BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//cfp.scipy.org//2023 BEGIN:VTIMEZONE TZID:CST BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20061029T080000Z TZNAME:CST TZOFFSETFROM:-0500 TZOFFSETTO:-0600 END:STANDARD BEGIN:STANDARD DTSTART:20071104T030000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST TZOFFSETFROM:-0500 TZOFFSETTO:-0600 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000402T030000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060402T090000Z TZNAME:CDT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20070311T030000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-2023-CDRJYE@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:Enjoy a gentle introduction to Python for folks who are complet ely new to it and may not have much experience programming. Learn how to w rite Python while practicing loops\, if’s\, functions\, and usage of Pyt hon’s built-in features in a series of fun\, interactive exercises insid e Jupyter Notebooks. By the end you’ll be ready to write your own basic Python -- but most importantly\, I want you to learn the form and vocabula ry of Python so that you can understand Python documentation\, interpret c ode written by others\, and get the most out of other SciPy tutorials. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:Introduction to Python and Programming - Matt Davis URL:https://cfp.scipy.org/2023/talk/CDRJYE/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-CJUYJM@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:Communicating scientific data often relies on making comparison s between multiple datasets.\nJoin the Matplotlib team to learn about crea ting multi-axis figures to display such data side-by-side.\nThis intermedi ate level tutorial will cover a variety of tools for making multi-axis fig ures.\nOf particular focus will be the [subplot_mosaic](https://matplotlib .org/stable/gallery/subplots_axes_and_figures/mosaic.html) and the layout engines: tight\, constrained\, and compressed.\nThis tutorial will emphasi ze the use of Matplotlib's Object Oriented (OO) API and why that is genera lly recommended over the pyplot (plt) API. DTSTAMP:20260607T101922Z LOCATION:Classroom 101 SUMMARY:Mosaic Magic with Matplotlib - Kyle Sunden URL:https://cfp.scipy.org/2023/talk/CJUYJM/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-DDJTZL@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:One of the key questions in modern data science and machine lea rning\, for businesses and practitioners alike\, is how do you move machin e learning projects from prototype and experiment to production as a repea table process. In this workshop\, we present an introduction to the landsc ape of production-grade tools\, techniques\, and workflows that bridge the gap between laptop data science and production ML workflows. DTSTAMP:20260607T101922Z LOCATION:Classroom 106 SUMMARY:Full-stack Machine Learning for Data Scientists - Savin Goyal\, Hug o Bowne-Anderson URL:https://cfp.scipy.org/2023/talk/DDJTZL/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-B9CHA7@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:Privacy guarantee is **the** most crucial requirement when it c omes to analyse sensitive data. However\, data anonymisation techniques al one do not always provide complete privacy protection\; moreover Machine L earning models could also be exploited to _leak_ sensitive data when _atta cked_\, and no counter-measure is applied. *Privacy-preserving machine lea rning* (PPML) methods hold the promise to overcome all these issues\, allo wing to train machine learning models with full privacy guarantees. In thi s tutorial we will explore several methods for privacy-preserving data ana lysis\, and how these techniques can be used to safely train ML models _wi thout_ actually seeing the data. DTSTAMP:20260607T101922Z LOCATION:Classroom 203 SUMMARY:PPML: Machine Learning on data you cannot see - Valerio Maggio URL:https://cfp.scipy.org/2023/talk/B9CHA7/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-ZCUDYT@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:In this tutorial\, attendees will learn hands-on how to optimiz e the trajectory of a self-landing rocket in a real-time simulated setting using CVXPY\, a Python-embedded modeling language for convex optimization . We integrate the optimization with the Kerbal Space Program\, to showcas e a complete landing mission without human intervention\, ideally in one p iece. CVXPY allows solving complex problems declaratively\, letting convex optimization find an optimal way of meeting target conditions with respec t to an objective function. After solving the initial problem\, attendees will use a selection of advanced CVXPY features while making the example g radually more realistic. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:Controlling Self-Landing Rockets Using CVXPY - Philipp Schiele\, St even Diamond\, Eric Sager Luxenberg URL:https://cfp.scipy.org/2023/talk/ZCUDYT/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NEUUKG@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:Between telescopes and satellite cameras and MRI machines and m icroscopes\, scientists are producing more images than they can realistica lly look at. They need specialized viewers for multi-dimensional images\, and automated tools to help process those images into knowledge. In this t utorial\, we will cover the fundamentals of algorithmic image analysis\, s tarting with how to think of images as NumPy arrays\, moving on to basic i mage filtering\, and finishing with a complete workflow: segmenting a 3D i mage into regions and making measurements on those regions. At every step\ , we will visualize and understand our work using matplotlib and napari. DTSTAMP:20260607T101922Z LOCATION:Classroom 202 SUMMARY:image analysis and visualization in Python with scikit-image\, napa ri\, and friends - Juan Nunez-Iglesias\, Lars Grüter\, Kira Evans URL:https://cfp.scipy.org/2023/talk/NEUUKG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VBZ9PN@cfp.scipy.org DTSTART;TZID=CST:20230710T080000 DTEND;TZID=CST:20230710T120000 DESCRIPTION:This tutorial is an introduction to Pydantic\, a library for da ta validation and settings management using Python type annotations. Using a semi-realistic ML and / or scientific software pipeline scenario we dem onstrate how Pydantic can be used to support type validations for scientif ic data structures\, APIs and configuration systems. We show how the use o f Pydantic in scientific and ML software leads to a more pleasant user exp erience as well as more robust and easier to maintain code. A minimum know ledge of Python type annotations\, class definitions and data structures w ill be helpful\nfor beginners but not required. DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:Building better data structures\, APIs and configuration systems fo r scientific software using Pydantic - Nick Langellier\, Axel Donath URL:https://cfp.scipy.org/2023/talk/VBZ9PN/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-CQRYUC@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:This tutorial session is intended to give attendees a gentle in troduction to applying causal thinking and causal inference to data using python. Causal data analysis is very common in many academic domains (e.g. in social psychology\, epidemiology\, macroeconomics\, etc) as well as in industry (all of the largest Silicon Valley tech companies employ teams o f scientists who answer business questions purely with causal inference me thods). The tutorial will involve a combination of presentations with open Q&A and hands-on exercises contained in Google Colab notebooks. DTSTAMP:20260607T101922Z LOCATION:Classroom 203 SUMMARY:Introduction to Causal Inference - Roni Kobrosly URL:https://cfp.scipy.org/2023/talk/CQRYUC/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-RKV3PZ@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:Visual Studio Code (VS Code) is a free code editor that runs on Windows\, Linux\, macOS and in your browser. This tutorial aims at Python programmers of all levels who are already using VS Code or are interested in doing so\, and will take them from zero (installing VS Code) to a prod uction setup for Python development. We will cover starter topics\, such a s customizing the UI and extensions\, using code autocomplete\, code navig ation\, debugging\, and Jupyter Notebooks. We will also go into advanced u se cases\, such as remote development\, pair programming via Live Share\, Dev containers\, GitHub Codespaces & more. DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:Meet your coding best friend: VS Code💖 - A hands-on tutorial on how to get the most out of the world’s most popular Python editor - Guen Prawiroatmodjo\, Sarah Kaiser\, Leopold Talirz URL:https://cfp.scipy.org/2023/talk/RKV3PZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-UJBWPQ@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algo rithms clearly and efficiently. We'll introduce basic array syntax and arr ay indexing\, review some of the available mathematical functions in NumPy \, and discuss how to write your own routines. DTSTAMP:20260607T101922Z LOCATION:Classroom 202 SUMMARY:Introduction to Numerical Computing With NumPy - Sandhya Govindaraj u URL:https://cfp.scipy.org/2023/talk/UJBWPQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-8BZN3E@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:We will kick off this tutorial with an introduction to deep lea rning and highlight its primary strengths and use cases compared to tradit ional machine learning. In recent years\, PyTorch has emerged as the most widely used deep learning library for research. However\, a lot has change d regarding how we train neural networks these days. After getting a firm grasp of the PyTorch API\, you will learn how to train deep neural network s using various multi-GPU training paradigms. We will also fine-tune large language models (transformers) and deploy them to the cloud. DTSTAMP:20260607T101922Z LOCATION:Classroom 106 SUMMARY:Modern Deep Learning with PyTorch - Sebastian Raschka URL:https://cfp.scipy.org/2023/talk/8BZN3E/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-7BRY3J@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:Machine learning (ML) pipelines involve a variety of computatio nally intensive stages. As state-of-the-art models and systems demand more compute\, there is an urgent need for adaptable tools to scale ML workloa ds. This idea drove the creation of Ray—an open source\, distributed ML compute framework that not only powers systems like ChatGPT but also pushe s theoretical computing benchmarks. Ray AIR is especially useful for paral lelizing ML workloads such as pre-processing images\, model training and f inetuning\, and batch inference. In this tutorial\, participants will lear n about AIR’s composable APIs through hands-on coding exercises. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:Scalable machine learning workloads with Ray AI Runtime - Emmy Li\, Adam Breindel URL:https://cfp.scipy.org/2023/talk/7BRY3J/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NFWZXD@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:Already familiar with ipywidgets\, but ready to take your skill s to the next level? In this tutorial we walk through what it takes to tr ansform an exploratory Jupyter Notebook into a mature web application. Web apps can be a valuable product of collaboration between researchers and s oftware developers\, and the packages used in this tutorial were selected to support this relationship\, starting with using JupyterLab as an integr ated development environment. Attendees will learn how to design and docum ent a scientific web application that accommodates increasing complexity\, but is also inheritable by the researchers who maintain them in the long run. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:How the Little Jupyter Notebook Became a Web App: Managing Increasi ng Complexity with nbdev - Nicole Brewer\, Ludovico Bianchi URL:https://cfp.scipy.org/2023/talk/NFWZXD/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LZPDBD@cfp.scipy.org DTSTART;TZID=CST:20230710T133000 DTEND;TZID=CST:20230710T173000 DESCRIPTION:[PyVista](https://github.com/pyvista/pyvista) is a general purp ose 3D visualization library used for over 1400+ open source projects for the visualization of everything from [computer aided engineering and geoph ysics to volcanoes and digital artwork](https://dev.pyvista.org/getting-st arted/external_examples.html).\n\nPyVista exposes a Pythonic API to the [V isualization Toolkit (VTK)](http://www.vtk.org) to provide tooling that is immediately usable without any prior knowledge of VTK and is being built as the 3D equivalent of Matplotlib\, with plugins to Jupyter to enable vis ualization of 3D data using both server- and client-side rendering. DTSTAMP:20260607T101922Z LOCATION:Classroom 101 SUMMARY:3D Visualization with PyVista - Alexander Kaszynski\, Tetsuo Koyama \, Bane Sullivan URL:https://cfp.scipy.org/2023/talk/LZPDBD/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-ALSYBR@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:While most scientists aren't at the scale of black hole imaging research teams that analyze Petabytes of data every day\, you can easily fall into a situation where your laptop doesn't have quite enough power to do the analytics you need.\n\nIn this hands-on tutorial\, you will learn the fundamentals of analyzing massive datasets with real-world examples on actual powerful machines on a public cloud provided by the presenters – starting from how the data is stored and read\, to how it is processed an d visualized. DTSTAMP:20260607T101922Z LOCATION:Classroom 203 SUMMARY:Data of an Unusual Size: A practical guide to analysis and interact ive visualization of massive datasets - Pavithra Eswaramoorthy\, Dharhas P othina\, Christopher Ostrouchov URL:https://cfp.scipy.org/2023/talk/ALSYBR/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VKXXNH@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:This tutorial will show you how to use the Pandas or Xarray API s you already know to interactively explore and visualize your data even i f it is big\, streaming\, or multidimensional. Then just replace your expr ession arguments with widgets to get a web app that you can share as HTML+ WASM or backed by a live Python server. These tools let you focus on your data rather than the API\, and let you build linked\, interactive drill-d own exploratory apps without having to run a web-technology software devel opment project\, which you can then share without becoming an operations s pecialist. DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:hvPlot and Panel: Visualize all your data easily\, from notebooks t o dashboards - Sophia Yang\, James A. Bednar URL:https://cfp.scipy.org/2023/talk/VKXXNH/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-XBUC8S@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:Despite its reputation for being slow\, Python is the leading l anguage 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 perfor med by array-oriented (vectorized) calls into precompiled routines.\n\nThi s tutorial is an introduction to array-oriented programming. We'll focus o n techniques that are equally useful in NumPy\, Pandas\, xarray\, CuPy\, A wkward Array\, and other libraries\, and we'll work in groups on three cla ss projects: Conway's Game of Life\, evaluating decision trees\, and compu tations on ragged arrays. DTSTAMP:20260607T101922Z LOCATION:Classroom 101 SUMMARY:Thinking in arrays - Jim Pivarski URL:https://cfp.scipy.org/2023/talk/XBUC8S/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-GQ7PG3@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:This tutorial is an introduction to cloud-based geospatial anal ysis with Earth Engine and the geemap Python package. We will cover the ba sics of Earth Engine data types and how to visualize\, analyze\, and expor t Earth Engine data in a Jupyter environment using geemap. We will also de monstrate how to develop and deploy interactive Earth Engine web apps. Thr oughout the session\, practical examples and hands-on exercises will be pr ovided to enhance learning. The attendees should have a basic understandin g of Python and Jupyter Notebooks. Familiarity with Earth science and geos patial datasets is not required\, but will be useful. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:An Introduction to Cloud-Based Geospatial Analysis with Earth Engin e and Geemap - Steve Greenberg\, Qiusheng Wu URL:https://cfp.scipy.org/2023/talk/GQ7PG3/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NDYWUR@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:In this workshop\, we will introduce Numba - a JIT compiler tha t is designed to speed up numerical calculations. Most people found all of it is like a mystery - It sounds like magic\, but how does it work? Under what conditions does it work? And because of it\, new users found it hard to start using it and it requires a steep learning curve to get the hang of it. This workshop will provide all the knowledge that you need to make Numba works for you. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:Power up your work with compiling and profiling - Cheuk Ting Ho URL:https://cfp.scipy.org/2023/talk/NDYWUR/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-7NLG3F@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:This tutorial introduces Keras\, a powerful deep learning libra ry and demonstrates how to enable generative models using Keras. The first part delves into the Keras training pipeline and extended modules. The se cond part explores image generative models using stable diffusion\, with l ive coding examples to generate novel images and teach the model new conce pts. Finally\, you'll explore language generative models\, including GPT a nd BART\, with a live coding example that demonstrates how to enable these models. By the end of this tutorial\, you'll have a solid understanding o f how to harness Keras to create powerful AI applications. DTSTAMP:20260607T101922Z LOCATION:Classroom 202 SUMMARY:Explore generative models in AI with Keras - Divyashree Shivakumar Sreepathihalli\, Chen Qian URL:https://cfp.scipy.org/2023/talk/7NLG3F/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-8TAA7K@cfp.scipy.org DTSTART;TZID=CST:20230711T080000 DTEND;TZID=CST:20230711T120000 DESCRIPTION:Pandas can be tricky\, and there is a lot of bad advice floatin g around. This tutorial will cut through some of the biggest issues I've s een with Pandas code after working with the library for a while and writin g three books on it.\n\nWe will discuss:\n\n* Proper types\n* Chaining\n* Aggregation\n* Debugging DTSTAMP:20260607T101922Z LOCATION:Classroom 106 SUMMARY:Idiomatic Pandas - Matt Harrison URL:https://cfp.scipy.org/2023/talk/8TAA7K/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-QXAYRM@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:Xarray provides data structures for multi-dimensional labeled a rrays and a toolkit for scalable data analysis on large\, complex datasets with many related variables. Xarray combines the convenience of labeled d ata structures inspired by Pandas with NumPy-like multi-dimensional arrays to provide an intuitive and scalable interface for scientific analysis. T his tutorial will introduce data scientists already familiar with Xarray t o more intermediate and advanced topics\, such as applying functions in Sc iPy/NumPy with no Xarray equivalent\, advanced indexing concepts\, and wra pping other array types in the scientific Python ecosystem. DTSTAMP:20260607T101922Z LOCATION:Classroom 203 SUMMARY:Xarray: Friendly\, Interactive\, and Scalable Scientific Data Analy sis - Deepak Cherian\, Thomas Nicholas\, Anderson Banihirwe\, Jessica Sche ick\, Don Setiawan\, Scott Henderson\, Negin Sobhani URL:https://cfp.scipy.org/2023/talk/QXAYRM/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-MQQJKG@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:Dask is a Python library for scaling and parallelizing Python c ode. It provides familiar\, high-level interfaces to extend the SciPy ecos ystem to larger-than-memory or distributed environments\, as well as lower -level interfaces for parallelizing custom algorithms. In this tutorial\, we’ll cover advanced features of Dask like applying custom operations to Dask DataFrames and arrays\, debugging computations\, diagnosing performa nce issues\, and more. Attendees should walk away with a deeper understand ing of Dask’s internals\, an introduction to more advanced features\, an d ideas of how they can apply these features effectively to their own work loads. DTSTAMP:20260607T101922Z LOCATION:Classroom 106 SUMMARY:Advanced Dask Tutorial - Naty Clementi\, James Bourbeau\, Julia Sig nell\, Charles Blackmon-Luca URL:https://cfp.scipy.org/2023/talk/MQQJKG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-F3HAUQ@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:Resampling and Monte Carlo statistical techniques are surprisin gly intuitive\, and they are often more flexible and accurate than their b etter-known analytical counterparts. In this tutorial\, participants will develop their intuitive understanding of frequentist statistics and apply it using three functions in `scipy.stats` - `monte_carlo_test`\, `permutat ion_test`\, and `bootstrap` - to dramatically expand the statistical analy ses they can perform with the SciPy Library. DTSTAMP:20260607T101922Z LOCATION:Classroom 202 SUMMARY:Resampling and Monte Carlo Methods in SciPy.stats - Matt Haberland\ , Albert Steppi URL:https://cfp.scipy.org/2023/talk/F3HAUQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-C9QZXU@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:Bokeh is a library for interactive data visualization. You can use it with Jupyter Notebooks or create standalone web applications\, all using Python. This tutorial is a complete guide to Bokeh\, where we start with a basic line plot and step-by-step make our way to creating a dashboa rd with several interacting components. This tutorial will be helpful for scientists who are looking to level-up their analysis and presentations\, and tool developers interested in adding custom plotting functionally or d ashboards. DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:Interactive data visualization with Bokeh - Pavithra Eswaramoorthy\ , Ian Thomas\, Bryan Van de Ven\, Timo Metzger\, Victoria Adesoba URL:https://cfp.scipy.org/2023/talk/C9QZXU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-YHEYVY@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:One of the biggest challenges for data scientists and machine l earning engineers alike is the friction caused by the iteration cycle betw een prototyping and production. It’s not enough to deploy a working mode l to a serving app. The iterative process itself needs to be a tight feedb ack loop between experimentation\, data and model refinement\, deploying t o production\, and dealing with data drift. In this tutorial\, attendees w ill learn how to unify the common tools in the Python Data/ML scientific s tack into a single orchestration plane using Flyte so that you can reduce the friction between prototyping and production. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:A Hands-on Introduction to Production-grade Data Science Orchestrat ion with Flyte - Niels Bantilan URL:https://cfp.scipy.org/2023/talk/YHEYVY/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LJQPVT@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:SymPy is a Python library for symbolic mathematics. This tutori al will introduce SymPy to a beginner audience. It will cover an introduct ion to symbolic computing\, basic operations\, simplification\, calculus\, matrices\, advanced expression manipulation\, code generation\, and selec ted advanced topics. The tutorial does not have any prerequisites beyond k nowledge of Python and basic freshman level mathematics. It will be presen ted with Jupyter notebooks with regular exercises for the attendees. After attending this tutorial\, attendees will be able to start using SymPy to solve their own problems. DTSTAMP:20260607T101922Z LOCATION:Classroom 101 SUMMARY:SymPy Introductory Tutorial - Sangyub Lee\, Aaron Meurer\, Anutosh Bhat URL:https://cfp.scipy.org/2023/talk/LJQPVT/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-PTB7DU@cfp.scipy.org DTSTART;TZID=CST:20230711T133000 DTEND;TZID=CST:20230711T173000 DESCRIPTION:We love Python but maybe not enough to commit to an entire codi ng language. What if we could understand the fundamentals and begin workin g with real-time data in a single session? Actionable python scripts and u nderstanding the frameworks might be enough to be a springboard for larger exploration projects. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:Python for answering geospatial questions: exploring social inequit y in our communities - bonny p mcclain URL:https://cfp.scipy.org/2023/talk/PTB7DU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-Y8PPAA@cfp.scipy.org DTSTART;TZID=CST:20230711T183000 DTEND;TZID=CST:20230711T203000 DESCRIPTION:SciPy Welcome Reception hosted by Enthought. Tuesday\, July 11\ , 6:30-8:30 at Enthought HQ\, 200 W Cesar Chavez\, Austin. Meet fellow att endees! Food and drinks served! \n\n[Walk](https://www.google.com/maps/dir /AT%26T+Hotel+and+Conference+Center\,+University+Avenue\,+Austin\,+TX/Enth ought\,+200+W+Cesar+Chavez+St+Suite+202\,+Austin\,+TX+78701/@30.272726\,-9 7.7524166\,15z/data=!3m2!4b1!5s0x8644b508a6554d83:0x7edf0a3a6fece735!4m18! 4m17!1m5!1m1!1s0x8644b59de7f3c8cf:0x7ef52b1ad3321879!2m2!1d-97.7404423!2d3 0.2816145!1m5!1m1!1s0x8644b509cdd787e9:0x108b9372002d7f55!2m2!1d-97.746398 5!2d30.2642596!2m3!6e1!7e2!8j1689100200!3e2?entry=ttu)\, get a ride\, or [ take the bus](https://www.google.com/maps/dir/AT%26T+Hotel+and+Conference+ Center\,+University+Avenue\,+Austin\,+TX/Enthought\,+200+W+Cesar+Chavez+St +Suite+202\,+Austin\,+TX+78701/@30.2737123\,-97.7521933\,15z/data=!3m1!5s0 x8644b508a6554d83:0x7edf0a3a6fece735!4m19!4m18!1m5!1m1!1s0x8644b59de7f3c8c f:0x7ef52b1ad3321879!2m2!1d-97.7404423!2d30.2816145!1m5!1m1!1s0x8644b509cd d787e9:0x108b9372002d7f55!2m2!1d-97.7463985!2d30.2642596!2m3!6e1!7e2!8j168 9100200!3e3!5i3?entry=ttu&utm_medium=s2email&shorturl=1) with [CapMetro](h ttps://www.capmetro.org/app)! DTSTAMP:20260607T101922Z LOCATION:Enthought - 200 W Cesar Chavez St SUMMARY:SciPy Welcome Reception - 200 W Cesar Chavez URL:https://cfp.scipy.org/2023/talk/Y8PPAA/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-X7YH7A@cfp.scipy.org DTSTART;TZID=CST:20230712T091500 DTEND;TZID=CST:20230712T100000 DESCRIPTION:Michael Droettboom is a Principal Software Engineering Manager at Microsoft where he leads the CPython Performance Engineering Team. That team contributes directly to the upstream CPython project\, and recently helped make Python 3.11 up to 60% faster than 3.10.\n\nMichael has been co ntributing to open source for over 25 years: he is the former lead maintai ner of matplotlib\, a major contributor to astropy\, and he is the origina l author of Pyodide and airspeed velocity. His work has supported such div erse applications as the Hubble and James Webb Space Telescopes\, the Fire fox web browser\, infrared retinal imaging\, and optical sheet music recog nition. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Keynote - Open Source Contributors in Space and Time - Michael Droe ttboom URL:https://cfp.scipy.org/2023/talk/X7YH7A/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-333PY7@cfp.scipy.org DTSTART;TZID=CST:20230712T104500 DTEND;TZID=CST:20230712T111500 DESCRIPTION:Research on animal acoustic communication is being revolutioniz ed by deep learning. In this talk we present vak\, a framework that allows researchers in this area to easily benchmark deep neural network models a nd apply them to their own data. We'll demonstrate how research groups are using vak through examples with TweetyNet\, a model that automates annota tion of birdsong by segmenting spectrograms. Then we'll show how adopting Lightning as a backend in version 1.0 has allowed us to incorporate more m odels and features\, building on the foundation we put in place with help from the scientific Python stack. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:vak: a neural network framework for researchers studying animal aco ustic communication - Yarden Cohen\, David Nicholson URL:https://cfp.scipy.org/2023/talk/333PY7/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-DF8PVV@cfp.scipy.org DTSTART;TZID=CST:20230712T104500 DTEND;TZID=CST:20230712T111500 DESCRIPTION:While the NumPy C API lets developers write C that builds or ev aluates arrays\, just writing C is often not enough to outperform NumPy. N umPy's usage of Single Instruction Multiple Data routines\, as well as mul ti-source compiling\, provide optimizations that are impossible to beat wi th simple C. This presentation offers principles to help determine if an a rray-processing routine\, implemented as a C-extension\, might outperform NumPy called from Python. A C-extension implementing a narrow use case of the ``np.nonzero()`` routine will be studied as an example. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Out-Performing NumPy is Hard: When and How to Try with Your Own C-E xtensions - Christopher Ariza URL:https://cfp.scipy.org/2023/talk/DF8PVV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-G9P3AG@cfp.scipy.org DTSTART;TZID=CST:20230712T104500 DTEND;TZID=CST:20230712T111500 DESCRIPTION:N-dimensional datasets are common in many scientific fields\, a nd quickly accessing subsets of these datasets is critical for an efficien t exploration experience. Blosc2 is a compression and format library that recently added support for multidimensional datasets. Compression is cruci al in effectively dealing with sparse datasets as the zeroed parts can be almost entirely suppressed\, while the non-zero parts can still be stored in smaller sizes than their uncompressed counterparts. Moreover\, the new double data partition in Blosc2 reduces the need for decompressing unneces sary data\, which allows for top-class slicing speed. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Fast Exploration of the Milky Way (or any other n-dimensional datas et) - Francesc Alted URL:https://cfp.scipy.org/2023/talk/G9P3AG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LAKM79@cfp.scipy.org DTSTART;TZID=CST:20230712T112500 DTEND;TZID=CST:20230712T115500 DESCRIPTION:The NASA Atmosphere SIPS\, located at the University of Wiscons in\, is responsible for producing operational cloud and aerosol scientific products from satellite observations. With decades of satellite observati ons\, new scientific algorithms are employing Machine Learning (ML) method s to improve processing efficiencies and scientific analyses. In preparati on for future developments\, we are working with NASA Atmospheric Science Teams to understand ML requirements and assist in developing new tools tha t will benefit both the Science Teams and the broader Open-Source Science community. This talk will step through a ML methodology being used to iden tify cloud types and severe aerosols. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:A Computer Vision (ML) Approach to Classifying Clouds and Aerosols from Satellite Observations - Steve Dutcher URL:https://cfp.scipy.org/2023/talk/LAKM79/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VUFGS8@cfp.scipy.org DTSTART;TZID=CST:20230712T112500 DTEND;TZID=CST:20230712T115500 DESCRIPTION:Numerical Python libraries can run computations on many CPU cor es with various parallel interfaces. When we simultaneously use multiple l evels of parallelism\, it may result in oversubscription and degraded perf ormance. This talk explores the programming interfaces used to control par allelism exposed by libraries such as NumPy\, SciPy\, and scikit-learn. We will learn about parallel primitives used in these libraries\, such as Op enMP and Python's multiprocessing module. We will see how to control paral lelism in these libraries to avoid oversubscription. Finally\, we will loo k at the overall landscape for configuring parallelism and highlight paths for improving the user experience. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Can There Be Too Much Parallelism? - Thomas J. Fan URL:https://cfp.scipy.org/2023/talk/VUFGS8/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-AQ3Z3U@cfp.scipy.org DTSTART;TZID=CST:20230712T112500 DTEND;TZID=CST:20230712T115500 DESCRIPTION:An important problem in genomics is identifying the proteins th at bind to DNA. Although many methods attempt to learn DNA motifs underlyi ng protein binding as position-weight matrices (PWMs)\, these PWMs cannot faithfully represent real biology. For instance\, a static PWM cannot desc ribe a zinc-finger protein whose fingers can optionally include one-nucleo tide spacing. TF-MoDISco is a framework for extracting motifs using attrib ution scores from a machine-learning model. The learned motifs and syntax overcome many of the limitations presented by PWM. I will describe the TF- MoDISco algorithm and showcase its efficient re-implementation\, tfmodisco -lite. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:tfmodisco-lite: an attribution-based motif discovery algorithm - Ja cob Schreiber URL:https://cfp.scipy.org/2023/talk/AQ3Z3U/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-3NBFHV@cfp.scipy.org DTSTART;TZID=CST:20230712T131500 DTEND;TZID=CST:20230712T134500 DESCRIPTION:The Scientific Python project aims to better coordinate the eco system and grow the community. Come hear about our recent progress and our plans for the coming year! DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Scientific Python: from `__init__` to `__call__` - Juanita Gomez URL:https://cfp.scipy.org/2023/talk/3NBFHV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-T3XSHX@cfp.scipy.org DTSTART;TZID=CST:20230712T131500 DTEND;TZID=CST:20230712T134500 DESCRIPTION:Data quality remains a core concern for practitioners of machin e learning\, data science\, and data engineering\, and in recent years spe cialized packages have emerged to validate and monitor data and models. Ho wever\, as the open source community iterates on data frameworks – notab ly\, highly performant entrants such as Polars – data quality libraries need to catch up to support them. In this talk\, you will learn about Pand era and its journey from being a pandas-only validator to a generic tool f or testing arbitrary data containers so that it can provide a standardized way of creating data validation tools. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Pandera: Beyond Pandas Data Validation - Niels Bantilan URL:https://cfp.scipy.org/2023/talk/T3XSHX/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-RSE89M@cfp.scipy.org DTSTART;TZID=CST:20230712T131500 DTEND;TZID=CST:20230712T134500 DESCRIPTION:In this contribution we will present the first stable version v 1.0 of Gammapy\, an openly developed Python package for gamma-ray astronom y. Gammapy provides methods for the analysis of astronomical gamma-ray dat a\, such as measurement of spectra\, images and light curves. By relying o n standardized data formats and a joint likelihood framework\, it allows a stronomers to combine data from multiple instruments and constrain underly ing astrophysical emission processes across large parts of the electromagn etic spectrum. Finally we will share lessons learned during the journey to wards version v1.0 for an openly developed scientific Python package. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Gammapy: a Python Package for Gamma-Ray Astronomy Version v1.0 - Ax el Donath URL:https://cfp.scipy.org/2023/talk/RSE89M/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NPG3NS@cfp.scipy.org DTSTART;TZID=CST:20230712T135500 DTEND;TZID=CST:20230712T142500 DESCRIPTION:Our recent work implements a domain-specific language called Di sciplined Saddle Programming (DSP) in Python. It is available at https://g ithub.com/cvxgrp/dsp. DSP allows specifying convex-concave saddle\, or min imax problems\, a class of convex optimization problems commonly used in g ame theory\, machine learning\, and finance. One application for DSP is to naturally describe and solve robust optimization problems. We show numero us examples of these problems\, including robust regressions and economic applications. However\, this only represents a fraction of problems solvab le with DSP\, and we want to engage with the SciPy community to hear about further potential applications. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Disciplined Saddle Programming - Philipp Schiele\, Eric Sager Luxen berg URL:https://cfp.scipy.org/2023/talk/NPG3NS/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-KXWZJY@cfp.scipy.org DTSTART;TZID=CST:20230712T135500 DTEND;TZID=CST:20230712T142500 DESCRIPTION:Qiskit is an open-source SDK for quantum computers\, enabling d evelopers to work with these powerful machines using a familiar python int erface. First released in 2017\, Qiskit has become the most popular packag e for quantum computing (Unitary Fund\, 2022)\, with a thriving open-sourc e community. As Qiskit has grown and changed\, so has our approach to nurt uring our community. This talk will share important lessons we’ve learnt over the years\, including practical tips you can apply to your own proje cts. Whether you’re just starting in open-source or already manage an es tablished community\, this talk is for you! DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Beyond Bits & Qubits: Effective Open Source Community Management in Quantum Computing - Abby Mitchell URL:https://cfp.scipy.org/2023/talk/KXWZJY/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-JTXC9W@cfp.scipy.org DTSTART;TZID=CST:20230712T135500 DTEND;TZID=CST:20230712T142500 DESCRIPTION:In the era of exascale computing\, storage and analysis of larg e scale data have become more important and difficult. We present libyt\, an open source C++ library\, that allows researchers to analyze and visual ize data using yt or other Python packages in parallel during simulation r untime. We describe the methods for reading adaptive mesh refinement data structure\, handling data transition between Python and simulation with mi nimal memory overhead\, and conducting analysis with no additional time pe nalty using Python C API and NumPy C API. We demonstrate how it solves the problem in astrophysical simulations and increases disk usage efficiency. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:libyt: a Tool for Parallel In Situ Analysis with yt - Shin-Rong Tsa i URL:https://cfp.scipy.org/2023/talk/JTXC9W/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-RJPMGC@cfp.scipy.org DTSTART;TZID=CST:20230712T143500 DTEND;TZID=CST:20230712T150500 DESCRIPTION:Emukit is an open-source package for uncertainty quantification in Python. It provides various Bayesian methods\, such as optimization\, experimental design and quadrature\, in a flexible unified way that levera ges their commonalities. In the talk we will explain how and why Emukit wa s built\, what are its strengths and weaknesses\, how it is used today and in what scenarios one might find it useful. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Emukit: Python toolkit for uncertainty quantification - Andrei Pale yes URL:https://cfp.scipy.org/2023/talk/RJPMGC/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-QUNAY9@cfp.scipy.org DTSTART;TZID=CST:20230712T143500 DTEND;TZID=CST:20230712T150500 DESCRIPTION:Open source researchers are increasingly challenged while navig ating the data which open source communities inherently create when workin g in the open. While mining software repositories for insights into open s ource practices isn't new\, moving beyond code analysis into ecosystems-le vel research does not have a clear path. This talk will outline the curren t ethical\, legal\, and policy challenges community leaders\, as well as r esearchers in academia and industry face and the ambiguous areas decision makers should be aware of. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Thar Be Dragons - Ethical\, Legal\, and Policy Challenges when Meas uring Open Source - amanda casari URL:https://cfp.scipy.org/2023/talk/QUNAY9/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-DZBF7K@cfp.scipy.org DTSTART;TZID=CST:20230712T143500 DTEND;TZID=CST:20230712T150500 DESCRIPTION:Over the last decade\, the SunPy ecosystem\, a Python solar dat a analysis environment\, has evolved organically to serve the needs of sci entists analyzing solar physics data\, mostly on desktop and laptop comput ers. However\, modern solar observatories are producing data volumes in th e tens of petabytes\, necessitating the need for parallelized and out-of-c ore computation. HelioCloud is a cloud computing environment tailored for heliophysics research and colocated with many terabytes of solar physics d ata. In this talk\, we will show how the SunPy ecosystem\, combined with D ask on HelioCloud\, can be used to efficiently process high-resolution sol ar data. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Seeing the Sun through the Clouds: Accelerating the SunPy Data Anal ysis Ecosystem with Dask - Nabil Freij\, Will Barnes\, Jack Ireland\, Stua rt Mumford URL:https://cfp.scipy.org/2023/talk/DZBF7K/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-S8NSHT@cfp.scipy.org DTSTART;TZID=CST:20230712T152500 DTEND;TZID=CST:20230712T155500 DESCRIPTION:DuckDB is a novel analytical data management system. DuckDB sup ports complex queries\, has no external dependencies\, and is deeply integ rated into the Python ecosystem. Because DuckDB runs in the same process\, no serialization or socket communication has to occur\, making data trans fer virtually instantaneous. For example\, DuckDB can directly query Panda s data frames faster than Pandas itself. In our talk\, we will describe th e user values of DuckDB\, and how it can be used to improve their day-to-d ay lives through automatic parallelization\, efficient operators and out-o f-core operations. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:In-Process Analytical Data Management with DuckDB - Alex Monahan\, Hannes Mühleisen\, Mark Raasveldt URL:https://cfp.scipy.org/2023/talk/S8NSHT/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-8MMNUD@cfp.scipy.org DTSTART;TZID=CST:20230712T152500 DTEND;TZID=CST:20230712T155500 DESCRIPTION:The Open Force Field (OpenFF) initiative was formed to build a new generation of force fields for molecular dynamics (MD) simulations usi ng modern data-driven techniques. Openness is one of our fundamental found ing principles\, and everything we produce is released openly and accessib ly so that the community can validate\, modify\, or extend our work. Here we introduce some flagship packages in our ecosystem and the advances they have enabled in force field science and MD workflows. These include fitti ng custom functional forms\, exploring the addition of off-site charges\, and using neural networks to assign charges to protein-ligand systems. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Open Force Field: next-generation force fields with open data\, ope n software\, and open science - Jeff Wagner URL:https://cfp.scipy.org/2023/talk/8MMNUD/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-YESNZB@cfp.scipy.org DTSTART;TZID=CST:20230712T160500 DTEND;TZID=CST:20230712T163500 DESCRIPTION:GraphBLAS solves graph problems using sparse linear algebra. We are using it to build [`graphblas-algorithms`](https://github.com/python- graphblas/graphblas-algorithms)\, a fast backend to NetworkX. [`python-gra phblas`](https://github.com/python-graphblas/python-graphblas/) is faster and more capable than `scipy.sparse` for both graph algorithms and sparse operations. If you have sparse data or graph workloads that you want to sc ale and make faster\, then this is for you. Come learn what makes GraphBLA S special--and fast!--and how to use it effectively. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:GraphBLAS for Sparse Data and Graphs - Jim Kitchen\, Erik Welch URL:https://cfp.scipy.org/2023/talk/YESNZB/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-JH9JMV@cfp.scipy.org DTSTART;TZID=CST:20230712T160500 DTEND;TZID=CST:20230712T163500 DESCRIPTION:The NIST Interatomic Potentials Repository project has develope d Python APIs to support user interactions with the repository data hosted at https://potentials.nist.gov. The associated code is layered\, starting with generic methods for JSON/XML-based data and databases\, and building up to user-friendly interfaces specific to the repository. This design al lows for basic users to easily explore the data and expert users to perfor m more complicated operations or create custom APIs for other databases. T he repository APIs help users find and compare interatomic models\, set up simulations\, perform high throughput calculations\, and access the high throughput results. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Designing user-friendly APIs for the NIST Interatomic Potentials Re pository - Lucas Hale URL:https://cfp.scipy.org/2023/talk/JH9JMV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-TVRHYS@cfp.scipy.org DTSTART;TZID=CST:20230712T160500 DTEND;TZID=CST:20230712T163500 DESCRIPTION:TensorFlow Probability is a powerful library for statistical an alysis in Python. Using TensorFlow Probability’s implementation of Baye sian methods\, modelers can incorporate prior information and obtain param eter estimates and a quantified degree of belief in the results. Resamplin g methods like Markov Chain Monte Carlo can also be used to perform Bayesi an analysis. As an alternative\, we show how to use numerical optimization to estimate model parameters\, and then show how numerical differentiatio n can be used to get a quantified degree of belief. How to perform simulat ion in Python to corroborate our results is also demonstrated. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Bayesian Statistics with Python\, No Resampling Necessary - Charles D Lindsey URL:https://cfp.scipy.org/2023/talk/TVRHYS/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NUT798@cfp.scipy.org DTSTART;TZID=CST:20230712T170000 DTEND;TZID=CST:20230712T180000 DESCRIPTION:Lightning talks are 5-minute talks on any topic of interest for the SciPy community. We encourage spontaneous and prepared talks from eve ryone\, but we can’t guarantee spots. Sign ups are at the NumFOCUS booth during the conference. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Lightning Talks - URL:https://cfp.scipy.org/2023/talk/NUT798/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VUD8HM@cfp.scipy.org DTSTART;TZID=CST:20230712T180000 DTEND;TZID=CST:20230712T190000 DESCRIPTION:The Poster session will be in the Zlotnik Ballroom from 6:00-7: 00pm. \n\nThe Job Fair will be held concurrently in the Zlotnik foyer with participating sponsors. Sponsor companies will be available to discuss cu rrent job opportunities. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Poster Session and Job Fair - URL:https://cfp.scipy.org/2023/talk/VUD8HM/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-3NH3L8@cfp.scipy.org DTSTART;TZID=CST:20230712T190000 DTEND;TZID=CST:20230712T210000 DESCRIPTION:At Scholz Garten\, 1607 San Jacinto Blvd. Join your fellow comm unity members from 7:00-9:00. Walking distance from AT&T Center. Venue\, f ood\, and drinks sponsored by OSSci. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:SciPy Attendee Social Event hosted by Open Source Science (OSSci) - Scholz Garten\, 1607 San Jacinto Blvd URL:https://cfp.scipy.org/2023/talk/3NH3L8/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-DQQBWR@cfp.scipy.org DTSTART;TZID=CST:20230713T091500 DTEND;TZID=CST:20230713T100000 DESCRIPTION:Angela Pisco is the head of computational biology at insitro. S he is passionate about extracting meaningful information from biomedical d atasets and use that to improve disease understanding and drug development . She has studied Biomedical Engineering as BSc and MSc and have a PhD in Systems Biology. Her PhD work became the foundation of a new direction of thinking on why cancer develops resistance to chemotherapy\, which is the major reason for treatment failure. In her postdoctoral work\, she investi gated the mechanisms of cellular differentiation in the skin. She develope d a 3D computational model that recapitulated the observed changes in the mouse skin connective tissue and dermis during development. The combinatio n of the mathematical analysis with experimental data led to a new underst anding of how distinct fibroblast subpopulations become activated\, prolif erate\, and deposit matrix proteins during wound healing. Before moving to insitro\, she led the Data Science platform at CZ Biohub. There she made significant contributions for the whole organism cell atlas projects inclu ding the first whole mouse cell atlas\, the first aging cell atlas\, and T abula Sapiens\, one of the first Human Cell Atlas drafts (The Tabula Sapie ns Consortium\, Science 2022). She is also a founder and core member of Op en Problems in Single Cell (openproblems.bio)\, a community effort to impr ove multimodal data analysis by both generating gold standard datasets and benchmarking metrics and infrastructure. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Keynote - How Open Source Tools Power the Efforts of Biological Dat a Analysis and Drug Discovery - Angela Pisco URL:https://cfp.scipy.org/2023/talk/DQQBWR/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-MFQQRJ@cfp.scipy.org DTSTART;TZID=CST:20230713T104500 DTEND;TZID=CST:20230713T111500 DESCRIPTION:Google Earth Engine is a cloud-computing platform with a multi- petabyte catalog of satellite imagery and geospatial datasets. Built upon the Earth Engine Python API and open-source mapping libraries\, geemap ena bles Earth Engine users to interactively manipulate\, analyze\, and visual ize geospatial big data in a Jupyter environment. This presentation introd uces Earth Engine and highlights the key features of geemap for interactiv e mapping and geospatial analysis with Earth Engine. Attendees can utilize geemap to create satellite timelapse animations for any location on Earth within 60 seconds. Additional resources will be provided to the attendees to learn more about geemap. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Interactive Analysis of Satellite Imagery with Earth Engine and Gee map - Steve Greenberg\, Qiusheng Wu URL:https://cfp.scipy.org/2023/talk/MFQQRJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-TKQFWU@cfp.scipy.org DTSTART;TZID=CST:20230713T104500 DTEND;TZID=CST:20230713T111500 DESCRIPTION:Your users have entrusted their data to you. But what happens w hen a law enforcement government agency demands you share the data with th em? We will demystify the process of receiving and responding to law enfor cement’s demands for data. We demonstrate how designing around privacy c an limit what needs to be shared. To make subpoenas less scary\, we break them down as a technical process\, and share the protections we implemente d at Mozilla. If you want to understand the real-world impact of your app roaches to privacy\, this talk is for you. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Subpoenas Less Scary - Rebecca BurWei\, David Zeber URL:https://cfp.scipy.org/2023/talk/TKQFWU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VVVQRU@cfp.scipy.org DTSTART;TZID=CST:20230713T104500 DTEND;TZID=CST:20230713T111500 DESCRIPTION:This talk will discuss how Numba was used to accelerate MCViNE\ , a software environment for building and running digital twins of neutron experiments via Monte Carlo ray tracing. Numba is an open-source JIT comp iler for Python using LLVM to generate efficient machine code for CPUs and GPUs with NVIDIA CUDA. Python and Numba were used to create a GPU acceler ated version of MCViNE utilizing an extensible object-oriented design that has achieved a speedup of up to 1000x over the CPU. The performance gain with Numba enables more sophisticated data analysis and impacts neutron sc attering science and instrument design. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Using Numba for GPU acceleration of Neutron Beamline Digital Twins - Coleman Kendrick URL:https://cfp.scipy.org/2023/talk/VVVQRU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-BAD9ZQ@cfp.scipy.org DTSTART;TZID=CST:20230713T112500 DTEND;TZID=CST:20230713T115500 DESCRIPTION:Recharging ground aquifers is an urgent task for improving grou ndwater sustainability in California. Geophysical data can provide a capab ility to image the subsurface where the major data gap lies. However\, nei ther data nor analytic tools required to derive subsurface information is readily accessible. We present an interactive web application that utilize s a public database\, GIS capabilities and directly integrates Jupyter Not ebooks and Python packages from researchers to guide recharge site locatio n. Our demonstration showcases how this technology can contribute to impro ving groundwater recharge in California and how integrating the research k nowledge directly into a web application can increase the impact. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Accelerating the Use of Public Geophysical Data for Recharging Cali fornia’s Groundwater - SEOGI KANG URL:https://cfp.scipy.org/2023/talk/BAD9ZQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-AXSVZ3@cfp.scipy.org DTSTART;TZID=CST:20230713T112500 DTEND;TZID=CST:20230713T115500 DESCRIPTION:Jupyter-scatter is a scalable\, interactive\, and interlinked s catter plot widget for exploring datasets with up to several million data points. It focuses on data-driven visual encodings and offers two-way pan+ zoom and lasso interactions. Beyond a single instance\, jupyter-scatter ca n compose multiple scatter plots and synchronize their views and selection s. Moreover\, points can be connected by spline-interpolated lines. Thanks to the underlying WebGL rendering engine\, spatial and color changes are smoothly transitioned. Finally\, the API integrates seamlessly with Pandas DataFrames and offers functional methods that group properties by type to ease accessibility and readability. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Interactive Exploration of Large-Scale Datasets with Jupyter-Scatte r - Fritz Lekschas URL:https://cfp.scipy.org/2023/talk/AXSVZ3/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-H9FDBV@cfp.scipy.org DTSTART;TZID=CST:20230713T121500 DTEND;TZID=CST:20230713T130000 DESCRIPTION:Diversity\, equity and inclusion initiatives often start with m easurement - what do our communities look like today and how can we track progress against our goals? However\, data collected through APIs\, web sc raping\, surveys\, interviews\, inference etc. have the potential to expos e more details about an individual than they were expecting\, especially w hen aggregated across platforms and shared in public forums. This talk wil l discuss tactics\, opportunities and challenges when collecting sensitive data in and around open source communities\, while aligning with policies and regulations\, respecting the right to anonymity and ensuring the safe ty of all members of the community. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Diversity Luncheon Keynote: How can we protect vulnerable groups wh ile measuring representation in our communities? - Sophia Vargas URL:https://cfp.scipy.org/2023/talk/H9FDBV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-A9EGX9@cfp.scipy.org DTSTART;TZID=CST:20230713T131500 DTEND;TZID=CST:20230713T141000 DESCRIPTION:Imaging communities across different fields (microscopy\, remot e sensing\, medical imaging\, materials science) are currently all moving to develop cloud- and chunking friendly imaging formats based around Zarr. This includes OME-NGFF and GeoZarr. Although pretty much everyone has agr eed on Zarr as the container for the image data\, there is ongoing discuss ion about how best to store metadata about the images. In this BoF we'll d iscuss ways to encode *where* each pixel in the image is located in space (and time!) (and frequency!)\, and whether it's possible to harmonize this encoding across the different formats and standards. A relevant issue is https://github.com/ome/ngff/issues/174. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:[BoF Room 104] Where on Earth is my Pixel? - Juan Nunez-Iglesias\, Josh Moore URL:https://cfp.scipy.org/2023/talk/A9EGX9/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-3HXLZV@cfp.scipy.org DTSTART;TZID=CST:20230713T131500 DTEND;TZID=CST:20230713T141000 DESCRIPTION:Scientific Python Ecosystem Coordination (SPEC) documents (http s://scientific-python.org/specs/) provide operational guidelines for proje cts in the scientific Python ecosystem. SPECs are similar to project-speci fic guidelines (like PEPs\, NEPs\, SLEPs\, and SKIPs)\, but are opt-in\, h ave a broader scope\, and target all (or most) projects in the scientific Python ecosystem. Come hear more about what we are working on and planning . Better yet\, come share your ideas for improving the ecosystem! DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:[BoF Room 105] Scientific Python Ecosystem Coordination - Jarrod Mi llman\, Stéfan van der Walt\, Juanita Gomez URL:https://cfp.scipy.org/2023/talk/3HXLZV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-SZP3LA@cfp.scipy.org DTSTART;TZID=CST:20230713T131500 DTEND;TZID=CST:20230713T141000 DESCRIPTION:DataFrame libraries in general\, pandas and Dask specifically\, are moving towards a better integration with PyArrow. This has many benef its\, like improved performance and a reduced memory footprint. We want to connect with users to discuss how PyArrow can improve DataFrame libraries and what they expect out of PyArrow support. This can include things like improved performance\, more consistent behavior or better interoperabilit y with other libraries. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:[BoF Room 103] PyArrow in pandas and Dask - Matt Harrison\, James B ourbeau\, Patrick Hoefler URL:https://cfp.scipy.org/2023/talk/SZP3LA/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VGAUQN@cfp.scipy.org DTSTART;TZID=CST:20230713T142000 DTEND;TZID=CST:20230713T145000 DESCRIPTION:So you’ve written the perfect notebook\, but do you know who can read it? As a notebook author you have great stories\, code\, and visu alizations filling your work\, but how often do you consider accessibility ? Jupyter notebooks seem like they are for everyone\, but how a notebook g ets written can greatly impact how usable it is for people with disabiliti es. We’ve curated authoring-focused best practices for notebook content to help your notebooks be more inclusive and reach a wider audience. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Accessibility best practices for authoring Jupyter notebooks - Step hannie Jimenez Gacha\, Isabela Presedo-Floyd URL:https://cfp.scipy.org/2023/talk/VGAUQN/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-XSQKSA@cfp.scipy.org DTSTART;TZID=CST:20230713T142000 DTEND;TZID=CST:20230713T145000 DESCRIPTION:Aviation comprises 2-3% of global CO2 emissions. Transitioning to cleaner\, more sustainable aviation fuels can reduce its environmental impacts. To help accelerate sustainable aviation fuel development\, we tra ined machine learning models to predict fundamental properties of biofuel blends using Fourier transform infrared (FTIR) spectra. We leveraged TPOT and standard libraries like NumPy\, pandas\, and scikit-learn to develop t he models. This presentation will discuss how we overcame challenges with decomposing FTIR spectra data and using machine learning on small datasets (<100 samples). We will also discuss integration of the models into our o pen-source webtool to support biofuel research. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Using Python to accelerate sustainable aviation fuel research and d evelopment - Ana Comesana URL:https://cfp.scipy.org/2023/talk/XSQKSA/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-XMBALS@cfp.scipy.org DTSTART;TZID=CST:20230713T142000 DTEND;TZID=CST:20230713T145000 DESCRIPTION:UXarray aims to provide xarray-styled functionality for unstruc tured grid datasets. UXarray offers support for loading and representing u nstructured grids by utilizing existing Xarray functionality paired with n ew routines that are specifically written for operating on unstructured gr ids. In this talk\, we will present the current capabilities of the librar y: reading and writing of unstructured grids\, reading of datasets along w ith basic grid operations and the need to speed up computations\, integrat ion operations along with details on speedups obtained by using Numba and python indexing. We will also demonstrate the use of this library for visu alization of unstructured grids. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:UXarray\, a python library for unstructured climate and weather dat a - Rajeev Jain URL:https://cfp.scipy.org/2023/talk/XMBALS/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-7ZGCQM@cfp.scipy.org DTSTART;TZID=CST:20230713T150000 DTEND;TZID=CST:20230713T153000 DESCRIPTION:In research and data science\, effective communication requires weaving together narrative text and code to produce elegantly formatted o utput. By embedding executable Python code blocks inside markdown\, the op en-source publishing platform\, Quarto\, works with Jupyter and VS Code to enable you to create these fully reproducible documents and reports with the format and styling you need. In this talk I’ll share how to get star ted and a few of my favorite things in Quarto including creating a manuscr ipt\, presentation and website in HTML\, PDF and Word from a single source file\, and creating lessons\, reports\, and Confluence documents. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Scientific and technical publishing with Python and Quarto - Tracy Teal URL:https://cfp.scipy.org/2023/talk/7ZGCQM/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-MEGK33@cfp.scipy.org DTSTART;TZID=CST:20230713T150000 DTEND;TZID=CST:20230713T153000 DESCRIPTION:Behind every successful open source project is a strong contrib utor community. What makes these communities strong? What can you do in yo ur OSS project to nurture a thriving contributor community? In this presen tation\, we will share insights from the work of the Contributor Experienc e Lead team (NumPy\, SciPy\, Matplotlib\, and pandas) and discuss why desi gning and providing positive contributor experience is vital to sustainabi lity of each individual project and the SciPy ecosystem overall. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Contributor experience - Why it matters - Noa Tamir\, Melissa Weber Mendonça\, Inessa Pawson URL:https://cfp.scipy.org/2023/talk/MEGK33/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-QYHD3G@cfp.scipy.org DTSTART;TZID=CST:20230713T150000 DTEND;TZID=CST:20230713T153000 DESCRIPTION:*yt_xarray* is a new package in the scientific python ecosystem for linking *yt* and *xarray*. *yt*\, primarily used in computational ast rophysics\, has gradually broadened support for scientific domains\, inclu ding geoscience disciplines. Most geoscience data\, however\, still requir es manual steps to load into *yt*. *yt_xarray*\, a new *xarray* extension\ , aims to streamline communication of data from *xarray* to *yt*\, providi ng a potentially useful tool to the many geoscience researchers already us ing *xarray* while allowing *yt* to leverage the distributed backends alre ady supported by *xarray*. In this presentation\, we will provide an overv iew of the usage and design of *yt_xarray*. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Introducing yt_xarray - Chris Havlin URL:https://cfp.scipy.org/2023/talk/QYHD3G/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LCWBBP@cfp.scipy.org DTSTART;TZID=CST:20230713T155000 DTEND;TZID=CST:20230713T162000 DESCRIPTION:The open-source project\, Xarray\, combines labeled data struct ures inspired by Pandas with NumPy-like multi-dimensional arrays to provid e an intuitive and scalable interface for scientific analysis. Xarray has strong user bases in the physical sciences and geospatial community. Howev er\, new users commonly struggle to fit their dataset into the Xarray mode l and with conceptualizing and constructing an Xarray object that makes su bsequent analysis steps easy (“dataset wrangling”). We take inspiratio n from the “tidy data” concept for dataframes — “datasets structur ed to facilitate analysis” (Wickham\, 2014) — and attempt a definition of tidy data for labeled array objects provided by Xarray. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Tidy Geospatial Cubes - Deepak Cherian\, Emma Marshall\, Scott Hend erson URL:https://cfp.scipy.org/2023/talk/LCWBBP/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-RVLFPB@cfp.scipy.org DTSTART;TZID=CST:20230713T155000 DTEND;TZID=CST:20230713T162000 DESCRIPTION:Long-tailed distributions are common in natural and engineered systems\; as a result\, we encounter extreme values more often than we wou ld expect from a short-tailed distribution. If we are not prepared for the se "black swans"\, they can be disastrous.\n\nBut we have statistical tool s for identifying long-tailed distributions\, estimating their parameters\ , and making better predictions about rare events.\n\nIn this talk\, I pre sent evidence of long-tailed distributions in a variety of datasets -- inc luding earthquakes\, asteroids\, and stock market crashes -- discuss stati stical methods for dealing with them\, and show implementations using scie ntific Python libraries. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Taming Black Swans: Long-tailed distributions in the natural and en gineered world - Allen Downey URL:https://cfp.scipy.org/2023/talk/RVLFPB/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-T3NSL8@cfp.scipy.org DTSTART;TZID=CST:20230713T155000 DTEND;TZID=CST:20230713T162000 DESCRIPTION:A key feature of the Python data ecosystem is the reliance on s imple but efficient primitives that follow well-defined interfaces to make tools work seamlessly together (Cf. http://data-apis.org/). NumPy provide s an in-memory representation for tensors. Dask provides parallelisation o f tensor access. Xarray provides metadata linking tensor dimensions. Zarr provides a missing feature\, namely the scalable\, persistent storage for annotated hierarchies of tensors. Defined through a community process\, th e Zarr specification enables the storage of large out-of-memory datasets l ocally and in the cloud. Implementations exist in C++\, C\, Java\, Javascr ipt\, Julia\, and Python\, enabling. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Zarr: Community specification of large\, cloud-optimised\, N-dimens ional\, typed array storage - Sanket Verma\, Josh Moore\, John Kirkham URL:https://cfp.scipy.org/2023/talk/T3NSL8/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-X8KZ3E@cfp.scipy.org DTSTART;TZID=CST:20230713T163000 DTEND;TZID=CST:20230713T170000 DESCRIPTION:napari is an n-dimensional image viewer for Python. If you’ve ever tried `plt.imshow(arr)` and made Matplotlib unhappy because `arr` ha s more than two dimensions\, then napari might be for you! napari will gla dly *display higher-dimensional arrays* by providing sliders to explore ad ditional dimensions. But napari can also: *overlay* derived data\, such as points\, segmentations\, polygons\, surfaces\, and more\; and *annotate* and *edit* these data\, using standard data structures like NumPy or Zarr arrays\, allowing you to *seamlessly weave* exploration\, computation\, an d annotation in image analysis. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:View\, annotate\, and analyze multi-dimensional images in Python wi th napari - Juan Nunez-Iglesias URL:https://cfp.scipy.org/2023/talk/X8KZ3E/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-UT3CUZ@cfp.scipy.org DTSTART;TZID=CST:20230713T163000 DTEND;TZID=CST:20230713T170000 DESCRIPTION:MetPy is an open-source Python package for meteorological and a tmospheric science applications\, leveraging significantly many other piec es of the scientific Python stack (e.g. numpy\, matplotlib\, scipy\, etc.) . With a focus on sustainability\, Metpy extensively leverages GitHub Acti on to try to automate as much of the software development process as possi ble. Sustainability also extends to the growth of the community of develop ers\, and we have been working to try to make that sustainable as well. He re we talk about our experiences\, share our successes and lessons learned with trying to build a sustainable project. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Building MetPy for the Long Term: Working to Keep an Open Source Pr oject Sustainable - Ryan May URL:https://cfp.scipy.org/2023/talk/UT3CUZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-EPPR7R@cfp.scipy.org DTSTART;TZID=CST:20230713T163000 DTEND;TZID=CST:20230713T170000 DESCRIPTION:This project introduces an extensible workflow used to evaluate climate model output using collections of Jupyter notebooks. The workflow supports parametrizing and batch-executing notebooks using Papermill\, in conjunction with developing notebooks interactively. Additional features include integration with Dask and caching intermediate data products gener ated by notebooks. The final product of the workflow can automatically be built into a Jupyter book for easy presentation and shareability. While it was initially developed for climate modeling\, the flexible and extensibl e nature of this framework makes it adaptable to any kind of data analysis work\, and the presentation will highlight this capability. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Climate Model Evaluation Workflow Built on Jupyter Notebooks - Elen a Romashkova URL:https://cfp.scipy.org/2023/talk/EPPR7R/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-HWW7S7@cfp.scipy.org DTSTART;TZID=CST:20230713T172000 DTEND;TZID=CST:20230713T182000 DESCRIPTION:Lightning talks are 5-minute talks on any topic of interest for the SciPy community. We encourage spontaneous and prepared talks from eve ryone\, but we can’t guarantee spots. Sign ups are at the NumFOCUS booth during the conference. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Lightning Talks - URL:https://cfp.scipy.org/2023/talk/HWW7S7/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-7DDDWU@cfp.scipy.org DTSTART;TZID=CST:20230713T183000 DTEND;TZID=CST:20230713T192500 DESCRIPTION:Scientific open source software has often advanced by volunteer efforts with little financial support. In recent years\, there has been a n increase in different groups funding open source software. How has this changed the open source community? Where would future funding have the lar gest impact in the open source landscape? What new thing would you build t hat would make the lives of developers\, researchers\, and users easier? H ow much support is needed and what are the best ways to provide that suppo rt? What large scale project doesn’t exist that *needs* to exist? How do you balance funded and volunteer efforts? Join this lively discussion to help identify key focus areas for open source funding and resources. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:[BoF Room 104] Funding Open Source Software - Demitri Muna\, Paige Martin URL:https://cfp.scipy.org/2023/talk/7DDDWU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-GEDFS7@cfp.scipy.org DTSTART;TZID=CST:20230713T183000 DTEND;TZID=CST:20230713T192500 DESCRIPTION:Each new SciPy brings even more tools for data visualization an d for building data-rich scientific applications and dashboards. This BoF brings together maintainers of Python tools for data visualization and bui lding apps to help make sense of this complex landscape for users and to h ighlight new developments\, trends\, and opportunities. Join us and stay a head of the curve! DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:[BoF Room 103] Python Visualization and App Tools - Sophia Yang\, K ushal Kolar\, Bane Sullivan\, Juan Nunez-Iglesias\, Elliott Sales de Andra de\, Jon Mease\, Nathan Jessurun\, Hadley Wickham\, James A. Bednar URL:https://cfp.scipy.org/2023/talk/GEDFS7/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-H3GNAT@cfp.scipy.org DTSTART;TZID=CST:20230713T183000 DTEND;TZID=CST:20230713T192500 DESCRIPTION:"Python packaging is a rapidly changing landscape\, plagued by many hurdles and challenges for users. The scientific Python community fac es some of the greatest difficulties of anyone here\, given the high relia nce on external binaries and compiled code\, the diversity of packaging ec osystems (PyPI\, Conda\, others)\, and the fact that many if not most user s are not professional software engineers\, like in other ecosystems. This is made all the more critical by the importance of reproducible research\ , and its sensitivity to even small dependency changes.\n\nWe'd like to bu ild on the recent momentum behind evolving the packaging landscape to bett er serve these needs and building bridges between key players in the core Python and scientific spaces\, with an intense\, engaging and open discuss ion. This will bring together the key community stakeholders and everyday package authors to sync up on best practices\, strengthen collaboration\, and help come to consensus that would take months or even years if not for in-person discussion\, as well as provide a jumping-off point for followu p conversations and future action items." DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:[BoF Room 105] Scientific Python Packaging Summit - C.A.M. Gerlach\ , Henry Schreiner III URL:https://cfp.scipy.org/2023/talk/H3GNAT/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-HPFVLT@cfp.scipy.org DTSTART;TZID=CST:20230714T091500 DTEND;TZID=CST:20230714T100000 DESCRIPTION:Dr. Rumman Chowdhury is a trailblazer in the field of applied a lgorithmic ethics\, creating cutting-edge socio-technical solutions for et hical\, explainable and transparent AI. She currently runs Parity Consulti ng\, Parity Responsible Innovation Fund\, and is a Responsible AI Fellow a t the Berkman Klein Center for Internet & Society at Harvard University. S he is also a Research Affiliate at the Minderoo Center for Democracy and T echnology at Cambridge University and a visiting researcher at the NYU Tan don School of Engineering. Previously\, she was the director of the ML Eth ics\, Transparency\, and Accountability team at Twitter identifying and mi tigating algorithmic harms on the platform. Before that she was CEO and fo under of Parity\, an enterprise algorithmic audit platform company. She fo rmerly served as Global Lead for Responsible AI at Accenture Applied Intel ligence. In her work as Accenture’s Responsible AI lead\, she led the de sign of the Fairness Tool\, a first-in-industry algorithmic tool to identi fy and mitigate bias in AI systems. Dr. Chowdhury has been featured in int ernational media\, including the Wall Street Journal\, Financial Times\, H arvard Business Review\, NPR\, MIT Sloan Magazine among others. She was na med one of BBC’s 100 Women\, recognized as one of the Bay Area’s top 4 0 under 40\, and honored to be inducted to the British Royal Society of th e Arts (RSA). DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Keynote - Responsible AI in Practice: How far we've come and where we're going - Dr. Rumman Chowdhury URL:https://cfp.scipy.org/2023/talk/HPFVLT/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-DQR9NU@cfp.scipy.org DTSTART;TZID=CST:20230714T104500 DTEND;TZID=CST:20230714T111500 DESCRIPTION:In this talk\, we will examine the new CUDA package layout for Conda (as included in conda-forge). Show how CUDA components have been bro ken out. Share how this affects development and package building. Walk thr ough changes in the conda-forge infrastructure made to incorporate these n ew packages. Examine recipes using the new packages and what was needed to update them. Additionally will provide guidance on how to use these new p ackages in recipes or in library development. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:New CUDA Toolkit packages for Conda - Rick Ratzel\, John Kirkham\, Thomson Comer URL:https://cfp.scipy.org/2023/talk/DQR9NU/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-AXPZZG@cfp.scipy.org DTSTART;TZID=CST:20230714T104500 DTEND;TZID=CST:20230714T111500 DESCRIPTION:In this talk we will share a Python library to obtain and analy ze policing data\, that was developed in conjunction with community activi sts\, data scientists\, social scientists and the Small Town Police Accoun tability (SToPA) Research Lab. We will showcase components of the SToPA l ibrary which use Python tools such as web drivers\, optical character reco gnition\, geospatial mapping\, machine learning and statistical sampling t o better understand the policing landscape. The goal of this work is to p resent an easily replicable framework for analyzing police and community i nteractions with accessible on-ramps for activists\, developers and resear chers. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Small Town Police Accountability: A Data Science Toolkit - Anna Hae nsch\, Ariana Mendible URL:https://cfp.scipy.org/2023/talk/AXPZZG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LMMPRP@cfp.scipy.org DTSTART;TZID=CST:20230714T112500 DTEND;TZID=CST:20230714T115500 DESCRIPTION:Geolocated data from smartphone apps are well-established resou rces for research. While most of that data come as points (e.g.\, geotagge d photos)\, there are a growing number of apps that collect linear data fr om users activities (e.g.\, running\, hiking\, off-road driving). Using es tablished ecological methods\, shallow-machine learning packages\, and mul tiprocessing we demonstrate a novel approach using mobile app data to esti mate back-country recreation popularity at multiple scales. The topics cov ered include normalizing and thinning coordinate data\, merging linear dat a from multiple sources\, and accounting for spatial bias while preserving the integrity of the original data. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Using Linear Tracking Data to Estimate Backcountry Recreation Popul arity - Vincent Sutherland\, David C. Folch URL:https://cfp.scipy.org/2023/talk/LMMPRP/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-T7DTX8@cfp.scipy.org DTSTART;TZID=CST:20230714T112500 DTEND;TZID=CST:20230714T115500 DESCRIPTION:The array API standard (https://data-apis.org/array-api/) is a common specification for Python array libraries\, such as NumPy\, PyTorch\ , CuPy\, Dask\, and JAX. \n\nThis standard will make it straightforward fo r array-consuming libraries\, like scikit-learn and SciPy\, to write code that uniformly supports all of these libraries. This will allow\, for inst ance\, running the same code on the CPU and GPU.\n\nThis talk will cover t he scope of the array API standard\, supporting tooling which includes a l ibrary-independent test suite and compatibility layer\, what work has been completed so far\, and the plans going forward. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Python Array API Standard: Toward Array Interoperability in the Sci entific Python Ecosystem - Ralf Gommers\, Stephannie Jimenez Gacha\, Leo F ang\, Saul Shanabrook\, Travis Oliphant\, Matthew Barber\, Aaron Meurer\, Thomas J. Fan\, John Kirkham\, Stephan Hoyer\, Tyler Reddy\, Andreas Muell er\, Athan Reines\, Mario\, Alexandre Passos URL:https://cfp.scipy.org/2023/talk/T7DTX8/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-ALEQSL@cfp.scipy.org DTSTART;TZID=CST:20230714T112500 DTEND;TZID=CST:20230714T115500 DESCRIPTION:Existing production machine learning systems often suffer from various problems that make them hard to use. For example\, data scientists and ML practitioners often spend most of their time stitching and managin g bespoke distributed systems to build end-to-end ML applications and push models to production.\n\nTo address this\, the Ray community has built Ra y AI Runtime (Ray AIR)\, an open-source toolkit for building large-scale e nd-to-end ML applications. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Modern compute stack for scaling large AI/ML workloads - Jules S. D amji\, Amog Kamsetty URL:https://cfp.scipy.org/2023/talk/ALEQSL/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-BDV3EE@cfp.scipy.org DTSTART;TZID=CST:20230714T131500 DTEND;TZID=CST:20230714T134500 DESCRIPTION:Allegro and FLARE are two very different packages for construct ing machine learning potentials that are fast\, accurate\, and suitable fo r extreme-scale molecular dynamics simulations. Allegro uses PyTorch for e fficient equivariant potentials with state-of-the-art accuracy\, while FLA RE is a sparse Gaussian process potential with an optimized C++ training b ackend leveraging Kokkos\, OpenMP\, and MPI for state-of-the-art performan ce\, and a user-friendly Python frontend. We will compare and contrast the two methods\, discuss lessons learned\, and show spectacular scientific a pplications. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:Allegro and FLARE: Fast and accurate machine learning potentials fo r extreme-scale simulations - Anders Johansson URL:https://cfp.scipy.org/2023/talk/BDV3EE/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-Q9KTXS@cfp.scipy.org DTSTART;TZID=CST:20230714T131500 DTEND;TZID=CST:20230714T134500 DESCRIPTION:Fast interactive visualization remains a considerable barrier i n analyses pipelines for large neuronal datasets. Here\, we present *fastp lotlib*\, a scientific plotting library featuring an expressive API for ve ry fast visualization of scientific data. *Fastplotlib* is built upon *pyg fx* which utilizes the GPU via WGPU\, allowing it to interface with modern graphics APIs such as *Vulkan* for fast rendering of objects. *Fastplotli b* is non-blocking\, allowing for interactivity with data after plot gener ation. Ultimately\, *fastplotlib* is a general purpose scientific plotting library that is useful for the fast and live visualization and analysis o f complex datasets. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Ultra fast visualization of large datasets using modern graphics AP Is in jupyter notebooks - Kushal Kolar\, Caitlin Lewis URL:https://cfp.scipy.org/2023/talk/Q9KTXS/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-A7EZZV@cfp.scipy.org DTSTART;TZID=CST:20230714T131500 DTEND;TZID=CST:20230714T134500 DESCRIPTION:Once a maintainer of a project decides to step down of a projec t\, the community needs to quickly adapt to this decision. This situation can be devastating for small projects and lead to their extinction. This t alk demonstrates\, based on the case of poliastro\, that the community is a key factor for a software to survive no matter who is leading it. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:What happens when the main maintainer of a project takes a step dow n? - Jorge Martínez URL:https://cfp.scipy.org/2023/talk/A7EZZV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-SXJFBQ@cfp.scipy.org DTSTART;TZID=CST:20230714T135500 DTEND;TZID=CST:20230714T142500 DESCRIPTION:Relational databases manage structured data and facilitate quer ies in collaborative repositories\, but using SQL from a scientific progra mming language is awkward. DataJoint is an open-source framework for manag ing scientific data supporting data definition\, diagramming\, and queries . DataJoint makes computation a native part of its data model\, bridging t he gap between databases and numerical analysis in automated workflows. We will showcase the elegance of the relational data model and its versatili ty through neuroscience research examples. We will also introduce the Data Joint SciViz library\, enabling scientists to build web apps for data visu alization and unlocking further potential for data-driven discovery. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:DataJoint: Bringing databases back into data science - Dimitri Yats enko URL:https://cfp.scipy.org/2023/talk/SXJFBQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-EYHNUV@cfp.scipy.org DTSTART;TZID=CST:20230714T135500 DTEND;TZID=CST:20230714T142500 DESCRIPTION:Metal-Organic Frameworks (MOFs) have vast potential for gas ads orption\, but their practical use hinges on their ability to dissipate the rmal energy generated during adsorption. Here\, we performed the first hig h-throughput screening of thermal conductivity in over 10\,000 MOFs using molecular dynamics simulations. Next\, we developed a graph neural network (GNN) based model to swiftly predict the diagonal components of the therm al conductivity tensor for accelerated materials discovery. Attendees will gain insights into how GNNs can be trained to predict material tensor pro perties\, benefiting both the materials science and machine learning commu nities. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:A Graph-Neural Network-Based model for rapid prediction of Thermal Transport in Metal-Organic Frameworks - Meiirbek Islamov URL:https://cfp.scipy.org/2023/talk/EYHNUV/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-EDZ9YB@cfp.scipy.org DTSTART;TZID=CST:20230714T135500 DTEND;TZID=CST:20230714T142500 DESCRIPTION:As scientists continue to embrace the Jupyter ecosystem for con structing computational narratives of their science through code\, data\, and rich text\, they may encounter technical and community barriers to mai ntaining and sharing their science with new and existing audiences. We dem onstrate the value of open-source science community building and getting t here through reliance on the open-source Jupyter ecosystem\, pre-packaged GitHub and BinderHub-based infrastructure\, and documentation for creating \, sharing\, testing\, and maintaining Pythia Cookbooks for their computat ional narratives. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Better (Open Source) Homes and Gardens with Project Pythia - Kevin Tyle\, Drew Camron URL:https://cfp.scipy.org/2023/talk/EDZ9YB/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-CQNJ9Z@cfp.scipy.org DTSTART;TZID=CST:20230714T143500 DTEND;TZID=CST:20230714T150500 DESCRIPTION:CZ CELxGENE Discover has released all of its human and mouse si ngle-cell data through a new API that allows for efficient and low-latency querying. The data is fully standardized\, hosted publicly and it is comp osed by a count matrix of 50 mi cells (observations) by >60 k genes (featu res) accompanied by cell and gene metadata. While these data are built fro m more than 700 datasets\, the API enables convenient cell- and gene-based filtering to obtain any slice of interest in a matter of seconds. All dat a can be quickly transformed to numpy\, pandas\, anndata or Seurat objects . DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:An API for efficient and low-latency access to the largest standard ized single-cell data repository by CZ CELLxGENE Discover. - Pablo Garcia- Nieto URL:https://cfp.scipy.org/2023/talk/CQNJ9Z/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-9JTLCF@cfp.scipy.org DTSTART;TZID=CST:20230714T143500 DTEND;TZID=CST:20230714T150500 DESCRIPTION:Communities are at the heart of open source software and are fu ndamental to our projects’ long-term success. The Python ecosystem has s everal mature projects\, that have spent years working on community initia tives. Newer projects can learn from their experiences and build stronger foundations to foster healthy communities.\n\nIn this talk\, we share a se t of practices for community-first projects\, including repository managem ent\, contributor pathways\, and governance principles. We’ll also share real examples from our own journey transitioning a company-backed OSS pro ject\, Nebari (https://nebari.dev/)\, to be more community-oriented. DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Community-first open source: An action plan! - Pavithra Eswaramoort hy\, Dharhas Pothina URL:https://cfp.scipy.org/2023/talk/9JTLCF/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-F9P3F3@cfp.scipy.org DTSTART;TZID=CST:20230714T143500 DTEND;TZID=CST:20230714T150500 DESCRIPTION:Force fields (FF)—the (parametrized) mapping from geometry to energy\, are a crucial component of molecular dynamics (MD) simulations\, whose associated Boltzmann-like target probability densities are sampled to estimate ensemble observables\, to harvest quantitative insights of the system. State-of-the-art force fields are either fast (molecular mechanic s\, MM-based) or accurate (quantum mechanics\, QM-based)\, but seldom both . Here\, leveraging graph-based machine learning and incorporating inducti ve biases crucial to chemical modeling\, we approach the balance between a ccuracy and speed from two angles---to make MM more accurate and to make m achine learning force fields faster. DTSTAMP:20260607T101922Z LOCATION:Grand Salon C SUMMARY:From Espaloma to SAKE: To brew\, distill\, and mix force fields wit h balanced briskness\, smoothness\, and intricacy. - Yuanqing Wang URL:https://cfp.scipy.org/2023/talk/F9P3F3/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-RTA7JG@cfp.scipy.org DTSTART;TZID=CST:20230714T153000 DTEND;TZID=CST:20230714T163000 DESCRIPTION:Lightning talks are 5-minute talks on any topic of interest for the SciPy community. We encourage spontaneous and prepared talks from eve ryone\, but we can’t guarantee spots. Sign ups are at the NumFOCUS booth during the conference. DTSTAMP:20260607T101922Z LOCATION:Zlotnik Ballroom SUMMARY:Lightning Talks - URL:https://cfp.scipy.org/2023/talk/RTA7JG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-VA7ENC@cfp.scipy.org DTSTART;TZID=CST:20230714T164000 DTEND;TZID=CST:20230714T173500 DESCRIPTION:Here the aim of the panel would be to throw light on role code assistants like Co-Pilot and tools like ChatGPT and how they revolutionize coding careers. Also\, provide insights that help young and budding progr ammers to prepare themselves for futuristic careers. Also\, try to find an swers to some hypothetical questions like can AI replace human programmers ? Can it add or suggest new features to the language itself? and problems people may face while developing enterprise-grade applications with AI. DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:[BoF Room 104] Future of Python Programming Language in the Artific ial Intelligence Era - Gajendra Deshpande URL:https://cfp.scipy.org/2023/talk/VA7ENC/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LTDRGY@cfp.scipy.org DTSTART;TZID=CST:20230714T164000 DTEND;TZID=CST:20230714T173500 DESCRIPTION:NumFOCUS will facilitate a discussion around open source projec ts managing a robust Code of Conduct as well as ongoing DEI support DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:[BoF Room 105] Open Source Project Code of Conduct Management and D EI Support - Noa Tamir\, Leah Silen\, Inessa Pawson URL:https://cfp.scipy.org/2023/talk/LTDRGY/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-JXWQPG@cfp.scipy.org DTSTART;TZID=CST:20230714T164000 DTEND;TZID=CST:20230714T173500 DESCRIPTION:Come join the BoF to do a practice run on contributing to a Git Hub project. We will walk through how to open a Pull Request for a bugfix\ , using the workflow most libraries participating at the weekend sprints u se (hosted by the sprint chairs) DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:[BoF Room 103] SciPy 2023 Sprint Prep BoF - Gil Forsyth\, Brigitta Sipőcz\, Madicken\, Matt Davis URL:https://cfp.scipy.org/2023/talk/JXWQPG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-XNVLQA@cfp.scipy.org DTSTART;TZID=CST:20230714T174500 DTEND;TZID=CST:20230714T184000 DESCRIPTION:Discuss the effects of recent and potential performance improve ments on the scientific Python packages. The goal is to discuss the cost/b enefit tradeoffs of adapting existing libraries to take advantage of poten tial improvements\, especially per-interpreter GIL and nogil\, but also ty pe specializations in the interpreter. DTSTAMP:20260607T101922Z LOCATION:Classroom 103 SUMMARY:[BoF Room 103] CPython performance - Michael Droettboom URL:https://cfp.scipy.org/2023/talk/XNVLQA/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-LGZUNG@cfp.scipy.org DTSTART;TZID=CST:20230714T174500 DTEND;TZID=CST:20230714T184000 DESCRIPTION:"Notebooks can be a powerful tool for the purposes for which th ey were designed—learning\, experimenting\, and sharing results. However \, users face many challenges when trying to achieve true reproducbility w ith notebooks alone\, including lack of dependency management\, pitfalls o f non-linear interactive execution\, and requiring bespoke tooling to open and execute. Furthermore\, there is a growing need to go beyond reprodubi lity of individual results—siloed into an opaque format possessing limit ed interoperability with the rest of the Python ecosystem—toward reusuab ility of research methods\, that can be shared\, built upon\, and deployed by users across the world. \n\nTherefore\, we invite the community to sha re their tools and workflows to go beyond reproducibility and towards true reusable science\, built on the shoulders of giants. Furthermore\, we hop e to explore how we can encourage users and the community to move beyond t he notebooks monoculture and toward a holistic\, open\, modular and intero perable approaches to conducting research and developing scientific code." DTSTAMP:20260607T101922Z LOCATION:Classroom 104 SUMMARY:[BoF Room 104] Beyond Notebooks: From reproducible to reusable rese arch - C.A.M. Gerlach\, Juanita Gomez URL:https://cfp.scipy.org/2023/talk/LGZUNG/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-9H9KFM@cfp.scipy.org DTSTART;TZID=CST:20230714T174500 DTEND;TZID=CST:20230714T184000 DESCRIPTION:Feedback on SciPy 2023 and ideas for SciPy 2024 DTSTAMP:20260607T101922Z LOCATION:Classroom 105 SUMMARY:[BoF Room 105] SciPy 2024 - SciPy 2023 Committee URL:https://cfp.scipy.org/2023/talk/9H9KFM/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-NMNJYF@cfp.scipy.org DTSTART;TZID=CST:20230715T090000 DTEND;TZID=CST:20230715T100000 DESCRIPTION:Everyone will meet in Room 204 and organize before breaking out for the remainder of the day. \n\nEvery year\, our community dedicates th e last 2 days of the SciPy conference to Sprints\, where we work together on open-source projects to push our ecosystem forward.\n\nSprints are an i nformal part of the conference\, where all are welcome to exchange ideas\, hack on exciting projects\, and create lasting connections. All programm ing levels are welcome at the sprints.\n\nJoin us for the preparatory Spri nt BoF as well on Friday at 4:40 in Room 103 - https://cfp.scipy.org/2023/ talk/JXWQPG/\n\nInterested in leading a sprint at SciPy 2022? Sign up at h ttps://www.scipy2023.scipy.org/sprints DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Open Source Sprints [Kickoff in Room 204] - Dr. Tania Allard\, Brig itta Sipőcz\, Alan Braz URL:https://cfp.scipy.org/2023/talk/NMNJYF/ END:VEVENT BEGIN:VEVENT UID:pretalx-2023-WTNHTR@cfp.scipy.org DTSTART;TZID=CST:20230716T090000 DTEND;TZID=CST:20230716T100000 DESCRIPTION:Everyone will meet in Room 204 and organize before breaking out for the remainder of the day. \n\nEvery year\, our community dedicates th e last 2 days of the SciPy conference to Sprints\, where we work together on open-source projects to push our ecosystem forward.\n\nSprints are an i nformal part of the conference\, where all are welcome to exchange ideas\, hack on exciting projects\, and create lasting connections. All programm ing levels are welcome at the sprints.\n\nJoin us for the preparatory Spri nt BoF as well on Friday at 4:40 in Room 103 - https://cfp.scipy.org/2023/ talk/JXWQPG/\n\nInterested in leading a sprint at SciPy 2022? Sign up at h ttps://www.scipy2023.scipy.org/sprints DTSTAMP:20260607T101922Z LOCATION:Amphitheater 204 SUMMARY:Open Source Sprints [Kickoff in Room 204] - Dr. Tania Allard\, Brig itta Sipőcz\, Alan Braz URL:https://cfp.scipy.org/2023/talk/WTNHTR/ END:VEVENT END:VCALENDAR