Traditional paper CV available here

I'm a graduate Research Assistant (GRA) majoring in Computer Science, with a focus (and minor) in Statistics and Statistical Modeling. I'm interested in Machine Learning (ML) and Artificial Intelligence (AI). My research interests are in statistical learning theory, unsupervised learning, and building software for the purpose of scientific computing and reproducible research. Topics that interest me include Clustering, Manifold Learning, Topology Theory and Topological Data Analysis, approaches to Density Estimation, Reinforcement Learning (such as Generative Adversarial learning!), etc. I have supplemental research interests in the fields of Network Science, Bayesian Statistics, Computational Geometry, Computational Geometry, and 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 early 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 intersection between Machine Learning and Statistical Learning Theory. Broadly, the topics I've studied include: Bayesian Networks, Unsupervised types Deep Learning (e.g. SOMs and GANs), 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 detailed further down below.

My computational experience varies with what I'm doing. I use the R Project for Statistical Computing for nearly everything I do in ML. In my undergraduate years, I extensively used C++ (primarily C++11), for scientific computing projects, a few of which are listed below. Some of the projects actually required using regular ANSI-C89/C90. I spent about two years doing research into computational geometry and parallel computing with the Compute Unified Device Architecture (CUDA) and subsequent ports using OpenCL. These efforts lead to a few publications. I'm moderately proficient with Java, and I've had a number of class-or-personal projects requiring the use of other languages, i.e. Python and others.

- Density-based Clustering
- Statistical Learning Theory
- Unsupervised and Semi-supervised Learning
- Topology Theory in relation to "The Manifold Hypothesis"

Related Publication(s): (ongoing research effort)

- (Under development) M. Piekenbrock and D. Doran. Efficient Cover Parameterization and Simplicial Complex Generation for Mapper. SIAM Journal on Applied Algebra and Geometry, 2018 (intending).[Link]
- NOTE: This is paper is still in its very early stages. As incomplete work not yet suitable for arxiv, this rough draft is only available from this website.

Related Publication(s): (ongoing research effort)

- (Coming Soon)
- Maurice, M., Piekenbrock, M., & Doran, D. (2015, December). WAMINet: An Open Source Library for Dynamic Geospace Analysis Using WAMI. In Multimedia (ISM), 2015 IEEE International Symposium on (pp. 445-448). IEEE.

Related Publication(s):

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

Related Publication(s):

- 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)
- Piekenbrock, M., Robinson, J., Burchett, L., Nykl, S., Woolley, B., & Terzuoli, A. (2016, July). Automated aerial refueling: Parallelized 3D iterative closest point: Subject area: Guidance and control. In Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), 2016 IEEE National (pp. 188-192). IEEE.

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 for spatial data (R)
- Coming Soon! (Preprint available on request)
- 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)
- Implementation of unsplittable flow approximation algorithm (C++/Python)
- Search engine/web application for the Ozone Widget Framework (JavaScript/PHP)
- Conversion tool from Oracle’s Abstract Data Type to XMLType in Oracle’s Enterprise DBMS

Worked on:

Studied:

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`

Worked on:

I submitted a successful funding proposal under the Google Summer of Code (GSOC) Initiative to the R Project for Statistical Computing to explore, develop, and unify recent developments related the theory of density-based clustering. This involved a mixture of code development which culminated in the form of an R package, as well as deep research to further understand the theory and utility of the cluster tree. There was also a WSU newsroom piece that describes the proposal in a non-technical way.

Project LinkWorked on:

As I read more into theoretical foundations of density-based clustering, my research began to intersect Topology Theory and Manifold Learning. During this time, I started working in a minor capacity with a local research group studying how to combine techniques from the fields of topology and machine learning for the purpose of data analysis. Primarily, I researched theoretical extensions to the Mapper framework, a common algorithm used for performing TDAs.

Published:

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)
- Tutorial on Markov Chain Monte Carlo Basics(Presentation)(PDF)
- Introduction to Bayesian Network Training Basics(PDF)

- Staff PositionRegional Model United Nations Annual Conference ('16 and '17)
- Outstanding Position Paper AwardNational Model United Nations Annual Conference ('14)
- Outstanding Delegation AwardNational Model United Nations Annual Conference ('13)
- Honorable Mention AwardRegional Model United Nations Annual Conference ('13 and '14)
- Developer and Maintainer of:daymunc.org