I am a computer science graduate of the University of Arizona, where I also minored in business administration. My focus is networking: I earned my CCNA and work as a network technician at RabenIT, handling layer 1 and layer 2 infrastructure and helping people make sense of their technology.
I like understanding systems all the way down, from the object-oriented design of an application to the physical and data-link layers of a network. Off the clock I am laid back and easy to work with, a big sports fan (basketball, football, and baseball, which is part of what drew me to the baseball analytics work), and I run my own corner of the internet, including this site and my email at austinraben.com. When I build something, I take it all the way to running in the real world, not just working on my laptop.
A personal AI assistant I built and run on my own NAS, a home server I can SSH into from anywhere. COMPUTA is a Next.js app containerized with Docker, reachable over Tailscale, and connected to my calendar and tasks. The same NAS self-hosts a Plex media server and stores the timelapse footage from my 3D printer, pulled off the printer's flash drive over FTP. It all runs on my own hardware, under my own control. Building it taught me how to carry an idea all the way to an always-on service, from containerizing the app to exposing it securely over the network.
Private project. Demo available on request.
A two-part networking project. The first is an enterprise network designed and simulated in Cisco Packet Tracer, built from routers, core and distribution switches, and edge devices. The second is a Python automation script that connects to live Cisco devices with Netmiko, captures their running configurations into date-stamped backups every night, and flags configuration drift by comparing each day against the last. It handles timeouts and authentication failures gracefully and reports a summary of every run. Built alongside earning my CCNA certification. It showed me how much automation improves reliability in network operations, and how catching configuration drift early can prevent an outage.
A machine learning study, built with two teammates, testing whether a neighborhood's wealth and demographics can predict local crime. From six public Tucson GIS datasets, we built a Python pipeline to clean, merge, and normalize neighborhood income, population, age, race, and education data against 2024 police incident records, then trained two elastic net regression models: one for the likelihood of violent crime, one for the likelihood a weapon was involved. Each was tuned with 10-fold cross validation and evaluated using R-squared, MSE, RMSE, and residual analysis. I found and assembled the datasets, trained and evaluated the model for our first hypothesis, wrote the introduction, and helped shape the questions we set out to answer. Our original theses did not have enough usable data behind them, so we rebuilt the entire project around new questions and a new dataset partway through. That reset taught me the most: how to scope a data problem around the data that actually exists, and how to read a weak result honestly instead of forcing a story the numbers do not support.
A data project with the Arizona Baseball Software Development Team, where I was project manager leading a group of eight. We built predictive performance models from TrackMan batted-ball data, monitoring the TrackMan system and collecting stadium measurements from Google Earth, which I processed. I wrote the Python analysis and engineered an "xHR" metric that estimates home run likelihood across more than 60 NCAA stadiums, and the scalable formula behind it was adopted program-wide to support data-driven player development. I also produced hitting reports for the coaching staff and led many of the team's working calls. It taught me how to turn noisy real-world data into a metric an entire program relies on, and how to lead a technical team toward a shared goal.
A command-line application for running a restaurant from the perspective of a manager, host, and server. It handles the full floor workflow: seating customers, assigning servers to tables, taking and modifying orders against a live menu, splitting checks, collecting tips, and applying happy hour pricing, with manager tools for hiring, sales tracking, and secure login. Built in Java with an MVC structure and object-oriented design, and written entirely by hand without GenAI. It was a four-person group project for an upper-division software engineering course. I built the menu system, implemented the Happy Hour feature using the Decorator design pattern, added input validation, and wrote a large share of the unit tests. I learned how the right design pattern keeps a feature clean and extensible, and how much a shared codebase leans on solid encapsulation and testing when several people contribute.
A Java application that models the data behind a music streaming service. Users browse a music store, add albums and songs to a personal library, build and manage playlists, rate songs, and track their listening history across individual user accounts. The design separates the source catalog from each user's personal collection and follows an MVC structure, written entirely by hand without GenAI. It was a two-person project for the same software engineering course, and I implemented the majority of the system, including the core library model, playlist management, user accounts, and play tracking. It sharpened how I model a real domain with clear, well-separated classes, and showed me how a strong class design keeps a system easy to reason about and extend.
Other things I have built include WordHunt, a word game, and an experimental AI trivia game.
Layer 1 and layer 2 network work across client sites. I troubleshoot connectivity with Cisco IOS and Wireshark, install and terminate Cat6 and fiber, crimp RJ45, and validate links with toners, cable testers, and certification testers. I maintain MDF, IDF, and server room standards including rack layout, labeling, and cable management, handle TV installations, and keep clients updated through each job. The work has sharpened my troubleshooting instincts, attention to detail, and comfort communicating with clients under pressure.
Supported The Human Side of Organizations (BNAD 302) under Dr. Melendez, the first Eller College professor named a University Distinguished Outreach Professor. I graded memos, proctored early-morning exams, held office hours, and attended ethics workshops. It strengthened my communication, mentoring, and dependability, and gave me a firsthand look at how ethics plays out inside real organizations.
Cisco IOS · TCP/IP · subnetting · VLANs · OSPF · STP · NAT · ACLs · DHCP · DNS · HSRP · Wireshark · Cat6/fiber termination
Python · Java · C · JavaScript · TypeScript · Bash · SQL
OOP · MVC · design patterns · React · Next.js · Node.js · NestJS · MongoDB
Netmiko · Docker · Git · Linux · Tailscale · PuTTY · UniFi Controller · Packet Tracer · Windows Server
Leadership · communication · problem-solving · collaboration · attention to detail · adaptability
Led 50 participants on a 10-day international trip, managing logistics, scheduling, and group coordination.
Raised over $8,000 for the Children's Miracle Network through coordinated fundraising events.
Recognized as Most Outstanding Risk Management among 22 fraternities at the University of Arizona.
