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DESCRIPTION:Elena Tuzhilina\, PhD\n\nAssistant Professor Department of Stat
 istical Sciences\, University of Toronto\n\nWHEN: Wednesday\, March 18\, 2
 026\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 McGill College Avenue\,
  Rm 1140\; Zoom\n	NOTE: Elena Tuzhilina will be presenting in-person at SPG
 H \n\nAbstract\n\nCanonical Correlation Analysis (CCA) is a fundamental mu
 ltivariate method for measuring associations between two datasets\, with a
 pplications in genomics\, neuroimaging\, public health\, and machine learn
 ing. In high-dimensional settings\, however\, classical CCA breaks down\, 
 and existing sparse approaches often require a difficult trade-off between
  computational efficiency and statistical guarantees. We introduce ECCAR\,
  a fast and provably consistent sparse CCA algorithm that resolves this te
 nsion. By reformulating CCA as a high-dimensional reduced-rank regression 
 problem\, we obtain consistent estimators with high-probability error boun
 ds while avoiding computationally intensive procedures such as Fantope pro
 jections. The resulting method is scalable\, projection-free\, and substan
 tially faster than competing approaches. We validate ECCAR through extensi
 ve simulations and demonstrate its practical utility on real-world data\, 
 including an Alcohol Use Disorder and the Autism Brain Imaging Data Exchan
 ge datasets\, where it uncovers reliable and interpretable multivariate as
 sociations.\n\nSpeaker Bio\n\nElena Tuzhilina is an Assistant Professor in
  the Department of Statistical Sciences at the University of Toronto. She 
 received her Specialist’s degree in Mathematics from Moscow State Universi
 ty in 2015 and her PhD in Statistics from Stanford University in 2022 unde
 r the supervision of Professor Trevor Hastie\, where she focused on develo
 ping scalable statistical methods for structured and high-dimensional data
  with applications in biology and medicine. Her research lies in applied s
 tatistics\, with methodological contributions centered on dimension reduct
 ion and latent space modeling. She develops statistical methods that facil
 itate the analysis of complex biomedical datasets\, with applications in a
 reas such as neuroscience\, conformation reconstruction\, and single-cell 
 data analysis.. elenatuzhilina.github.io/index.html \n
DTSTART:20260318T193000Z
DTEND:20260318T203000Z
SUMMARY:Efficient Canonical Correlation Analysis with Sparsity
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/efficient-canonic
 al-correlation-analysis-sparsity-371787
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