Geographical Data & Symbolic Differentiation with Python

Hello PyData People!
We are excited to announce our next event of 2026! This time, we will be hosted at FlixBus Italia's headquarters in Milan for an evening dedicated to X
📅 When: Tuesday, June 23rd, 2026 – 18:30–21:00
📍 Where: FlixBus Italia, Corso Como 11, 20154 Milan
⚠️ Important: Spots are limited. Please keep your RSVP updated to allow others to participate if you can no longer attend.
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🕒 Agenda

• 18:30: 🚪 Doors open & Check-in
• 19:00: 🎤 Talk 1: Process geographical data in Python – Jacopo Farina
• 19:45: 🎤 Talk 2: Symbolic Differentiation of Matrices and N-Dimensional Arrays in SymPy – Francesco Bonazzi
• 20:30: 🍕 Networking & Social Dinner

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🎤 The Talks
1️⃣ Process geographical data in Python
Speaker: Jacopo Farina (Staff Data Engineer @ FlixBus)
In this talk, we are going to see how to process data from OpenStreetMap, public transport data (like the schedule from ATM) and other sources to calculate travel distances in a city, find addresses, find where and when people are using bike sharing (BikeMI) and nice things that can be done with this data. We are also going to see what OSM is and how it represents geographical data, how this data can be imported and used in Python (using PostGIS or DuckDB), how to process it and how to display the results.
About the Speaker:
Jacopo Farina is a data engineer at Flixbus, where he works on the demand forecasting, and a teacher at the Data Science Retreat in Berlin. Previously he worked on the backend using Node.js, reporting and ETL.
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2️⃣ Symbolic Differentiation of Matrices and N-Dimensional Arrays in SymPy
Speaker: Francesco Bonazzi (PhD in Physics and Software Developer @ Intesa Sanpaolo)
Numerical frameworks such as PyTorch and JAX routinely compute matrix and tensor gradients via reverse-mode automatic differentiation (backpropagation) to train neural networks. By contrast, there has been a notable absence of symbolic imple