About Me
I'm Anthony Ricevuto — a Computer Science student attending California State University, Long Beach, who blends technical problem-solving with creativity and exploration. I build systems at the intersection of data, physics, and intelligence — from predicting satellite-debris risks to detecting anomalies in aircraft before they occur. In addition to my personal projects, I am a Software Engineer for the Long Beach State Baseball Team, building data pipelines that parse and analyze TrackMan sensor data to generate reports for coaches. These tools transform complicated pitch and hit information into simple analytics that inform in-game decisions and evaluations of each team player, melding my baseball foundation with my love for data engineering and analysis. It is not the code that makes me different, but the mentality. The discipline, focus, and strategy I learned when I was playing college baseball I carry over and apply to my engineering work as well: remaining quick to adapt and under pressure; being a team player, and improving all the time. Aside from coding, you would typically find me playing guitar, taking my dog to the beach, and road-tripping through national parks with a backpack and a camera. Spending time outside reminds me that engineering is not just about technology, it's about creating tools that enable us to discover, explore, and stretch the limits of the possible. I want to build at the intersection of software, data, and discovery — systems that actually matter.
Featured Projects
Pre Flight Anomaly Detection
Developed and deployed a real-time aircraft sensor monitoring system using Azure Functions and Python that performs dynamic anomaly detection on critical flight parameters. The publicly accessible REST API uses Median Absolute Deviation statistical analysis to identify anomalous readings in RPM, temperature, pressure, and voltage data. Features dynamic model retraining and provides comprehensive normal operating range calculations for aviation safety applications.
View Project →Space Debris Risk Assessment
Developed a system to assess collision risk from orbital debris using real-time Two-Line Element (TLE) data. Parsed and propagated satellite trajectories with SGP4, applied a Kalman filter for state estimation, and trained a Random Forest classifier to label debris objects by collision risk level based on position and velocity. Enabled automated downloading, processing, and classification of space debris from CelesTrak.
View Project →Neil deGrasse Tyson AI Chatbot
An interactive AI chatbot powered by OpenAI that emulates the speaking style, knowledge, and enthusiasm of renowned astrophysicist Neil deGrasse Tyson. Engages users in conversations about space, science, and the cosmos with the characteristic wit and educational approach that Neil is famous for.
View Project →Get In Touch
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