I am a data architect and full stack engineer with an interest in machine learning. My formal education includes a Master's degree in applied data science.
My personal GitHub repositories are pushed to the FAR Analytics & Research GitHub organization. Examples of my work can also be found in the repositories of the Educational Technology Collective.
My University of Michigan enterprise GitHub account is located here.
Please see my resume for more information on my professional background.
Network⬄Services </TypeScript> </Node.js>
Network⬄Services provides a simple and intuitive toolkit that makes scaling your app and connecting it to the network easy. You can use it to transform an arbitrary application into a network-connected scalable Service Application. You can connect to your Service App, from the same process or another process, and call methods on it using a type-safe Service API.
Socketnaut </TypeScript> </Node.js>
Socketnaut makes scaling native Node.js servers easy. A Socketnaut Service consists of a TCP proxy and a pool of HTTP servers. Socketnaut will uniformly distribute incoming TCP sockets across the pool of allocated servers. This strategy allows for both distribution and parallel processing of incoming requests. Socketnaut consumes native Node.js servers (e.g., http.Server, https.Server, net.Server, tls.Server); hence, if you know the Node API, you already know how to build applications on Socketnaut.
Socketnaut can be combined with performant Node.js web application frameworks (e.g., Fastify, Koa, Express) in order to easily scale the main module of the web application.
A single Socketnaut instance can handle thousands of concurrent connections when running on capable hardware. When under load, Socketnaut will spawn HTTP servers in order to meet demand and release resources as demand declines; hence, Socketnaut mitigates its memory footprint by effectively managing its thread pool.
Streams Logger </TypeScript> </Node.js>
Streams is an intuitive and performant type-safe logger built on native Node.js streams. You can use the built-in logging components (e.g., the Logger, Formatter, Filter, ConsoleHandler, RotatingFileHandler, and SocketHandler) for common logging tasks or implement your own logging Node to handle a wide range of logging scenarios. Streams offers a graph-like API pattern for building sophisticated logging pipelines.
Memoiz </Python>
Memoiz provides a function decorator that can be used in order to augment functions or methods with memoization capabilties. It makes reasonable assumptions about how and if to cache the return value of a function or method based on the arguments passed to it. The decorator can be used on both free and bound functions.
JupyterLab Telemetry </Python> </JavaScript>
A JupyterLab extension for capturing JupyterLab events - deployed to the Coursera learning environment.
Port Agent </TypeScript> </Node.js>
Port Agent provides a simple and intuitive interface that makes inter-thread function calls easy. Port Agent will marshal the return value or Error from the other thread back to the caller. The other thread may be the main thread or a worker thread.
Scalability </TypeScript> </Node.js>
Scalability is a type-safe service scaling facility built on Network⬄Services. It provides a simple and intuitive API for scaling Node.js modules using Worker threads. You can create a Service App in your scaled module and call its methods from the main thread using a Service API. Scalability allows you to easily transform your single threaded application into a type-safe multithreaded one.
JS HTML Renderer </JavaScript> </Node.js>
A JS DSL for rendering HTML on the client or the server. The JS HTML Renderer provides a concise and intuitive syntax for writing HTML using JavaScript.
The Data Science Project </Python>
A Python flat-layout project template for data science projects.
In the data science domain projects are sometimes shared as an informal assemblage of scripts. This repository describes an alternative approach using a conventional Python flat-layout project. It follows formal conventions for packaging a Python project. You install it into your environment just like a conventional Python package. It consists of a single package with an IMRaD-like layout; it contains materials, methods, and results sub-packages. Project dependencies are specified in the pyproject.toml file.
Neural-pleX </Python>
Neural-pleX is an object oriented neural network implementation. The Neural-pleX API consists of Network, Layer, and Neuron constructors. The networks can be easily visualized using a visualization library.
git reset --mixed $(git log --pretty=format:"%h" | tail -n -1) && git status && git add . && git commit -m 'more' && git push --force