Date/Time

Date(s) - 10/20/2020
4:30 pm - 5:30 pm

Please join us for a UW Data Science Seminar event on Tuesday, October 20th from 4:30 to 5:30 p.m. The seminar will feature two early career data science researchers from UW share their latest work: Catherine Kuhn (Environmental Sciences) and Lalit Jain (Business).

Please use this zoom link for the event.

Catherine Kuhn and Lalit Jain

Catherine Kuhn: “No evidence of arctic-boreal lake greening”

Arctic and boreal ecosystems hold the highest concentration of the world’s lakes and are also undergoing the most rapid warming. Lake color is a designated Essential Climate Variable, but so far few studies have investigated changes in lake color at the global scale. Many global studies have used satellite observations to identify greening and browning trends associated with vegetation and carbon cycle dynamics in terrestrial ecosystems, yet changes in lake surface color have yet to be established at pan-arctic scales. Here we present decadal trends in arctic and boreal lake color derived using the high-resolution Landsat archive. We calculated annual growing season lake color from 1984 – 2019 for ~400,000 lakes and discovered overall declines in lake greenness over this 35-year period. Using ERA5 climate reanalysis data, we show that declines in lake greenness are 2.5 times greater in areas with enhanced warming and precipitation. In high northern latitudes, warmer and wetter conditions increase connectivity between lakes and the land surface, resulting in browning trends that are more apparent over continuous permafrost regions. In certain regions, however, lakes are greening. The observed shifts suggest that lake color of arctic and boreal lakes is undergoing significant changes in a warming climate.

Lalit Jain: “Applications of Adaptive Experimentation”

Scientific discovery is driven by a researchers ability to collect high quality data relevant to either verifying or disproving a hypothesis as quickly as possible. In recent years, a paradigm addressing this problem known as adaptive experimental design (AED) has been gaining traction. AED uses past measurements to inform the researcher what future measurements they should collect in a closed loop. In practice, AED has the potential to guide researchers to a conclusion with far fewer samples than any fixed data collection scheme. In this talk, we discuss some recent AED methods for best arm identification, and multiple hypothesis testing. We will also discuss a variety of applications including deciding the best caption for a cartoon, choosing amino acids sequences that form stable proteins, and running thousands of A/B tests on a large scale web platform.

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.