Department of Biostatistics
Colloquium Series

2015 - 2016


Organizer: Sebastien Haneuse
Coordinator: Meagan Plante

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September 24 - (Kresge G2, 4:00 - 5:30 pm)

Myrto Lefkopoulou Distinguished Lecture

Debashis Ghosh, Ph.D.
Professor and Chair of the Department of Biostatistics and Informatics Colorado School of Public Health, University of Colorado Anschutz Medical Campus


"Kernel Machines: Back to the Future"

ABSTRACT: In this talk, I will discuss a class of statistical methods for high-dimensional data that are termed kernel machines. While they have been popularized in the machine learning and have found tremendous utility in various genomics contexts recently, in fact the mathematics that underlies the procedures date back to over 100 years ago. In this talk, I will give a brief history of the development of kernel machines and show that one key property that arises is that of a metric. Given the availability of a metric for any particular data structure, a straightforward development of theory for testing for associations using kernel machines is available. The methodology is fairly generic and can be applied to a wide variety of fields. In this talk, we will describe applications of kernel machines to problems in multivariate genomic data fusion, metabolomics and neuroimaging genomics. Time permitting, we will discuss an approach to power considerations for Gaussian kernel machines.
November 12

Brian Reich, Ph.D.
Associate Professor, Department of Statistics, North Carolina State University

"Policy optimization for dynamic spatiotemporal systems"
ABSTRACT: Interventions performed in space and time subject to resource constraints are common in ecology and many other fields. For example, we consider intervention strategies to slow the spread of white nose syndrome (WNS) in hibernating bats. WNS has dire consequences for both the bat population and agriculture production in affected areas. A policy is required to determine where and when interventions such as cave closings should be implemented. Finding an optimal policy in this case is challenging because data are sparse, disease dynamics are complex, and the state and action spaces are extremely high dimensional. We propose a general framework for policy optimization in dynamic spatiotemporal systems. The key features of our approach are that it ensures an interpretable policy, exploits scientific knowledge of the disease, adapts to changes in the system, properly accounts for many sources of uncertainty, and can be applied to high-dimensional problems. In our analysis of WNS, we show that the proposed approach can lead to substantial improvements over competing methods.
December 10

Michael G. Hudgens, Ph.D.
Associate Professor, Department of Biostatistics and Director, Center for AIDS Research (CFAR) Biostatistics Core, University of North Carolina

"Causal Inference in the Presence of Interference"
ABSTRACT: A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this talk we will discuss recent approaches to assessing treatment effects in the presence of interference. Inference about different direct and indirect (or spillover) effects will be considered in a population where individuals form groups such that interference is possible between individuals within the same group but not between individuals in different groups. An analysis of an individually-randomized, placebo controlled trial of cholera vaccination in 122,000 individuals in Matlab, Bangladesh will be presented which indicates a significant indirect effect of vaccination.
January 28

Neil Shephard, Ph.D.
Department Chair and Professor of Economics and of Statistics, Harvard University

"Moment Conditions and Bayesian Nonparametrics"
ABSTRACT: Models phrased though moment conditions are central to much of modern inference. Here these moment conditions are embedded within a nonparametric Bayesian setup. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools using Hausdorff measures to analyze them on real and simulated data. These new methods can be applied widely, including providing Bayesian analysis of quasi-likelihoods, linear and nonlinear regression, missing data and hierarchical models.
February 25

Jeffrey Leek, Ph.D.
Associate Professor, Biostatistics, Bloomberg School of Public Health, Johns Hopkins University

"Talk Title TBA"
ABSTRACT: None Given.
March 24

Daniela Witten, Ph.D.
Associate Professor, Department of Biostatistics, University of Washington

"Talk Title TBA"
ABSTRACT: None Given.
April 21

Mitchell H. Gail, Ph.D.
Senior Investigator, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute National Institutes of Health

"Talk Title TBD"
ABSTRACT: None Given
May 19

Ziv Bar-Joseph, Ph.D.
Professor, Machine Learning Department & Computational Biology Department, School of Computer Science, Carnegie Melon University

"Talk Title TBD"
ABSTRACT: None Given


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Last Update: January 11, 2016