Data analysis

Monday, May 7, 2012 - 9:00am - 10:00am
Deborah Estrin (University of California, Los Angeles)
The most significant health and wellness challenges increasingly involve chronic conditions, from diabetes, hypertension, and asthma to depression, chronic-pain, sleep and neurological disorders. And three lifestyle behaviors contribute to many of these conditions.
Friday, June 26, 2009 - 10:30am - 11:30am
Gunnar Carlsson (Stanford University)
No Abstract
Friday, February 14, 2014 - 9:00am - 9:50am
Tomas Gedeon (Montana State University)
Experimental data on gene regulation and protein interaction is often very qualitative, with the only information available about pairwise interactions is the presence of either up-or down- regulation. Since majority of the parameters for any model in such a situation are not constrained by data, it is important to understand how different choices of parameters affect the dynamics and, therefore, the predictions of such a model.
Tuesday, December 10, 2013 - 11:30am - 12:20pm
Pek Lum (Ayasdi, Inc.)
Data has shape. Shape has meaning. I will discuss how Topological Data Analysis (TDA) has been applied to various biological problems such as identifying patient populations that might respond better to certain treatments, understanding the underlying etiology of a disease such as cancer and studying drug response at the single cell level.
Thursday, December 12, 2013 - 9:00am - 9:50am
Tomas Gedeon (Montana State University)
We use topological data analysis to investigate three dimensional spatial structure of the locus of afferent neuron terminals in cricket's Acheta domesticus terminal ganglion. Each afferent neuron innervates a filiform hair positioned on a cercus, a protruding appendage at the rear of the animal. The hairs transduce air motion to the neuron signal which is used by cricket to respond to the environment.
Wednesday, October 2, 2013 - 9:00am - 10:15am
Alessandro Rinaldo (Carnegie-Mellon University)
Recent advances in computational geometry and computational topology have made it possible to compute topological invariants of sets and functions from sample points. These types of data summaries provide new tools for preprocessing, summarizing and visualizing complex and even high dimensional data. As a result, the number and the variety of applications of topological data-analytic methods have been growing rapidly.
Monday, October 7, 2013 - 2:00pm - 2:50pm
Dmitriy Morozov (Lawrence Berkeley Laboratory)
This talk revisits merge trees, a basic topological descriptor that records
connectivity of sublevel sets of a scalar function. We introduce an interleaving
distance between two merge trees and establish its stability to perturbations of
the function. We show that this distance is never smaller than the bottleneck
distance between 0-dimensional persistence diagrams of the function.

On the computational side, we consider a distributed representation of merge
trees that not only improves their parallel computation, but also supports
Monday, June 17, 2013 - 11:00am - 12:30pm
Bin Yu (University of California, Berkeley)
This lecture will cover data summarization and visualization tools
such as kernel estimation, loess, scatterplot and dimension
reduction via principal component analysis (PCA). Specific
data examples will be used.
Friday, November 11, 2011 - 1:25pm - 2:25pm
Linda Ness (Telcordia)
Tuesday, October 25, 2011 - 9:00am - 10:00am
Michael Mahoney (Stanford University)
Recent empirical work has demonstrated that, although there often exists
meaningful small scale structure (e.g., clustering structure around a
single individual at the size-scale of roughly 100 individuals) in large
social and information networks, analogous large scale structure (e.g.,
meaningful or statistically significant properties of tens or hundreds of
thousands of individuals) either is lacking entirely or is of a form that
is extremely difficult for traditional machine learning and data analysis
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