I really like metacognition. I like ideas that are based on real life better than theory. I also get things better when I can see them. After I got my bachelor's degree, I usually spent my free time learning on my own, from trying out Linux to learning about artificial intelligence.
My background is in electrical and electronics engineering. I have hands-on experience using technical tools to visualize and explore complex systems. I learn best by building things and testing my assumptions rather than just reading about them.
My deeper interest now is in artificial intelligence, systems, and the broader world of technology that shapes how we live and work. I am preparing for further study and exploring where in tech or research I want to focus next. I do not claim to be an expert in any of these areas. I am still figuring things out.
A small weather app that tries to be honest about where its numbers come from. It shows a seven-day forecast from Open-Meteo, which is driven by the ECMWF IFS model and needs no API key. Your city is worked out on the server from your IP address using ipapi.co, and the IP is thrown away straight after; the page never asks the browser for your precise location. If a public webcam covers the same area, the app can pull a recent frame and run a simple brightness and edge check against the forecast for that moment, so you can see whether the sky in the image looks roughly like what the model predicts. The live page below loads a fresh webcam frame and a fresh forecast for the same coordinates at the same UTC timestamp, so the cross-check is actually real-time rather than a screenshot.
What I did: Built the frontend in Next.js 15 with Tailwind and Recharts, and the backend in FastAPI. Wrote the server-side IP-to-city resolver that discards the IP after use, the Open-Meteo client, and a small computer-vision routine (HSV colour statistics plus a Sobel edge check) that compares a webcam frame against the forecast for the same coordinates. Added an ensemble-fusion layer with stub adapters for OpenWeatherMap, NVIDIA Earth-2, and Google MetNet-3 so additional sources can be plugged in later. Limited the webcam sources to feeds that are explicitly public, including Statens vegvesen under NLOD, NOAA GLERL, and US DOT 511.
What I learned: How much of a weather app's user experience is really about trust: where the forecast comes from, how the location is chosen, and whether the picture on screen matches reality. I also learned a lot about keeping a project small on purpose, shipping a clear v0.1, and writing code so that the parts that touch the user's data are easy to read and verify.
After receiving acceptance into the Master of Science in Communications Engineering program at Polytechnic University of Turin, Italy, I initiated these projects as a self-directed learner.
A hands-on orbital propagation toolkit built around SGP4 and dSGP4. I use it to work with TLE data, track satellites, visualize orbits, and test how drag affects the results. If SGP4 fails on a particular input, it falls back to a simpler two-body model so nothing crashes.
What I did: Implemented TLE parsing and validation utilities, an educational reference SGP4 propagator with extra numerical-stability checks, a DifferentiableSGP4 PyTorch module for gradient-based analysis (inspired by ESA's dSGP4 project), and a LiveSGP4 tracker with automatic two-body fallback, detailed error history, and a validation test suite.
What I learned: How to work through complex mathematical models by implementing them in code, the value of delegating core propagation to trusted open-source libraries like the sgp4 package while building higher-level tooling on top, and how to make technical concepts approachable through visualization.
This was a personal learning project, not a formal research appointment or professional aerospace experience.
A mobile-first web app that predicts LEO satellite passes for any location and calculates whether the link margin is strong enough to hold a connection. Given a satellite (by NORAD ID or name) and an observer position, it returns upcoming pass times, SNR and link-budget curves over each pass, a timeline chart, and supports ICS calendar export and PDF reports. Built with a FastAPI/Python backend and a Next.js frontend.
A web application that fetches live TLE data from CelesTrak and visualizes thousands of satellites orbiting in real time. It uses Skyfield for orbit computation and an adaptive time-stepping algorithm for pass prediction, with a React frontend and a Python backend. The main challenge was keeping the rendering performant while tracking thousands of moving objects without freezing the browser.