After finishing my doctoral degree at Northeastern University (Boston, MA), I accepted a position as a Senior Computer Scientist at Tenet3, a research and development company that provides software solutions for various clients in the defense and intelligence communities.

My work has focused on developing, scaling, deploying, and maintaining a variety of graph- or machine learning (ML) related solutions in the Advanced Capabilities group. This has involved a mixture of research, software development, and client-facing work, with a focus on pioneering scalable solutions for analyzing large-scale graph data.

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.

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.

• Source code of satellite experiments

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 connections the Mapper algorithm has 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.

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.

• Experimental optimal design code

My graduate research at WSU was 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 network models. On the software side, the project involved:

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).

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.

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.

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

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.