I'm a graduate student majoring in Computer Science primarily working in the research realm, with a focus (and minor) in Statistics and Statistical Modeling. My research efforts are quite diverse, I think I could get into nearly anything relating to scientific computing, but I'm particularly interested in data science. I have supplemental research interests in the fields of Statistical Learning, Clustering, Network Science, Model-based Machine Learning, Bayesian Statistics, Computer Vision, and High Performance Parallel Computing.
I previously worked part time doing research at the Air Force Institute of Technology in the Low Orbitals Radar and Electromagnetism research group (starting 2013) doing either 1) research for an independent project the group received under supervision of Dr. Andrew Terzuoli or 2) supporting the graduate students' research efforts in the group. I worked with the group until Spring 2017.
In 2015, I started working for the Web and Complex Systems Lab as an undergraduate research assistant shortly after being introduced to Data Science in an elective class I took, CS 3250: Computational Tools and Techniques for Data Analysis taught by Derek Doran. I received a graduate research assistant position in the same lab shortly after, beginning my graduate degree working towards a M.S. in Computer Science.
I'm interested in the broad field of Data Science and Statistical Learning, and I've spent time learning about many different aspects and techniques of both fields. In short, I've spent anywhere from a few weeks to multiple months studying: Bayesian Networks, Neural Networks, Nonparametric density estimation, Clustering techniques, Social Network Models, Information Theory, Bayesian inference techniques, and Markov Chain Monte Carlo (MCMC) methods. The specific research I've done, along with several of the (either class or personal) projects and presentations I've given, are listed down further below.
|Wright State University||Masters of Science in Computer Science||(In Progress)|
Expected Fall 2017 or Spring 2018
|Wright State University||Bachelor of Science in Computer Science|
Minor in Statistics
CEG 7900:Network Science
CS 7830:Machine Learning
CS 3250:Computational Tools and Techniques for Data Analysis
STT 7020:Applied Stochastic Processes
CS 7230:Information Theory
CS 4850:Foundations of Artificial Intelligence
STT 3600/3610:Applied Statistics I & II
STT 4610:Theoretical Statistics I
CS 7200:Algorithm Design and Analysis
Density-based clustering algorithms, Discrete and continuous-time Markov Chains, Poisson Process Modeling, Brownian Motion, Adaptive Markov Chain Monte Carlo (MCMC) optimization techniques, [Dynamic] Bayesian Network modeling, Bayesian inference, parameter estimation techniques (EM/MAP), Random Graph Modeling (ERGMs, ER Model, etc.), Bayesian Linear and Logistic Regression, (simple) Artificial Neural Networks, internal cluster validation measures, non-parametric density estimation techniques, information theory
Branch-and-bound spatial indexing data structures (kd-trees, cover trees, locality sensitive hashing), the k-nearest neighbor problem, finite mixture modeling, general parameter estimation techniques (Expectation Maximization/ MAP estimates), Dirichlet Process Modeling
Various random graph models such as Erdős–Rényi models and Exponential Random Graph Models (ERGMs), entropy measures over networks, density-based clustering techniques (DBSCAN and OPTICS), non-parametric models (ARMA + ARIMA models)
Gauss–Newton Method, approximation algorithms for unsplittable flow problems, graph theory (by extension), relational (Oracle/PostGreSQL/SQLite) and document-based database interaction (MongoDB), Natural language processing techniques for SEO (PageRank), asynchronous vs. synchronous client-server communication strategies with AJAX and NodeJS/PHP servers, XML Schema and XML Technologies [Xlink, XPath, etc.]