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+<WRAP centeralign>##Data Science Seminar##\\ Hosted by the Department of Mathematics and Statistics</WRAP>
+  * Date: Tuesday, November 15, 2022
+  * Time: 12:00pm -- 1:00pm
+  * Room: Whitney Hall 100E
+  * Speaker: Dr. Rachael Hageman Blair (University at Buffalo)
+  * Title: Harnessing stability estimation for module detection, clustering, and ensemble clustering.
+<WRAP center box 80%>
+<WRAP centeralign>**//Abstract//**</WRAP>
+ \\
+Clustering data is a challenging problem in unsupervised learning where no gold
+standard exists. The selection of a clustering method, measures of dissimilarity,
+parameters, and the determination of the number of reliable groupings, are often viewed
+as subjective processes. Stability has become a valuable surrogate to performance and
+robustness that can guide an investigator in selecting and prioritizing clusters. This talk
+presents a framework for stability measurements based on resampling and out-of-bag
+estimation. Bootstrapping methods for cluster stability can be prone to overfitting in a
+setting analogous to poor delineation of test and training sets in supervised learning.
+Out-of-bag stability, which overcomes this issue, is observed to be consistently more
+conservative than traditional measures and is uniquely not conditional on a reference
+clustering. Furthermore, out-of-bag stability estimates can be estimated at different
+levels: item level, cluster level, and as an overall summary, which has good interpretive
+value for the investigator. This framework is extended to develop stability estimates for
+determining the number of clusters (model selection) through contrasts with simulated
+reference data with no signal. Finally, new out-of-bag stability estimates are developed
+to address the problems of ensemble clustering and multi-modal clustering. Applications
+in the Biomedical Sciences are presented. Stability estimation can be implemented
+using the &quot;bootcluster &quot; package on the Comprehensive R Archive Network (CRAN).
+ \\
+Biography of the speaker: Dr. Hageman Blair is an Associate Professor in Biostatistics
+at The University at Buffalo. She received her PhD in 2007 in Applied Mathematics from
+Case Western Reserve University and trained as a post-doc in statistical genetics at
+The Jackson Laboratory in Bar Harbor, Maine. Her methodological research interests
+include Computational Biology, Mathematical Biology, Network Theory, Cluster Analysis
+and Stability. She maintains several collaborations across the Biomedical Sciences and
+School of Engineering. She serves as the Associate Director of Education in UB’s new
+Institute of Artificial Intelligence and Data Science, which is home to a PhD program and
+four interdisciplinary masters programs.
+</WRAP>