This remote workshop, hosted by the Department's Research Training Group in Data Driven Discovery, features speakers from UA and other nearby institutions.  See https://math.arizona.edu/~klin/rtg/showcase21 for details, including Zoom information.

This is the keynote talk for the Data Driven Discovery Workshop, which begins at 10am on Wed 3/10.

This presentation will demonstrate how to use the 4-Dimensional Visual Delivery (4DVD) technology www.4dvd.org to explore climate dynamics, such as the weakening trade wind under the El Niño condition. 4DVD was developed at the Climate Informatics Lab, San Diego State University. The system visualizes and delivers netCDF climate data in a 4-dimensional space-time domain.  It allows users to quickly visualize the data before making a download for further analysis. Users can zoom in-and-out or other graphics options to help identify desired climate dynamics patterns. Data can eventually be downloaded for a spatial map of a given time and a historical climate time series of a given location after the map and time series are identified to be useful. In this way, the 4DVD database software enables a user to quickly reach the core interested climate features without downloading the entire dataset in advance. This not only saves time and storage space, but also helps quickly explore the climate dynamics from observed data or climate model output. As a demo, we will show three datasets: NOAA’s 20th Century Reanalysis model data, NASA’s Global Precipitation Climatology Project data, and NOAAGlobalTemp data.

Sam Shen is Distinguished Professor of Mathematics and Statistics at San Diego State University (SDSU), and Visiting Research Mathematician of Scripps Institution of Oceanography, UCSD. He was SDSU Mathematics and Statistics Department Chair from 2006 to 2011. Before joining SDSU in 2006, Dr. Shen was McCalla Professor of Mathematical and Statistical Sciences at the University of Alberta, Canada, and formerly President of the Canadian Applied and Industrial Mathematics Society. 

Dr. Shen received his B.Sc. in Engineering Mechanics in 1982 from East China Engineering Institute, and Ph.D. in Applied Mathematics from the University of Wisconsin-Madison in 1987.

Zoom:  https://arizona.zoom.us/j/94534134312   
Password:  “arizona” (all lower case)

Abstract:  Persistent homology, the flagship method of topological data analysis, can be used to provide a quantitative summary of the shape of data.  One way to pass data to this method is to start with a finite, discrete metric space (whether or not it arises from a Euclidean embedding) and to study the resulting filtration of the Rips complex.  In this talk, we will discuss several available methods for turning time series data into a a discrete metric space, including the Takens embedding, $k$-nearest neighbor networks, and ordinal partition networks.  Combined with persistent homology and machine learning methods, we show how this can be used to classify behavior in time series in both synthetic and experimental data.

Zoom:     https://arizona.zoom.us/j/91826900125   

Password:    "Locute"     

Abstract: We consider a simple model of a two-dimensional microswimmer with fixed swimming speed.  The direction of swimming changes according to a Brownian process, and the swimmer is interacting with boundaries.  The shape of the swimmer determines the range of allowable values that its degrees of freedom can assume --- its configuration space.  Using natural assumptions about reflection of the swimmer at boundaries, we compute the swimmer's invariant distribution across a channel consisting of two parallel walls, and the statistics of spreading in the longitudinal direction.  This gives us the effective diffusion constant of the swimmer's large scale motion.  This model offers insight into experiments of scattering of swimmers from boundaries, and serves as an exactly-solvable baseline when comparing to more complex models.  This is joint work with Hongfei Chen.

TBA

This is a series of NSF Noyce Seminars for the AZ Noyce Mathematics Teaching (MaTh) Scholars Project in Secondary Mathematics Education (PI: Cynthia Anhalt; Co-PI: Marta Civil; Co-PI: Rebecca McGraw; Co-PI: Jennifer Wolfe).  Please contact Jennifer Wolfe (jwolfe628@math.arizona.edu) if you are interested in attending.

TBA

TBA

TBA

This is a series of NSF Noyce Seminars for the AZ Noyce Mathematics Teaching (MaTh) Scholars Project in Secondary Mathematics Education (PI: Cynthia Anhalt; Co-PI: Marta Civil; Co-PI: Rebecca McGraw; Co-PI: Jennifer Wolfe).  Please contact Jennifer Wolfe (jwolfe628@math.arizona.edu) if you are interested in attending.