2026-07-15 –, University Hall
The Poster session will be in University Hall from 6:00-7:00pm. Meet with the poster authors to ask questions and learn about the posters that will be on display throughout the main conference.
- Hannes Hapke, David Cardozo, Triveni Gandhi - Opening the Black Box: Mechanistic Interpretability of Agent Tool Selection with Sparse Autoencoders (Data-Driven Discovery, Machine Learning and Artificial Intelligence)
- Gita Mohammadi - Using Scientific Python to Study Trigger Efficiencies in Searches for New Higgs Bosons at CERN (Spirit of SciPy)
- Rudraksh Karpe, Shivay Lamba, Suvrakamal Das, Satyam Soni - Python Carbon Loops: Closing the Feedback Loop Between Your Code and Its Climate Impact (Environmental, Earth, and Climate Sciences)
- Venkateswaran Shekar - RECAP: A Python framework for reproducible experiment capture and provenance (General)
- Emmanuel I. Obi - Teaching Python the Difference Between Radiation Dose and Damage (Biological and Medical Sciences)
- Alexander Luebbert - Data-Driven Optimization Framework for Competitive Performance in FIRST Robotics Competition (Scientific Computing in Education)
- Carlos García Jurado Suarez - Efficient Federated Inference on Entomology Images with PyTorch (Data-Driven Discovery, Machine Learning and Artificial Intelligence)
- Allison Ding - Minimizing Noise Clusters in Topic Modeling: A Scalarized Hyperparameter Optimization Approach with GPU Acceleration (Data-Driven Discovery, Machine Learning and Artificial Intelligence)
- Nick Hodgskin - Modernising Parcels for the era of Cloud-Native Geospatial data (Environmental, Earth, and Climate Sciences)
- Daniel McCloy, Eric Larson, Britta Westner - On-boarding and retaining maintainer talent for MNE-Python (Maintainers and Community)
- Noor Aftab - Building with Agents: The Open Source Story of the Scientific Repo-Agent (Data-Driven Discovery, Machine Learning and Artificial Intelligence)
- Deven Maheshwari - Climate is not a straight line: Scalable Python-based GAMM Workflows for Wildlife Conservation (Environmental, Earth, and Climate Sciences)
- Avik Basu - Right Predictions, Wrong Reasons: Explanation Drift Monitoring in Production (Data-Driven Discovery, Machine Learning and Artificial Intelligence)
- Erik Bolch, Mahsa Jami - Multi-Sensor Earth Science Made Easy: NASA VITALS (Environmental, Earth, and Climate Sciences)
- Rachael Sexton - Trimming the Hairball: Three Libraries for Better Network Recovery & Metrology (General)
- Abby Mitchell - Unravelling the mystery of free threading for scientific computing (General)
- Joe Cheng, on behalf of Isabella Velásquez - Merging without fear: Using validation to protect your Python workflows (General)
- Aishwarya Chander, Christian La France, Alexander - A Cloud-Native Single-Cell Data Analysis pipeline with Zarr, Icechunk, and RAPIDS-singlecell (Biological and Medical Sciences)
- Richard Iannone - Creating beautiful documentation sites for Python libraries with Great Docs (Maintainers and Community)
- Tarun Gandrathi - Building Trustworthy Scientific Python Workflows in Pharma (Biological and Medical Sciences)
- Jesse Loi - Bridging the Technical Gap: A Student-Led RAG Pipeline for Community-Driven Document Analysis (Scientific Computing in Education)
- Dylan Madisetti - Hash all the things: Caching for fast notebook restarts (General)
- Bhupendra Raut - Adapt: Prototyping a Real-Time, Reproducible Data Analysis Framework for Adaptive Radar Scanning (Environmental, Earth, and Climate Sciences)
- ** Adam Theisen** - From Towers to Lidars: ACT Unifies Atmospheric Data into Reproducible Python Workflows (Environmental, Earth, and Climate Sciences)
- Marc Berliner - 5x Fewer Stored Time Steps with Certified Accuracy: A Streaming Compression Algorithm for Adaptive Differential Equation Solvers (Environmental, Earth, and Climate Sciences)
- Lucas Sterzinger - Improving access of HDF5/NetCDF4 data in S3 cloud storage: a case study using NASA Land Surface Model data (Environmental, Earth, and Climate Sciences)
- Sruthi Pisipati, Haris Javed - Everything That Breaks When You Put an LLM Agent in Production (Data-Driven Discovery, Machine Learning and Artificial Intelligence)