Women remain critically underrepresented in data science and Python communities, comprising only 15–22% of professionals globally and less than 3% of contributors to Python open-source projects. This disparity not only limits diversity but also represents a missed opportunity for innovation and community growth. This talk explores actionable strategies to address these gaps, drawing from my leadership in Women in AI at IBM, TechWomen mentorship, and initiatives with NumFOCUS. Attendees will gain insights and practical steps to create inclusive environments, foster diverse collaboration, and ensure the scientific Python community thrives by unlocking its full potential.

You'll be randomly paired with another conference attendee for a 5-minute chat. Non-cheesy icebreakers will be provided. Virtual and in-person attendees welcome!

Zoom link: https://numfocus-org.zoom.us/j/87475693685?pwd=XehxS6v7cYI63UsS2h9nBsrGzAhcDs.1 2025-07-10 13:15 until 2025-07-10 14:15

Generative AI has rapidly changed the landscape of computing and data education. Many learners are utilizing generative AI to assist in learning, so what should educators do to address the opportunities, risks, and potential for their use? The goal of this open discussion session is to bring together community members to unravel these pressing questions in order to not only improve learning outcomes in a variety of diverse contexts: not only students learning in a classroom setting, but also ed-tech or generative AI designers developing new user experiences that aim to improve human capacities, and even scientists interested in learning best practices for communicating results to stakeholders or creating learning materials for colleagues. The open discussion will include ample opportunity for community members to network with each other and build connections after the conference.

Collaborating on code and software is essential to open science—but it’s not always easy. Join this BoF for an interactive discussion on the real-world challenges of open source collaboration. We’ll explore common hurdles like Python packaging, contributing to existing codebases, and emerging issues around LLM-assisted development and AI-generated software contributions.

We’ll kick off with a brief overview of pyOpenSci—an inclusive community of Pythonistas, from novices to experts—working to make it easier to create, find, share, and contribute to reusable code. We’ll then facilitate small-group discussions and use an interactive Mentimeter survey to help you share your experiences and ideas.

Your feedback will directly shape pyOpenSci’s priorities for the coming year, as we build new programs and resources to support your work in the Python scientific ecosystem. Whether you’re just starting out or a seasoned developer, you’ll leave with clear ways to get involved and make an impact on the broader Python ecosystem in service of advancing scientific discovery.

Conferences serve as a way to connect groups of humans around common topics of interest. In the open source community, they have played a critical role in knowledge sharing, advancing technology, and fostering a sense of community. This is especially true for the global Python community. Times are changing, the political climate both in the US and abroad has drastically shifted making gathering in the real world much more complex. Advancements in technologies have changed the calculus on what is considered quality participation. Join us in this BoF to discuss these challenges and how we can continue to come together as a community.

NVIDIA’s CUDA platform has long been the backbone of high-performance GPU computing, but its power has historically been gated behind C and C++ expertise. With the recent introduction of native Python support, CUDA is more accessible to the programming language you know and love, ushering in a new era for scientific computing, data science, and AI development.

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.

The “napari-activelearning” plugin provides a framework to fine tune deep learning models for large-scale bioimage analysis, such as digital pathology Whole Slide Images (WSI). This plugin was developed with the motivation of easing the integration of deep learning tools into bioimage analysis workflows. This plugin implements the concept of Active Learning for reducing the time spent on labeling samples when fine tuning models. Because this plugin is integrated into Napari and leverages the use of next generation file formats (Zarr), it is suitable for fine tuning deep learning models on large-scale images with little image preparation.

AI assistants are evolving from simple Q&A bots to intelligent, multimodal, multilingual, and agentic systems capable of reasoning, retrieving, and autonomously acting. In this talk, we’ll showcase how to build a voice-enabled, multilingual, multimodal RAG (Retrieval-Augmented Generation) assistant using Gradio, OpenAI’s Whisper, LangChain, LangGraph, and FAISS. Our assistant will not only process voice and text inputs in multiple languages but also intelligently retrieve information from structured and unstructured data. We’ll demonstrate this with a flight search use case—leveraging a flight database for retrieval and, when necessary, autonomously searching external sources using LangGraph. You will gain practical insights into building scalable, adaptive AI assistants that move beyond static chatbots to autonomous agents that interact dynamically with users and the web.

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.

Designing tomorrow's materials requires understanding how atoms behave – a challenge that's both fascinating and incredibly complex. While machine learning offers exciting speedups in materials simulation, it often falls short, missing vital electronic structure information needed to connect theory with experimental results. This work introduces a powerful solution: Density Functional Tight Binding (DFTB), which, combined with the versatile tools of Scientific Python, allows us to understand the electronic behavior of materials while maintaining computational efficiency. In this talk, I will present our findings demonstrating how DFTB, coupled with readily available Python packages, allows for direct comparison between theoretical predictions and experimental data, such as XPS measurements. I will also showcase our publicly available repository, containing DFTB parameters for a wide range of materials, making this powerful approach accessible to the broader research community.

This talk explores various methods to accelerate traditional machine learning pipelines using scikit-learn, UMAP, and HDBSCAN on GPUs. We will contrast the experimental Array API Standard support layer in scikit-learn with the cuML library from the NVIDIA RAPIDS Data Science stack, including its zero-code change acceleration capability. ML and data science practitioners will learn how to seamlessly accelerate machine learning workflows, highlight performance benefits, and receive practical guidance for different problem types and sizes. Insights into minimizing cost and runtime by effectively mixing hardware for various tasks, as well as the current implementation status and future plans for these acceleration methods, will be provided.

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.

Xarray has enormous potential as a data model and toolkit for labeled N-D arrays in biology. Originally developed within the geosciences community, it is seeing increased usage in biology, with applications ranging from genomics to image analysis and beyond. However, it has not yet been widely adopted. This presentation will investigate what the blockers have been to wider adoption, showcase the power of Xarray in biology through existing use cases, and present a roadmap for the future of Xarray in biological workflows through recent and upcoming improvements in Xarray.

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.

Join us for an evening of connection and creativity at the Museum of Glass, just a short walk from the conference venue. Explore stunning glass art exhibits, enjoy refreshments, and connect with fellow SciPy attendees in a unique and inspiring setting.

The virtual poster session takes place 6:00–7:00 p.m. in the SciPy 2025 Gather space. Meet with the poster authors to ask questions and learn about the posters that will be on display in the poster hall inside our virtual space. Virtual and in-person ticket holders are all welcome to attend in Gather!

The Poster session will be in the Ballroom from 6:00-7:00pm. Meet with the poster authors to ask questions and learn about the posters that will be on display throughout the main conference.

Lightning talks are 5-minute talks on any topic of interest for the SciPy community. We encourage spontaneous and prepared talks from everyone, but we can’t guarantee spots. Sign ups are at the NumFOCUS booth during the conference.