UW Data Science Seminar event on Wednesday, March 31st from 4:30 to 5:30 p.m. The seminar is the first of three seminars that feature projects from this year’s Winter Incubator program.

2021 Winter Incubator projects

The goal of our Winter Incubator program is to enable new science by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Our team of data scientists provides expertise in state-of-the-art technology and methods in statistics and machine learning, data manipulation and analytics at all scales, cloud and cluster computing, software design and engineering, visualization, and other topics. Read more about the 2021 projects here.

“Mapping and Visualizing Ocean Gliders to Observe Submesoscale Flows” – Dhruv Balwada, School of Oceanography, College of the Environment

Abstract: Since the industrial revolution 25-30% of the human-created carbon and 90% of the excess heat in the earth system has been sequestered into the deep ocean. A lot of this tracer (like heat, carbon and oxygen) transport from the surface into the interior takes place synoptically in narrow filaments (sub-mesoscale flows), which then merge and mix together at depth to result in a mean increase in amount of tracer at depth. To study the dynamics of these structures we need to make observations that span the depth of the water column and are collected at scales of a few kilometers and hours. This is possible using gliders, which profile the ocean on a zig-zag path as the move up and down through the water column.

The goal of this project is to develop tools to better explore these glider data sets. In particular we want to:

  • Develop a mapping algorithm to map from the scattered space-time observations collected by the glider to a grid, which is easier to visualize and conduct analysis on, and also respects the structural properties of the fields.
  • Develop a visualization dashboard for the glider, which allows for an interactive analysis of the data such as co-locating multiple variables to get a deeper insight into how observed structures might be generated.

“Detecting Wildflowers in Spectral Imagery” – Aji John, Department of Biology

Abstract: Alpine wildflowers are an integral part of montane ecosystems; they provide a wide variety of ecosystem services like pollination and nutrient recycling. Numerous studies have found that these wildflower species are sensitive to climate warming as their flowering phenology (development stage) is strongly related to snowmelt. To understand the effects of climate change on these vulnerable wildflowers, records of various stages of development are required: MeadoWatch is one of the citizen science initiatives run by Janneke Hille Ris Lambers (JHRL) lab at UW that has spearheaded the effort of documenting stages for the past 8 years. Volunteers visit the sites along two popular trails on the south and east side of Mt. Rainier from bud break to post flowering. The program has so far been successful in raising awareness of climate change on wildflowers and being a natural history conduit to staff at Mt. Rainier National Park.

At the beginning of 2020, a related initiative was started whereby field images of meadow flowers were captured alongside hyperspectral imagery from hoisted drones. The goal was first to improve remotely sensed phenology detection of these meadows, and secondly to complement citizen science observations by capturing finer spectral signatures of meadow flowers such that it can be then cross-evaluated with imagery from satellite providers (e.g., Planet, Sentinel-2 and Landsat 8). However, to understand and derive field observed spectral signatures, there is a need to delineate and demarcate flowers from heterogeneous backgrounds (e.g., trees, leaves, soil, rocks, etc.). We plan to delineate meadow flowers from complex backgrounds like rocks, soil and leaves using Convolutional Neural Nets (CNN) as they have shown promising results on ImageNet-based datasets. The results would assist in better prediction of wildflower season which can complement citizen science efforts and park visitor management.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.