Matt Piekenbrock
I'm currently a graduate student at NEU's Khoury College of Computer Sciences, advised by Jose Perea.
A more traditional paper CV is available here
Employment
Graduate Research Assistant
Perea Lab
Fall 2019 - Present
MSU/NEU
Topological Data AnalysisLinear AlgebraMachine LearningMotivated by my previous work on the foundations of density-based clustering, I focused on implementing and extending the Mapper algorithm, a popular and very general method which has been used successfully for data analysis.
After beginning my doctoral research at Michigan State University (Fall 19’), I transferred to Northeastern University (Boston, MA) in Fall 2021 after my advisor (Jose Perea) accepted a joint appointment offer at the Khoury College of Computer Sciences.
My doctoral research focused on applications of topological theory to various common machine learning applications. In particular, much of my time was spent on accelerating the persistence algorithm in time-varying settings, codeveloping a topological dimensionality reduction using fiber bundle theory, and on studying a spectral-relaxations of the persistent rank invariant.
SCaN Intern
National Aeronautics and Space Administration
Summer 2022
John H. Glenn Research Center at Lewis Field, OH
Space networkingGraph TheoryFlow algorithmsTowards enabling delay-tolerant satellite communications in uncertain space environments, I was re-hired back at NASA as part of the Space Communications and Navigation (SCaN) program to expand the algorithmic theory on time-dependent routing.
Job Description
Towards enabling delay-tolerant satellite communications in uncertain space environments, I was re-hired back at NASA as part of the Space Communications and Navigation (SCaN) program to expand the algorithmic theory on time-dependent routing. My research focused on incorporating additional geometric assumptions into routing models built for delay- and disruption-tolerant networks, particularly in the low Earth orbit regime.Research Associate
Oak Ridge Institute for Science and Education
Fall 2017, Fall 2018 - Fall 2019
Air Force Research Laboratory, WPAFB
TopologyMapperR packageMotivated by my previous work on the foundations of density-based clustering, I focused on implementing and extending the Mapper algorithm, a popular and very general method which has been used successfully for data analysis.
Job Description
In 2017, I joined with a local research group under Dr. Ryan Kramer as part of AFRL’s Human Performance Wing to explore and expand the intersection between algorithms in TDA and machine learning. My time there was focused on implementing and extending the Mapper algorithm, a topological method that reframes common data analysis tasks as problems of analyzing level sets on topological spaces (introduction available here).
My research was centered around enabling the efficient construction of mappers in multiscale settings and on understanding the connections the Mapper algorithm had to other existing constructions, such as Reeb graphs, nerve complexes, and hierarchical clustering. Beyond learning the theory and developing the software, I also applied Mapper to various geospatial and image analysis tasks, such as video segmentation, object recognition, and clustering.
Software
- Mapper (R Package)
- simplextree (R Package)
- Vignette on using Mapper for shape recognition
- Mapper (R Package)
Graduate Research Assistant
Web and Complex Systems Group
Spring 2016 - Fall 2018
Wright State University
ClusteringNetwork analysisMachine LearningMotivated by my previous work on the foundations of density-based clustering, I focused on implementing and extending the Mapper algorithm, a popular and very general method which has been used successfully for data analysis.
My graduate research aimed at modeling real-world traffic network networks at a macroscopic scale. The high-level goal of the project was to model dynamic network representations extracted from raw positioning/track information via random (distributional) network models. On the software side, the project involved:
- Density-based clustering (R/Rcpp)
- Geospatial Point of Interest (POI) detection / Nonparameteric distribution modeling (R/C++)
- Spatio-temporal network models ®
Research topics involved during this time include density-based clustering algorithms, cluster validation measures, non-parametric density estimation techniques, Markov Chain Monte Carlo (MCMC) optimization techniques, and random graph modeling (stochastic block models).
LERCIP Intern
National Aeronautics and Space Administration
Summer 2018
John H. Glenn Research Center at Lewis Field, OH
Experimental designMachine learningMaterial scienceTowards accelerating the design and discovery materials for use in extreme environments, I was hired by Dr. Steven Arnold under NASAs 10-week LERCIP program to apply Machine Learning to a specific Material Science problem.
Job Description:
I was hired by Dr. Steven Arnold under NASAs 10-week LERCIP program to use machine learning to accelerate the simulation-based design of materials and structures through multiscale modeling, in line with NASA’s 2040 vision.
Publications:
- Stuckner, J., Piekenbrock, M., Arnold, S. M., & Ricks, T. M. (2021). Optimal experimental design with fast neural network surrogate models. Computational Materials Science, 200, 110747.
- Arnold, S. M., Piekenbrock, M., Ricks, T. M., & Stuckner, J. (2020). Multiscale analysis of composites using surrogate modeling and information optimal designs. In AIAA Scitech 2020 Forum (p. 1863).
Software
Student Participant
Google Summer of Code
Summer 2017
R Project for Statistical Computing
ClusteringLearning theoryR packageTowards unifying recent developments related the theory and utility of density-based clustering, this project involved a mixture of research and code development which culminated in the form of an R package for estimating the empirical cluster tree.
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 (see the project page). This involved a mixture of research and code development which culminated in the form of an R package for estimating the cluster tree, a hierarchical summary of the level-sets of a density function. There was also a WSU newsroom piece that describes the proposal in a non-technical way.
Civilian Research Assistant
Oak Ridge Institute for Science and Education
June 2014 - Spring 2015
Air Force Institute of Technology, WPAFB
OptimizationGraph theoryFlow algorithmsTowards the end of my undergraduate degree, my contract at the [Air Force Institute of Technology](https://www.afit.edu/) (AFIT) was extended under ORISE, where I continued working with the LOREnet group under Dr. Andrew Terzuoli
Job Description
Towards the end of my undergraduate degree, my contract at the Air Force Institute of Technology (AFIT) was extended under ORISE, where I continued working with the LOREnet group under Dr. Andrew Terzuoli. During this time I primarily worked with Dr. Scott Nykl on the development of a novel Iterative Closest Point algorithm amenable to massive parallelization, implemented in C++/CUDA, for the purposes of enabling real-time tracking of aircraft in the context of Autonomous Aerial Refueling.
Publications
- 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) (doi: 10.1007/978-3-319-50835-1_53)
- 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. (doi: 10.1109/NAECON.2016.7856797)
Projects
Example projects included, but were not limited too:
- Researching hierarchical markov model for predicting web navigation patterns
- Parallelizing existing atmospheric absorption routines with OpenCL
- Coding a nonlinear optimization algorithm in ANSI-C, and making it callable from MATLAB via MEX
Civilian Research Assistant
Southwestern Ohio Council For Higher Education
December 2013 - June 2014
Air Force Institute of Technology, WPAFB
OptimizationGraph theoryFlow algorithmsAs my first experience doing undergraduate research, I worked in a heavily multi-disciplinary team called the Low Orbitals Radar and Electromagnetism group, where I worked on a diverse set of projects involving computational, statistical, or physics-based requirements
Job Description
Under the guidance of Dr. Andrew Terzuoli, I was hired at the Air Force Institute of Technology (AFIT) to do research in a multi-disciplinary team called the Low Orbitals Radar and Electromagnetism (LOREnet) group, where I worked on a diverse set of projects involving computational, statistical, or physics-based requirements. As my first research-oriented experience, I primarily assisted graduate students with programmatic or computationally-intensive tasks.
Projects
Example projects included, but were not limited too:
- Implementing an unsplittable flow approximation algorithm (C++ and Python)
- Creating a conversion tool between Oracle’s Abstract Data Type and XMLType (Java)
- Developing a prototype UI for searching and viewing 3d models (JavaScript+three.js)
Education
Doctorate in CS (Pursuing)
Khoury College of Computer Sciences
Northeastern University, 2021-Present
Advisor: Jose Perea
Click for teaching experience, coursework taken, and other details...Teaching:
- Graduate teaching assistant - Unsupervised Data Mining (CS 6220 / DS 5230), Spring 2023
- Graduate teaching assistant - Unsupervised Machine Learning (CS 6220 / DS 5230), Fall 2023
- Graduate teaching assistant - Data Mining Techniques (CS 6220 / DS 5230), Summer 2023
- Graduate teaching assistant - Machine Learning (CS 6140 / 4420), Spring 2023
- Graduate teaching assistant - Unsupervised Machine Learning (CS 6220 / DS 5230), Fall 2022
Coursework (GPA: 3.83):
- Research Seminar - Topological Data Analysis
- Formal Verification, Modeling, & Synthesis
- Network Visualization
Doctorate in CMSE (Transferred)
Computational Mathematics, Science and Engineering
Michigan State University, 2019-2021
Advisor: Jose Perea
Click for teaching experience, coursework taken, and other details...Teaching:
- Graduate teaching assistant - Computational Modeling (CMSE 201), Fall 2020
Coursework (GPA: 3.83):
- Numerical Linear Algebra (CMSE 823)
- Numerical Differential Equations (CMSE 821)
- Math Foundations of Data Science (CMSE 890)
- Topological Methods for the Analysis of Data (CMSE 890)
- Parallel Computing (CMSE 822)
- Geometry and Topology II (MTH 869)
- Mathematical foundations of analysis (CMSE 890)
- Algebra I (MTH 818)
Masters of Science in CS
College of Engineering and Computer Science
Wright State University, 2015-2018
Advisor: Derek Doran
Click for teaching experience, coursework taken, and other details...Coursework (GPA: 3.88):
- Network Science
- Machine Learning
- Information Theory
- Applied Stochastic Processes
- Algorithm Design and Analysis
- Empirical Analysis
- Advanced Programming Languages
- Distributed Computing
Bachelor of Science in CS (+STT)
College of Engineering and Computer Science
Wright State University, 2010-2015
Click for teaching experience, coursework taken, and other details...Coursework (GPA: 3.42, in-major):
- Applied Statistics I & II
- Optimization Techniques
- Foundations of AI
- Computational Tools for Data Analysis
- Theoretical Statistics
- Linear Algebra
Programming Experience
My computational experience is diverse. My university coursework required using Java, C++, or Matlab (10-15’). I used C++98 or ANSI-C extensively for the AFIT-affiliated projects, occasionally writing high level scripts in Python or Matlab (+MEX) (13-15’). I used either the R project (+Rcpp) or Python (+Cython) for the majority of the projects I was involved in, preferring the former (15-19’). Since 2020, interfacing Python with modern C++ FFIs (e.g. pybind11) has been my primary development workflow.