Difference between revisions of "SOCR News HDDA 2024"

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(Created page with "== SOCR News & Events: 2024 HDDA Special Session on ''Data Science, Artificial Intelligence, and High-Dimensional Spatiotemporal Dynamics'' == ==Overview== D...")
 
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* '''Conference''': [https://sites.google.com/essec.edu/hdda-xiii/ 2024 High-Dimensional Data Analysis (HDDA-13)] (Organizer: [https://brocku.ca/mathematics-science/mathematics/directory/syed-ejaz-ahmed/ S. Ejaz Ahmed (Brock University)]).
 
* '''Conference''': [https://sites.google.com/essec.edu/hdda-xiii/ 2024 High-Dimensional Data Analysis (HDDA-13)] (Organizer: [https://brocku.ca/mathematics-science/mathematics/directory/syed-ejaz-ahmed/ S. Ejaz Ahmed (Brock University)]).
 
* '''Session Format''':  Three talks (details coming up).
 
* '''Session Format''':  Three talks (details coming up).
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==Session Presenters==
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* [https://my.linkedin.com/in/eric-tatt-wei-ho-2388709 Eric Tatt Wei Ho], [https://uevent.utp.edu.my/all-you-need-to-know/ CAPE and Department of Statistics], Universiti Teknologi Petronas, Malaysia.
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* ...TBD...
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* [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov], [https://www.socr.umich.edu/ Statistics Online Computational Resource], University of Michigan.
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Revision as of 10:01, 5 October 2023

SOCR News & Events: 2024 HDDA Special Session on Data Science, Artificial Intelligence, and High-Dimensional Spatiotemporal Dynamics

Overview

Data analysis methods are rapidly evolving due to the significant expansion of the size and complexities of data and the proliferation of new technologies. From social media networks to public health, bioinformatics to personalized medicine, environmental studies to nanoscience, and even financial analysis, diverse domains are facing new challenges. This surge has not only been confined to academic research; rather, it has permeated the practical spheres of businesses and governmental entities. As a response to this evolving landscape, there is an imperative to craft novel algorithms that can effectively scale with the dimensions of these datasets. In parallel, the development of new theoretical tools is essential to comprehend the statistical properties inherent to these algorithms. Promising breakthroughs in this realm encompass techniques such as variable selection, penalized methods, and variational inference, marking the frontier of advancements in data analysis and interpretation.

Since its inception in 2011 at the Fields Institute in Toronto, HDDA gathers leading researchers in the area of high-dimensional statistics and data analysis. The objectives include: (1) to highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advance of both mathematical and statistical sciences, (2) to identify important directions for future research in the theory of regularization methods and variational inference, in algorithmic development, and in methodology for different application areas, facilitate collaboration between theoretical and subject-area researchers (econometrics, finance, social science, biostatistics), and (3) to provide opportunities for highly qualified personnel to meet and interact with leading researchers in the area.

Session Logistics

Session Presenters





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