PyData & Scientific Libraries Stack
8 talks from the 2026 edition.
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Array-Oriented Programming in Python: Libraries, Techniques, and Trade-offs ▶ Recording 📝 Transcript
Iason Krommydas
Python has become the dominant language for scientific computing and data science, largely due to powerful array libraries that enable high-performance numerical computation. This tutorial introduces array-oriented...
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Black Hole Stars: An Astronomical Mystery (Mostly) Solved with NumPyro and JAX ▶ Recording 📝 Transcript
Raphael Hviding
The James Webb Space Telescope has revealed a mysterious population of "Little Red Dots": extremely distant objects that have upended our understanding of the early Universe. However, revealing the true nature of...
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Demystifying Parallel Programming in Python: from CPU to quantum processors, including GPU and TPU ▶ Recording 📝 Transcript
Gaël Pegliasco
This talk provides a beginner-friendly overview of Python’s parallel programming ecosystem. You’ll discover the key libraries and techniques—JIT compilation, multithreading, multiprocessing, distributed computing,...
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Heat: scaling the Python scientific stack to HPC systems ▶ Recording 📝 Transcript
Claudia Comito, Thomas Saupe
Python’s scientific stack (NumPy/SciPy) is often confined to single-node execution. When datasets exceed local memory, researchers face a steep learning curve, typically choosing between complex manual distribution...
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Metashade: Compilerless Immediate-Mode Shader Generation in Pure Python ▶ Recording 📝 Transcript
Pavlo Penenko
Discover how to build a GPU shader generator in pure Python, without having to write a compiler. We start by discussing how Pythonic embedded domain-specific languages (EDSLs) can help address the common challenges...
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PyTorch and CPU-GPU Synchronizations ▶ Recording 📝 Transcript
Tomas Ruiz
CPU–GPU synchronizations are a subtle performance killer in PyTorch: they block the host, prevent the CPU from running ahead, and create GPU idle gaps. This talk explains what host-device synchronization is, how it’s...
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Simulating the World using SimPy: A practical Example ▶ Recording 📝 Transcript
Niklas
Modern systems are complex - and testing them in real environments is often expensive, risky, or simply not reproducible. Simulation is a practical way to explore behavior under controlled conditions: run scenarios,...
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State of In-Browser ML: WebAssembly, WebGPU, and the Modern Stack ▶ Recording 📝 Transcript
Oleh Kostromin, Iryna Kondrashchenko
What if you could run real data/ML workflows right in your browsers - sandboxed, with no installation or sending your data anywhere? Such an approach would have tons of benefits: it is easy to distribute, safer by...