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DESCRIPTION:Camila P. E. de Souza\, PhD\n\nAssociate Professor\, Department
  of Statistical and Actuarial Sciences\n	University of Western Ontario\n	\n	N
 OTE: Meet & Greet Camila de Souza from 3-3:30pm in Room 1140\n\nWHEN: Wedn
 esday\, November 5\, 2025\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 M
 cGill College Avenue\, Rm 1140\; Zoom\n	NOTE: Camila de Souza will be prese
 nting in-person at SPGH\n	 \n\nAbstract\n\nVariational Inference is a metho
 d for analytically approximating the posterior distribution in Bayesian mo
 dels\, offering a more computationally efficient alternative to Markov Cha
 in Monte Carlo (MCMC) sampling techniques. In this talk\, I will present w
 ork from two recent publications\, co-authored with my students and collab
 orators. The first paper applies VI to functional data clustering\, where 
 the goal is to identify groups of curves without prior group membership in
 formation. Using a B-spline regression mixture model with random intercept
 s\, we developed a novel variational Bayes (VB) algorithm for simultaneous
  clustering and smoothing of functional data. The second paper focuses on 
 survival data analysis\, proposing a VB algorithm for inferring the parame
 ters of the log-logistic accelerated failure time model by incorporating a
  piecewise approximation technique to address intractable calculations and
  achieve Bayesian conjugacy. In both papers\, we conducted extensive simul
 ation studies to assess the performance of the proposed VB algorithms\, co
 mparing them with other methods\, including MCMC algorithms. Applications 
 to real data illustrate the practical use of the methodologies. The propos
 ed VB algorithms demonstrate excellent performance in clustering functiona
 l data and analyzing survival data while significantly reducing computatio
 nal costs compared to MCMC methods. The links to the papers are as follows
 : https://doi.org/10.1007/s11634-024-00590-w and https://doi.org/10.1007/s
 11222-023-10365-6.\n	\n	Speaker Bio\n\nDr. Camila de Souza is an Associate P
 rofessor at the Department of Statistical and Actuarial Sciences at the Un
 iversity of Western Ontario. Before joining Western\, Dr. de Souza was a p
 ostdoctoral fellow at the Shah Lab for Computational Cancer Biology at the
  BC Cancer Agency Research Centre. She completed her PhD in Statistics at 
 the University of British Columbia (UBC). She is originally from Brazil\, 
 where she received her Master’s and Bachelor’s Degrees in Statistics at th
 e University of Campinas. Her research program consists of developing new 
 statistical methods to analyze large and complex data structures arising f
 rom various areas in the Natural Sciences\, Health\, and Engineering. Dr. 
 de Souza conducts research on techniques involving clustering\, hierarchic
 al mixture models\, mixed-effects models\, hidden Markov models\, non-para
 metric regression\, semi-parametric models\, the expectation-maximization 
 (EM) algorithm\, and Bayesian variational inference. Her website is https:
 //www.desouzacpe.com/\n	 \n
DTSTART:20251105T203000Z
DTEND:20251105T213000Z
SUMMARY:Advancing Functional Data Clustering and Survival Analysis with Var
 iational Inference
URL:/spgh/channels/event/advancing-functional-data-clu
 stering-and-survival-analysis-variational-inference-368423
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