IEEE CIS/SPS Lecture by Dr. Tegan Emerson "Learning from the Shape of Data: Computational Topology for Complex Network Analysis"
In the fourth installment of this season's lecture series, we are delighted to feature a talk by Dr. Tegan Emerson, Research Mathematician and Karle Fellow at the Naval Research Laboratory (NRL), Washington DC
Abstract:
The human brain, and many other complex structures, can be studied using networks. One can define the nodes and edges of the graph in a multitude of ways. These networks can then be studied using ideas from computational topology: a set of algorithmic methods that characterize topological invariants such as connectedness, loops, or holes in high-dimensional data structures. These methods go beyond pairwise connections and enable one to understand global low-dimensional structures in networks, which is difficult for existing methods. In particular, persistent homology, a method that consists of a mathematical formalism to explore the persistence of such structures, has led to promising results in many applications including dynamical systems, neuronal networks, and others. Persistence Images are a stable vector representation of homological features identified from persistent homology. Persistence images can detect differences between networks associated to different classes more strongly than other representations of homological information in many settings. An introduction to homology and topological data analysis will be followed by several examples of applications of persistent homology. The applications portion of the talk will focus primarily on the use of topological techniques for revealing discriminatory structure in functional networks of brain activity of schizophrenics, their asymptomatic siblings, and healthy controls.
Date and Time
Location
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Registration
- Date: 26 Apr 2018
- Time: 06:30 PM to 08:00 PM
- All times are (GMT-05:00) US/Eastern
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- University of Maryland
- College Park, Maryland
- United States 20814
- Building: A.V. Williams
- Room Number: 2460
- Click here for Map
- Contact Event Host
- Co-sponsored by IEEE Computational Intelligence Northern Virginia Chapter (Joel Goodman | George Stantchev) and IEEE Signal Processing Washington Chapter (Min Wu)
- Starts 20 April 2018 05:00 PM
- Ends 26 April 2018 06:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Dr. Tegan Emerson of Naval Research Laboratory
Learning from the Shape of Data: Computational Topology for Complex Network Analysis
Biography:
Tegan Emerson received her B.S. in Mathematics from Oregon State University in 2011 and her M.S. and Ph.D. in Mathematics at Colorado State University in 2013 and 2017, respectively. Currently, she is a Karle Fellow working as a Mathematician in Applied Optics at the Naval Research Laboratory. Her research interests include geometric and topological data analysis, dimensionality reduction, algorithms for image and video processing, and optimization. She was awarded ”Best Paper” at the 2016 Workshop on Hyperspectral Imaging and Signal Processing: Evolutions in Remote Sensing and was a member of the American delegation at the Heidelberg Laureate Forum in 2014.
Agenda
6:30 - 7:00pm Check In/Networking; Light dinner with pizza and refreshments
7 - 7:50pm Lecture
7:50 - 8:10 Q&A + Networking
If arriving by car, feel free to use parking lot 11b, which is open for general access after 4pm. If arriving by Metro, free shuttle service is available from the College Park station to campus.