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UID:252@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20250206T163000
DTEND;TZID=America/Los_Angeles:20250206T172000
DTSTAMP:20250204T201014Z
URL:https://escience.washington.edu/events/uw-data-science-seminar-kasim-r
 afiq-and-medha-agarwal/
SUMMARY:UW Data Science Seminar: Kasim Rafiq and Medha Agarwal
DESCRIPTION:Please join us for the second talk in a special series in the U
 W Data Science Seminar featuring the AI@UW Seed Grant awardees. The AI@UW 
 Seed Grant projects partner domain researchers from an applied disciplinar
 y field with researchers specializing in AI theory and/or methodology. The
 se projects were supported by the eScience Institute in collaboration with
  the Office of Research\, the Paul G. Allen School of Computer Science &am
 p\; Engineering\, the Information School\, and the NSF Institute for Found
 ations of Data Science (IFDS).  This seminar will feature Dr. Kasim Rafiq
 \, a postdoctoral researcher for the Center for Ecosystem Sentinels\, and 
 Medha Agrarwal\, a PhD student in the UW Department of Statistics on  Thur
 sday\, February 6th from 4:30 to 5:20 p.m. PT.\n\n\n\nThe seminar will be 
 held in Hitchcock Hall 132 – Campus Map.\n"Revealing the Hidden Lives o
 f Cryptic Carnivores with Machine Learning and AI"\nAbstract Animal-worn 
 accelerometers offer exciting opportunities to advance the study of animal
  behaviour and ecology across diverse taxa and research topics by remotely
  identifying specific behaviours. However\, challenges such as imbalanced 
 training datasets and unknown model uncertainties have hindered the broade
 r adoption of behaviour classification models within wildlife ecology. In 
 our talk\, we introduce the potential for behaviour classification to adva
 nce the study of wildlife ecology and introduce an open-source method for 
 classifying animal behaviour from raw acceleration data. Our approach inte
 grates machine learning and statistical inference techniques to evaluate a
 nd mitigate class imbalances\, changes in model performance across ecologi
 cal settings\, and noisy classifications. Using data from free-ranging Afr
 ican wild dogs in the Okavango Delta\, we then demonstrate the utility of 
 our approach in real-world settings and conclude with a discussion of futu
 re directions.\n\nBiography: Dr. Kasim Rafiq is a postdoctoral researcher 
 within the Abrahms Lab in the Center for Ecosystem Sentinels. His work foc
 uses on understanding the impacts of global change on the movements and be
 haviors of predators through a combination of fieldwork\, animal-worn sens
 ors\, and AI. Kasim is a Washington Research Foundation Fellow and eScienc
 e Postdoctoral Fellow.\n\n Medha Agarwal is a PhD student in the Departmen
 t of Statistics at the University of Washington. Her research specializes 
 in generative modeling\, optimal transport\, and the application of deep l
 earning.\n\n\nThe UW Data Science Seminar is an annual lecture series at
  the University of Washington that hosts scholars working across applied a
 reas of data science\, such as the sciences\, engineering\, humanities and
  arts along with methodological areas in data science\, such as computer s
 cience\, applied math and statistics. Our presenters come from all domain 
 fields and include occasional external speakers from regional partners\, g
 overnmental agencies and industry.\nThe 2024-2025 seminars will be held in
  person\, and are free and open to the public.
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