Difference between revisions of "SOCR News"

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==2024==
 
==2024==
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* Sept 1, 2024: Ivo Dinov is [https://regents.umich.edu/files/meetings/07-24/2024-07-IV-1.pdf appointed by the UMich Board of Regents as the Henry P. Tappan Collegiate Professor (September 1, 2024 through August 31, 2029). The [https://bentley.umich.edu/legacy-support/um/umpresid.php second UMich President], [https://en.wikipedia.org/wiki/Henry_Philip_Tappan Dr. Henry P. Tappan] (1852-1863), was a [https://heritage.umich.edu/stories/tappans-end/ prolific writer who provided a vision for establishing the University of Michigan as a primer research intensive academic institution].
 
* August 28-30, 2024: Ivo Dinov is organizing a special session [[SOCR_News_HDDA_2024| Data Science, Artificial Intelligence, and High-Dimensional Spatiotemporal Dynamics]] at the 2024 High-Dimensional Data Analysis Conference (HDDA-13) in Singapore.
 
* August 28-30, 2024: Ivo Dinov is organizing a special session [[SOCR_News_HDDA_2024| Data Science, Artificial Intelligence, and High-Dimensional Spatiotemporal Dynamics]] at the 2024 High-Dimensional Data Analysis Conference (HDDA-13) in Singapore.
 
* July 8-10, 2024: (2024 NIGMS TWD PDO Biomedical Training Program Directors' Conference) [https://sites.udel.edu/grimesgroup/research-team/ Catherine Grimes] (Delaware) and [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] (Michigan) are co-Chairing a [https://events.faseb.org/event/dc343624-9195-4916-96f8-234b8c15b6f0/ Special Session of the T32 Predoctoral Program PDs/PIs].
 
* July 8-10, 2024: (2024 NIGMS TWD PDO Biomedical Training Program Directors' Conference) [https://sites.udel.edu/grimesgroup/research-team/ Catherine Grimes] (Delaware) and [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] (Michigan) are co-Chairing a [https://events.faseb.org/event/dc343624-9195-4916-96f8-234b8c15b6f0/ Special Session of the T32 Predoctoral Program PDs/PIs].

Revision as of 10:33, 17 July 2024

SOCR News, Events & Announcements - News & RSS Feeds

2024

This paper Statistical foundations of invariance and equivariance in deep artificial neural network learning was recognized as being of remarkable quality by the AmStat/SMI review committee.

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