Single Page Résumé available here

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've worked part time doing research at the Air Force Institute of Technology in the Low Orbitals Radar and Electromagnetism research group since 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.

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.

The specific research I've done, along with several of the projects and presentations I've given, are listed down below.

The majority of the programming I've done in my life has been with C++ (primarily C++11), although in recent years I've been using the R Statistical Environment for statistical computing and graphics as my daily driver for most tasks. I've delved into using plain ANSI-C89/C90 in a couple of the projects (listed below). I spent about two years doing quite a bit of scientific computing with the Compute Unified Device Architecture (CUDA) and subsequent dabbling with OpenCL, of which the former efforts lead into a few publications. I'm moderately proficient with Java, and I've had a number of class-or-personal projects requiring the use of Python, Ruby, JavaScript, PHP, and Perl, but my general interest in scientific computing keeps me busy within the realm of R and C++, and occasionally Python.

- Density-based Clustering
- Non-parametric density estimation
- Markov Chain Monte Carlo optimization techniques
- Modeling stochastic processes

Related Publication: (In development)

Related Publication: (Under Review)

Hahsler, Michael, Matthew Piekenbrock, and Derek Doran. "dbscan: Fast Density Based Clustering in R", Journal of Statistical Software.

Related Publication(s): (Published)

- J. Robinson, M. Piekenbrock, L. Burchett, et. al. Parallelized Iterative Closest Point for Autonomous Aerial Refueling. In International Symposium on Visual Computing (pp. 593-602). Springer International Publishing. (2016, December)
- L. Burchett, J. Robinson, M. Piekenbrock, et. al. “Automated aerial refueling: Parallelized 3d iterative closest point,” in IEEE NAECON, 2016, pp. 1–5 (2016)

School | Degree | Graduation Year |
---|---|---|

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 | 2015 |

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 (R/Rcpp)
- Nonparametric Geospatial Point of Interest detection (R/C++)
- Spatio-temporal Social Network Model of spatial data (R)
- Computer Vision Project involving a parallelized Iterative Closest Point (ICP) algorithm (C++/CUDA)
- Parallelization of existing atmospheric absorption routines (MATLAB MEX/OpenCL)
- Modeling web navigation patterns using hierarchical Markov Models (R/MATLAB)
- Web interface to viewing 3D models (WebGL/JavaScript [+ HTML/CSS])
- J. Robinson, M. Piekenbrock, L. Burchett, et. al. Parallelized Iterative Closest Point for Autonomous Aerial Refueling. In International Symposium on Visual Computing (pp. 593-602). Springer International Publishing. (2016, December)
- L. Burchett, J. Robinson, M. Piekenbrock, et. al. “Automated aerial refueling: Parallelized 3d iterative closest point,” in IEEE NAECON, 2016, pp. 1–5 (2016)
- Dynamic Geospatial analysis of wide-area motion imagery (R/Python/Java)
- Maurice, Matthew, Matthew Piekenbrock, and Derek Doran. "WAMINet: An Open Source Library for Dynamic Geospace Analysis Using WAMI." Multimedia (ISM), 2015 IEEE International Symposium on. IEEE, 2015.
- Conversion of Nonlinear Optimization algorithm to C89 implementation (MATLAB/C)
- Search engine/web application for the Ozone Widget Framework (JavaScript/PHP)
- Implementation of unsplittable flow approximation algorithm (C++/Python)
- Conversion tool from Oracle’s Abstract Data Type to XMLType in Oracle’s Enterprise DBMS

Worked on:

Studied:

Density-based clustering algorithms, Discrete and Continous-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

Worked on:

Studied:

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

Published:

Worked on:

Studied:

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)

Published:

Worked on:

Studied:

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.]

- dbscan R Package
- Clustering Presentation(GIF Animations)(Video) (PDF) Presentation I gave to the Data Science and Security Cluster Group and the Kno.e.sis research group
- Machine Learning Project: Bayesian Linear Regression(PDF)
- Artificial Neural Network Code (made from scratch)(R Code)
- Exploratory Research on Markov Chain Monte Carlo Basics(Presentation)(PDF)
- Exploratory Research on Bayesian Network Training Basics(PDF)

- Developer and Maintainer of:daymunc.org
- Staff Position: Chair of Simulated Security CouncilRegional Model United Nations Annual Conference (2017)
- Staff Position: Chair of United Nation Childrens FundRegional Model United Nations Annual Conference (2016)
- Outstanding Position Paper AwardNational Model United Nations Annual Conference (2014)
- Honorable Mention AwardRegional Model United Nations Annual Conference (2014)
- Outstanding Delegation AwardNational Model United Nations Annual Conference (2013)
- Honorable Mention AwardRegional Model United Nations Annual Conference (2013)