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X-WR-CALNAME:Helsinki Institute for Information Technology | HIIT
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X-WR-CALDESC:Events for Helsinki Institute for Information Technology | HIIT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20190103T140000
DTEND;TZID=Europe/Helsinki:20190103T150000
DTSTAMP:20200126T203832
CREATED:20181219T094905Z
LAST-MODIFIED:20181219T095012Z
UID:6178-1546524000-1546527600@www.hiit.fi
SUMMARY:CS Forum - Mikael Kuusela - Locally stationary spatio-temporal interpolation of Argo profiling float data
DESCRIPTION:Mikael Kuusela\nCarnegie Mellon University \nAbstract:\nArgo floats measure seawater temperature and salinity in the upper 2\,000 m of the global ocean. Statistical analysis of the resulting spatio-temporal data set is challenging due to its nonstationary structure and large size. We propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure. We also investigate Student-t distributed fine-scale variation as a means to account for non-Gaussian heavy tails in ocean temperature data. Cross-validation studies comparing the proposed approach with the existing state-of-the-art demonstrate clear improvements in point predictions and show that accounting for the nonstationarity and non-Gaussianity is crucial for obtaining well-calibrated uncertainties. I will conclude by briefly describing on-going work on using these methods to obtain improved estimates of ocean climate and dynamics. \nBio:\nDr. Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University where he develops statistical methods for analyzing large and complex data sets in the physical sciences. His recent work has had two focal points: 1) developing spatio-temporal interpolation methods for analyzing oceanographic data from Argo profiling floats and 2) uncertainty quantification in ill-posed inverse problems with applications to the unfolding problem at the Large Hadron Collider at CERN. He obtained his PhD in Statistics in July 2016 from EPFL in Lausanne\, Switzerland. He then moved to the US where he was a postdoc at the University of Chicago and at SAMSI before joining Carnegie Mellon in August 2018. His BSc and MSc degrees are in Engineering Physics and Mathematics from Aalto University. \nHost:\nArno Solin\nAssistant Professor in Machine Learning\nDepartment of Computer Science\, Aalto University \n
URL:https://www.hiit.fi/event/cs-forum-mikael-kuusela-locally-stationary-spatio-temporal-interpolation-of-argo-profiling-float-data/
LOCATION:Aalto University\, Computer Science building\, Lecture room T3
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