If you have interest in NumPy, SciPy, Signal Processing, Simulation, DataFrames, Linear Programming (LP), Vehicle Routing Problems (VRP), or Graph Analysis, we'd love to hear what performance you're seeing and how you're measuring.

The rapidly evolving Python ecosystem presents increasing challenges for adapting code using traditional methods. Developers frequently need to rewrite applications to leverage new libraries, hardware architectures, and optimization techniques. To address this challenge, the Numba team is developing a superoptimizing compiler built on equality saturation-based term rewriting. This innovative approach enables domain experts to express and share optimizations without requiring extensive compiler expertise. This talk explores how Numba v2 enables sophisticated optimizations—from floating-point approximation and automatic GPU acceleration to energy-efficient multiplication for deep learning models—all through the familiar NumPy API. Join us to discover how Numba v2 is bringing superoptimization capabilities to the Python ecosystem.

Synthetic aviation fuels (SAFs) offer a pathway to improving efficiency, but high cost and volume requirements hinder property testing and increase risk of developing low-performing fuels. To promote productive SAF research, we used Fourier Transform Infrared (FTIR) spectra to train accurate, interpretable fuel property models. In this presentation, we will discuss how we leveraged standard Python libraries – NumPy, pandas, and scikit-learn – and Non-negative Matrix Factorization to decompose FTIR spectra and develop predictive models. Specifically, we will review the pipeline developed for preprocessing FTIR data, the ensemble models used for property prediction, and how the features correlate with physicochemical properties.

This talk presents zfit with the newest improvements, a general purpose distribution fitting library for complicated model building beyond fitting a normal distribution. The talk will cover all aspects of fitting with a focus on the strong model building part in zfit; composable distributions with sums, products and more, build and mix binned and unbinned, analytic and templated functions in multiple dimensions. This includes the creation of arbitrary, custom distributions with minimal effort that fulfils everyones need. Thanks to the numpy-like backend used by TensorFlow, zfit is highly performant by using JIT compiled code on CPUs and even GPUs, a showcase for scientific computing faster than numpy.

Rydberg atoms offer unique quantum properties that enable radio-frequency sensing capabilities distinct from any classical analogue; however, large parameter spaces and complex configurations make understanding and designing these quantum experiments challenging. Current solutions are often developed as in-house, closed-sourced software simulating a narrow range of problems. We present RydIQule, an open-source package leveraging tools of computational python in novel ways to model the behavior of these systems generally. We describe RydIQule’s approach to representing quantum systems using computational graphs and leveraging numpy broadcasting to define complete experiments. In addition to discussing the computational challenges RydIQule helps overcome, we outline how collaboration between physics and computational research backgrounds has led to this impactful tool.

This keynote will trace the personal journey of NumPy's development and the evolution of the SciPy community from 2001 to the present. Drawing on over two decades of involvement, I’ll reflect on how a small group of enthusiastic contributors grew into a vibrant, global ecosystem that now forms the foundation of scientific computing in Python. Through stories, milestones, and community moments, we’ll explore the challenges, breakthroughs, and collaborative spirit that shaped both NumPy and the SciPy conventions over the years.

Many scientists rely on NumPy for its simplicity and strong CPU performance, but scaling beyond a single node is challenging. The researchers at SLAC need to process massive datasets under tight beam time constraints, often needing to modify code on the fly. This is where cuPyNumeric comes in—a drop-in replacement for NumPy that distributes work across CPUs and GPUs. With its familiar NumPy interface, cuPyNumeric makes it easy to scale computations without rewriting code, helping scientists focus on their research instead of debugging. It’s a great example of how the SciPy ecosystem enables cutting-edge science.

Generative Artificial Intelligence (AI) is reshaping engineering education by offering students new ways to engage with complex concepts and content. Ethical concerns including bias, intellectual property, and plagiarism make Generative AI a controversial educational tool. Overreliance on AI may also lead to academic integrity issues, necessitating clear student codes of conduct that define acceptable use. As educators we should carefully design learning objectives to align with transferrable career skills in our fields. By practicing backward design with a focus on career-readiness skills, we can incorporate useful prompt engineering, rapid prototyping, and critical reasoning skills that incorporate generative AI. Engineering students want to develop essential career skills such as critical thinking, communication, and technology. This talk will focus on case studies for using generative AI and rapid prototyping for scientific computing in engineering courses for physics, programming, and technical writing. These courses include assignments and reading examples using NumPy, SciPy, Pandas, etc. in Jupyter notebooks. Embracing generative AI tools has helped students compare, evaluate, and discuss work that was inaccessible before generative AI. This talk explores strategies for using AI in engineering education while accomplishing learning objectives and giving students opportunities to practice career readiness skills.

Cubed is a framework for distributed processing of large arrays without a cluster. Designed to respect memory constraints at all times, Cubed can express any NumPy-like array operation as a series of embarrassingly-parallel, bounded-memory steps. By using Zarr as persistent storage between steps, Cubed can run in a serverless fashion on both a local machine and on a range of Cloud platforms. After explaining Cubed’s model, we will show how Cubed has been integrated with Xarray and demonstrate its performance on various large array geoscience workloads.

This talk walks all Pythonistas through recent CuPy feature development. Join me and hear my story on how an open-source novice started contributing to and helping CuPy over the years grow into a full-fledged, reliable, GPU-accelerated array library that covers most of NumPy, SciPy, and Numba functionalities.

Despite its reputation for being slow, Python is the leading language of scientific computing, which generally needs large-scale (fast) computations. This is because most scientific problems can be split into "metadata bookkeeping" and "number crunching," where the latter is performed by array-oriented (vectorized) calls into precompiled routines.

This tutorial is an introduction to array-oriented programming. We'll focus on techniques that are equally useful in any array library, with a particular focus on NumPy and JAX. You'll work in groups on four class projects: Conway's Game of Life using arrays, iterative computations on arrays, just-in-time (JIT) compilation for the Mandelbrot set, and exploring data in ragged arrays.