Thinking About Data: Representations, Transformations, and Applications

Zavala, Victor M.

Title:

Thinking About Data: Representations, Transformations, and Applications

dc.contributor.author	Zavala, Victor M.
dc.contributor.corporatename	Georgia Institute of Technology. School of Chemical and Biomolecular Engineering	en_US
dc.contributor.corporatename	University of Wisconsin--Madison	en_US
dc.date.accessioned	2021-10-23T03:02:49Z
dc.date.available	2021-10-23T03:02:49Z
dc.date.issued	2021-10-13
dc.description	Presented online October 13, 2021 from 3:30 p.m.- 4:30 p.m.	en_US
dc.description	Victor M. Zavala is the Baldovin-DaPra Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison and a computational mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He holds a B.Sc. degree from Universidad Iberoamericana and a Ph.D. degree from Carnegie Mellon University, both in chemical engineering. He is on the editorial board of the Journal of Process Control, Mathematical Programming Computation, and IEEE Transactions on Control Systems and Technology. He is a recipient of NSF and DOE Early Career awards and of the Presidential Early Career Award for Scientists and Engineers. His research interests include computational modeling, statistics, control, and optimization.	en_US
dc.description	Runtime: 63:23 minutes	en_US
dc.description.abstract	A dataset can be represented in different mathematical forms; for example, a micrograph can be represented as an image, as a matrix, as a graph (network), or as an intensity function. These representations are used to perform transformations of the data with the goal of extracting different types of features such as spatial patterns, geometrical patterns, correlations, principal components, gradients of light, and frequencies. These features contain key information that facilitate visualization and analysis, detection of abnormalities, and construction of predictive models. In this talk, we show how to use representations and transformations in innovative ways to analyze complex datasets arising in flow cytometry, liquid crystals, chemical processes, and molecular dynamics. We show how these tools can be used to design chemical sensors for the detection of contaminants in air and liquid mixtures, to predict reaction rates for acid-catalyzed reactions, to predict material properties from images, and to detect faults.	en_US
dc.format.extent	63:23 minutes
dc.identifier.uri	http://hdl.handle.net/1853/65395
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	School of Chemical and Biomolecular Engineering Seminar Series
dc.subject	Data	en_US
dc.subject	Machine learning	en_US
dc.subject	Engineering	en_US
dc.title	Thinking About Data: Representations, Transformations, and Applications	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	School of Chemical and Biomolecular Engineering
local.contributor.corporatename	College of Engineering
local.relation.ispartofseries	School of Chemical and Biomolecular Engineering Seminar Series
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