Jonathan S. McHenry

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       Ph.D. Candidate
Applied Mathematics, Statistics

Department of Mathematics and Statistics
University of Maryland Baltimore County
1000 Hilltop Circle, Baltimore, MD 21250
Office: SOND 401
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Short bio
I am a fourth year PhD student applying my strong background in analysis, optimization, linear algebra, and mathematical statistics to a serious study of the mathematics of machine learning and data analysis. I enjoy solving problems by using a judicious combination of math, computers, and common sense. In addition to math I am interested in machine learning, econometrics, bioinformatics, biometrics, and education metrics.
Research Projects
Bird Detection
In summer 2013, I participated in the Kaggle (a Data Science community) competition which involved detecting birds from audio recordings. Interestingly, the audio was captured in a “Long-Term Experimental Research Forest” in the Cascade mountain range of Oregon. My simple statistical models (multinomial logistic regression) and machine learning algorithms (random forests) did not perform well on the noisy variables that we extracted from the data, but I learned a lot about setting up a Python + MySQL development environment in Windows and Linux, and about leading/working with a team of highly competent developers on a fast-paced deadline-driven research project, and I got to get my hands dirty writing SQL and playing with Python machine learning packages such as scikit-learn.

Bovine Lameness Detection
This 2012 research project involved classifying cows as lame or sound by using 3D time-series data from a scale that cows walk across. Despite the difficulties of noisy high-dimensional data and misbehaving cows/equipment, we were able to report success in the Phase I USDA trial as of August 2012. A critical part of this success was due to my development of an efficient data handling system and innovative heuristic classification algorithms.
The project was funded by a USDA grant to Dr. Uri Tasch of UMBC's Mechanical Engineering Department who contracted my time through CIRC.
I presented this research at the 2013 CS&E conference in Boston

Fraud Detection
In summer 2011, I took an internship at the United States Financial Industry Regulatory Authority (FINRA). My task was to study the suitability of automated fraud detection techniques, specifically Benford's Law, for use at FINRA. It turned out that Benford's Law could not be used directly because the data did not satisfy certain assumptions. However, not to be dismayed, I developed a novel fraud detection technique based on the spirit of Benford's Law that was applicable to FINRA data. This technique successfully discovered many data anomalies that will warrant further investigation.
Further, I studied Bernie Madoff's $65,000,000,000 Ponzie scheme and compiled a set of techniques capable of detecting a future fraud of that type. I did not have access to the IT system containing production data, so some lucky future researcher will get to apply my techniques to catch bad guys in real time.
“Whereas Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get computers to program themselves”
–Tom M. Mitchell, CMU, July 2006

“... Out of such utilitarian concerns will emerge general principles, including mathematical ones. A typical and generic problem is to describe a manifold and its inherent and possibly low-dimensional geometry, when it is presented through noisy data embedded in a high-dimensional space. If we have had four centuries of physically based and motivated mathematics, it does not seem a stretch of the imagination to assume that we will have one or more centuries of mathematics based on the organization of data and the intelligence to be derived from it, perhaps to be named the mathematics of knowledge and intelligence. Mathematics and pure mathematicians have a long tradition of exploring the issues of data, intelligence, noise and meaning. The classical works of Kolmogorov and of Shannon illustrate this point. The future is bright for an expansion of this type of inquiry.”
–J. Glimm, Bulletin of the AMS, Jan. 2010
Teaching Assistantship (MATH 251: Multivariable Calculus) through the Department of Mathematics and Statistics.
Graduate Research Assistantship through UMBC's Center for Interdisciplinary Research and Consulting
The GRA responsibilities include giving math software workshops, consulting, and networking.
In addition, I led a team of undergraduate researchers in the 2012 High Performance Computing Research Experience for Undergraduates.
I was awarded the CIRC Consultant of the Year for 2011/2012!
Fall 2013 Roles: CIRC Lead Math RA, Teaching Assistant for Multivariate Calculus, Math Gym Data Architect
2012–2014: President of the SIAM Student Chapter at UMBC
Doctor of Philosophy: Applied Mathematics [in progress], UMBC
Master of Science: Applied Mathematics, UMBC
Bachelor of Science: Mathematics, UMBC
Bachelor of Science: Physics, UMBC

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“The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill.” –Albert Einstein

Jonathan McHenry
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