Campuses:

Visualization

Tuesday, March 8, 2011 - 2:00pm - 3:00pm
Daniel Keefe (University of Minnesota, Twin Cities)
Intuitively, one of the best ways to understand motion is to see it,
but how can we picture the large multidimensional databases of motion
collected today in surgical training or biomechanical studies? In
this talk, I will present recent research that begins to address this
question through developing novel methods for interactively querying
and visualizing motion data. I will describe motivating problems and
initial tools developed to support data analysis in two application
Wednesday, March 9, 2011 - 11:30am - 12:30pm
Michael Gleicher (University of Wisconsin, Madison)
Most of my work is focused around a single (broad) question: How can
we use our understanding of human perception and artistic traditions
to improve our tools for communicating and data understanding? In
problems ranging from molecular biology to video editing, we are faced
with a deluge of data. In this talk, I'll survey some of the ways
we've tried to turn this problem into solutions. I'll discuss our
efforts in data visualization and multimedia, showing how we can use
Wednesday, October 29, 2008 - 12:15pm - 1:05pm
Tony Jebara (Columbia University)
Given a graph between N high-dimensional nodes, can we faithfully visualize it in just a few dimensions? We present an algorithm that improves the state-of-the art in dimensionality reduction by extending the Maximum Variance Unfolding method. Visualizations are shown for social networks, species trees, image datasets and human activity.
Tuesday, April 21, 2015 - 11:30am - 12:30pm
Viktoria Averina (Boston Scientific), Jeff Sachs (Merck & Co., Inc.)
Viktoria Averina: Algorithm Development for Medical Devices

Implantable cardiac devices such as pacemakers and defibrillators can track and analyze patient’s physiological data. I will showcase key steps of research and development of device-based respiratory monitoring: how one measures patient’s respiratory function, why one would want to track it, and what challenges come up during the implementation.
Tuesday, June 25, 2013 - 9:00am - 10:30am
Bin Yu (University of California, Berkeley)
This lecture will illustrate the power of the sparse coding principle and low-rank regularization in modeling neuron responses to natural images in the very challenging visual cortex area V4.
Monday, June 18, 2012 - 10:40am - 11:00am
Laura Lurati (The Boeing Company)
Algorithms for design optimization are increasingly able to handle complex problem formulations. We will consider the design of a fuel tank consisting of four different disciplinary sub-system components- structures, aerodynamics, cost, and systems. This is a multi-disciplinary, design problem with multiple competing objectives. We will examine several formulations and how to best match the problem formulation with the choice of optimizer.
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