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UID:379@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20261020T163000
DTEND;TZID=America/Los_Angeles:20261020T172000
DTSTAMP:20261015T221023Z
URL:https://escience.washington.edu/events/uw-data-science-seminar-dani-jo
 nes/
SUMMARY:UW Data Science Seminar: Dani Jones
DESCRIPTION:\nPlease join us for a UW Data Science Seminar featuring Univer
 sity of Michigan Research Faculty Dani Jones on Tuesday\, October 20th fro
 m 4:30 to 5:20 p.m. PT. The seminar will be held in HCK 132.\n“Informing
  Environmental Observing System Design with Machine Learning”\nAbstract:
  Designing environmental observing systems involves deciding where and whe
 n to collect data to answer scientific questions and address management ne
 eds\, often with limited resources and incomplete knowledge of the system.
  Drawing on work in physical oceanography and environmental science in Ant
 arctica and the North American Great Lakes\, this talk will explore how qu
 antitative methods and machine learning can help inform these decisions. I
  will discuss approaches to characterizing uncertainty and assessing the v
 alue of additional observations\, including recent work using DeepSensor\,
  a framework based on convolutional Gaussian neural processes\, to investi
 gate sensor placement in the Great Lakes. Together\, these examples illust
 rate how machine learning can help us make better use of environmental obs
 ervations and design observing systems that are more effective at addressi
 ng scientific questions.\n\nSpeaker Bio: Dani Jones (they/she) is Research
  Faculty at the University of Michigan’s Cooperative Institute for Great
  Lakes Research (CIGLR). Their background spans physical oceanography\, ap
 plied mathematics\, and data science. Before joining the University of Mic
 higan\, they spent ten years as a physical oceanographer at the British An
 tarctic Survey\, working on numerical modeling and observational data anal
 ysis. Their current research focuses on data-intensive approaches to Great
  Lakes science and management\, including the use of machine learning to i
 nform environmental observing-system design.\n\n\nThe 2026-2027 seminars w
 ill be held in person\, and are free and open to the public.\n\n\n\n\n\n\n
ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa
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