[Forum SIS] Seminar DANIEL KOWAL (Rice University) - Padova, Dec. 12, 2019 12.30 a.m.

[Patrizia Piacentini - Dip]

The Department of Statistical Sciences, University of Padova, invites 
you to the Seminar:

TITLE
SCALABLE BAYESIAN INFERENCE AND SUMMARIZATION FOR FUNCTIONAL DATA

SPEAKER
DANIEL KOWAL
Rice University

WHEN
December 12, 2019 - 12.30 a.m.

WHERE
Room Cucconi - Campus S. Caterina
Via C. Battisti, 241
Padova

ABSTRACT
**Modern scientific monitoring systems, such as wearable and implantable 
devices, commonly record data over a continuous domain at high 
resolutions. These /functional data/ are high-dimensional, strongly 
correlated, and usually measured concurrently with other variables of 
interest. Bayesians models for functional data are particularly 
appealing: they accommodate multiple dependence structures, handle 
missing or irregularly-spaced data, and provide regularization via 
shrinkage priors. However, these models are often complex, 
computationally intensive, and difficult to interpret. This talk will 
focus on two fundamental challenges for Bayesian functional data 
analysis: (1) constructing sufficiently flexible and scalable functional 
regression models and (2) extracting interpretable posterior summaries. 
The proposed modeling framework is nonparametric and uses an unknown 
functional basis to learn prominent functional features, which are 
associated with scalar predictors within a regression model. A 
customized projection-based Gibbs sampler provides posterior inference 
with linear time complexity in the number of predictors, which is 
empirically faster than existing frequentist and Bayesian alternatives. 
Using the posterior distribution, a decision theoretic approach for 
Bayesian variable selection is developed, which identifies a subset of 
covariates that retains nearly the predictive accuracy of the full 
model. The methodology is applied to actigraphy data to investigate the 
association between intraday physical activity and responses to a sleep 
questionnaire.

BIO
Dr. Daniel Kowal is an assistant professor in the Department of 
Statistics at Rice University. Dr. Kowal develops statistical 
methodology and algorithms for massive data sets with complex dependence 
structures, such as functional, time series, and spatial data. His 
recent work focuses on Bayesian models for prediction and inference, as 
well as scalable approximations to complex models. He received his PhD 
from Cornell University in 2017.

*Apologies for cross posting*

-- 
Patrizia Piacentini
Dipartimento di Scienze Statistiche
Via C. Battisti, 241
35121 Padova
tel 049 8274167
fax 049 8271524

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