Work History
Glyphic Bio - De novo protein sequencing using nanopores
Built the instrument control software for the prototype sequencer, then pivoted to Glyphic's real time sequencing platform. Managed the project that lifted the entire bioinformatics pipeline into Nextflow, reducing unit compute costs by more than 5x. Primary owner of the company's cloud infrastructure. Built a handful of custom tools for ingesting and analyzing lab data from scratch.
Ruby Robotics - Automated tissue biopsy platform
Acquired by Intuitive Surgical in 2025
Over a few months, built a C# app that enabled cytopathologist end-users to navigate thousands of high-resolution tissue scan images. Helped refactor instrument control code that performed the tissue scans, speeding them up significantly.
Deepcell - Pioneering the morpholome
Built the instrument code (C#) for the next-generation prototype instrument, a platform for high throughput single-cell sorting and deposition.
GenapSys - Next-gen sequencing platform
Refactored the entire primary analysis pipeline, plugging C modules into Python to achieve massive speedups and more efficient resource use. Became primary owner of the pipeline code delivered to customers with the seqeuencer.
Preamble
Phase Transitions
We're all familiar with the idea of a phase transition—the ice melting in your glass of water, for example. In a simple sense, a phase transition describes a point where a system abruptly changes between two distinct states. Allowing time for things to equilibrate, the ice will turn to water ever so slightly above 0°C, and back to ice every so slightly below it. In this case, 0°C is what's called the critical point.
Intro
Graph Theory
A "graph" in this context is math parlance for a network. Graph theory is something of a young field, tracing its origin back to a paper by Leonhard Euler about the Seven Bridges of Königsberg. In its simplest form, a graph contains only two elements: vertices and edges. Any vertex can be connected to any other through an edge.
Background
Percolation
Let's start with an "empty" graph of vertices with no edges, then one at a time we'll add an edge. If we choose which two vertices to connect uniformly at random, we'll have what's called an Erdős–Rényi random graph. Add enough edges and eventually every vertex in the graph becomes connected into a single cluster. The process of adding links until reaching the point of full connectivity is called percolation.
Background Con't
Percolation & Phase Transitions
One way to describe a phaste transition is by its order parameter, a quantity that changes abruptly at the transition's critical point. With that in mind, let's look at what happens when we track the size of the largest connected cluster as we add edges to an empty graph. As the size of the graph approaches infinity, it will naturally start near zero, but add edges until there are right around half as many as there are vertices (averaged over many realizations) and all of a sudden the largest cluster starts growing from nearly zero to encompass the entire graph. Just what we'd expect from a phase transition.
Motivation
Guided Percolation
Erdős–Rényi growth is an example of a continuous phase transition, where the order parameter remains smooth at the critical point. In 2009, a paper by Achlioptas et. al. contended that by considering a selection criteria during the addition of each edge, it was possible to achieve a discontinuous transition. Though this contention turned out to be incorrect, the process of guiding percolation through a selection criteria can still change both the location of the critical point, and the overall "speed" of the transition.
Our Research
Localized Selection Rules, Tunable Criticality
The selection criteria considered by Achlioptas et. al. requires knowing the state of the entire graph at all times to enact. We developed a criteria that requires only knowledge of the local state for each vertex under consideration. We demonstrated that this type of selection criteria retains the slower "speed" of an Erdős–Rényi transition, while admitting tunability of the critical point—essentially, a delay in the onset of the phase transition—similar to the Achlioptas process.
Publication
We introduce a guided network growth model, which we call the degree product rule process, that uses solely local information when adding new edges.
🗎 Read paper (APS) | 🗎 Read paper (Arxiv)Feel free to reach out about research collaborations, professional opportunities, or anything else. I'm always happy to connect.