I earned a Ph.D in Computational Science and Engineering at Georgia Tech. My research focuses on numerical, statistical, and streaming algorithms for data analysis. The applications include complex networks, online media, and observational medical data. My Dissertation Defense was on March 28th 2016!
For information on my Programming and Software related activities, you can go to my Github site.
I am a core maintainer of the Julia Package LightGraphs.
I am currently working at the Georgia Tech Research Institute on Data Analysis and High Performance Computing as applied to healthcare and social science problems in general.
You can email me at james@jpfairbanks.net.
I am a core maintainer of the Julia package LightGraphs.jl which has been selected as a core package for the product Julia Pro.
My defense is scheduled for March 28th 2016 at 9:00 AM.
My defense was a great success! It was a grand adventure. Thanks to everyone who has helped along the way.
Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra.
This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each contribute a fragment to understanding the graph; however, their combination allows us to understand dynamic behavior and graph structure.
Spectral partitioning methods rely on eigenvectors for solving data analysis problems such as clustering. Eigenvectors of large sparse systems must be approximated with iterative methods. This dissertation analyzes how data analysis accuracy depends on the numerical accuracy of the eigensolver. This leads to new bounds on the residual tolerance necessary to guarantee correct partitioning. We present a novel stopping criterion for spectral partitioning guaranteed to satisfy the Cheeger inequality along with an empirical study of
I am interested in solving real world problems on large Graph or Network data using statistically sound reasoning and mathematically rigorous methods. Traditional statistics and Machine Learning operate on data that are collections of real vectors. We define and use the analogous concepts where the data are connections between entities that do not live in such a nice space. We solve real problems using this graph representation, and make sure that those solutions are grounded in a solid theoretical framework, allowing us to reason more effectively. I am also interested in understanding the complexity of streaming computations. Of particular interest are online learning, one pass algorithms, and the W-stream model of computation.
The major problems of interest to me are:
I am particularly interested in solving these problems under the Streaming Analysis Model. When the underlying graph is changing over time, many problems become more challenging and interesting.
If we want to apply linear algebra methods to graph analysis in streams, it is important to understand the connection between the numerical accuracy of these computations and the analysis accuracy of the methods that they support. For example in spectral partitioning, how much accuracy do we need on the eigenvectors in order to find good partitions?
You can view the slides from my talks
I was the TA for CSE6643 Numerical Linear Algebra being taught by Prof. Haesun Park. The syllabus is available here.
Because I frequently need to define the same macros in every latex document, I decided to write my own style file on the web.
You can use it by downloading it as jpfairbanks.sty and then \usepackage{jpfairbanks}
. The commands can change at any time so make a copy or be prepared.
I was the TA for CSE6220 which is being taught by Prof. Srinivas Aluru. You can find some helpful resources that might be helpful to students and others. If you are looking to learn about LaTeX you can get it at the source, and then read the Wikibook.
Because I frequently need to define the same macros in every latex document, I decided to write my own style file on the web.
You can use it by downloading it as jpfairbanks.sty and then \usepackage{jpfairbanks}
. The commands can change at any time so make a copy or be prepared.
The Georgia Tech CSE department was praised in an article in the SIAM NEWS July/August 2015. I participated in the quoted discussion at SIAM CSE 2015.
The Georgia Tech Online Masters of Computer Science was praised as a leader in the future of online education in The Economist Magazine.