Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Tamara
Broderick <http://www.tamarabroderick.com/>*, a Professor of Electrical
Engineering and Computer Science at MIT. She will be presenting work
entitled *Feature allocations, probability functions, and paintboxes.* Please
find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
P.S.: You can now follow the workshop on Twitter at @gov3009
<https://twitter.com/gov3009>
Title: Feature allocations, probability functions, and paintboxes
Abstract: Clustering involves placing entities into mutually exclusive
categories. We wish to relax the requirement of mutual exclusivity,
allowing objects to belong simultaneously to multiple classes, a
formulation that we refer to as "feature allocation." The first step
is a theoretical one. In the case of clustering the class of
probability distributions over exchangeable partitions of a dataset
has been characterized (via exchangeable partition probability
functions and the Kingman paintbox). These characterizations support
an elegant nonparametric Bayesian framework for clustering in which
the number of clusters is not assumed to be known a priori. We
establish an analogous characterization for feature allocation; we
define notions of "exchangeable feature probability functions" and
"feature paintboxes" that lead to a Bayesian framework that does not
require the number of features to be fixed a priori. The second step
is a computational one. Rather than appealing to Markov chain Monte
Carlo for Bayesian inference, we develop a method to transform
Bayesian methods for feature allocation (and other latent structure
problems) into optimization problems with objective functions
analogous to K-means in the clustering setting. These yield
approximations to Bayesian inference that are scalable to large
inference problems.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Justin
Reich*, the Richard L. Menschel HarvardX Research Fellow in the Office of
the President and Provost at Harvard University. He will be presenting work
entitled *Massive Open Online Courses and the Science of Learning*. Please
find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
Title: Massive Open Online Courses and the Science of Learning
Abstract: Large-scale open online learning environments continuously record
learner activities: the 54 courses conducted by HarvardX and MITx in the
2013-2014 academic year had 1.1MM participants who recorded over a half a
billion actions. Increasingly, online learning platforms also support A/B
testing frameworks that allow for a variety of experimental designs. This
combination of data recording and experimentation opens up excited new
avenues for educational research. This talk will provide an overview of the
various research strands currently underway at HarvardX, inspired by social
psychology, behavioral economics, instructional design, computer science,
computational social science, and other fields. I will argue for three
important shifts in the future direction of online learning research: from
studies of engagement to research on learning, from siloed investigations
to institutional data-sharing, and from post-hoc observational studies to
more sophisticated research designs. One aim of the talk is to introduce
the Applied Statistics community to the data and research opportunities
available through HarvardX, and to encourage more faculty and graduate
students to begin new studies with these data and new collaborations with
us.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Kayhan
Batmanghelich*, a post-doc at the MIT Computer Science and Artificial
Intelligence Lab. He will be presenting work entitled *Joint Modeling
Imaging and Genetics: a Probabilistic Approach*. Please find the abstract
below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
Title: Joint Modeling Imaging and Genetics: a Probabilistic Approach
Abstract:
An increasing number of clinical and imaging research studies is collecting
various additional information including genetic data. The goals of the
emerging field of imaging genetics can be summarized into two aims: 1)
using imaging biomarkers as an intermediate phenotype to uncover underlying
biological mechanisms of diseases; 2) phenotype discovery.
In this talk, we will focus on the first goal, namely using imaging as an
intermediate phenotype, and briefly discuss the second goal of discovering
image-based phenotypes associated with a disease. We propose a unified
Bayesian framework for detecting genetic variants associated with a disease
while exploiting image-based features as an intermediate phenotype. Imaging
genetics methods typically comprise two separate steps. First, image
features are selected based on their relevance to the disease phenotype.
Second, a set of genetic variants is identified to explain the selected
features. In contrast, our method performs these tasks simultaneously to
ultimately assign probabilistic measures of relevance to both genetic and
imaging markers. We derive an efficient approximate inference algorithm
that handles high dimensionality of imaging genetic data. We evaluate the
algorithm on synthetic data and show that it outperforms traditional
models. We also illustrate the application of the method in the context of
the Alzhemer's disease (ADNI dataset).
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Lisa
Berkman*, Thomas D. Cabot Professor of Public Policy and of Epidemiology at
the Harvard School of Public Health. She will be presenting work
entitled *Work,
Family, and Health Network results: preliminary findings on a randomized
work site intervention to improve employee health*.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Anton
