Q&A Panels
Algorithmic Accountability
Panel #1
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- Safe, Fair and Ethical AI - A Practical Framework, by Tariq Rashid
- Meditations on First Deployment: A Practical Guide to Responsible Data Science & Engineering, by Alejandro Saucedo
- Responsible ML in Production, by Catherine Nelson and Hannes Hapke
Panel #2
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- What Lies in Word Embeddings, by Vincent D. Warmerdam
- Building fairer models for finance, by Andrew Weeks
- Games, Algorithms, and Social Good, by Manojit Nandi
- Open Source Fairness, by Aileen Nielsen
Causal and Statistical Methods
Panel #1
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- Uncertainty Quantification for Online Learning via Hierarchical Incremental Gradient Descent, by Vihan Singh
- What cyber security can teach us about COVID-19 testing, by Hagit Grushka - Cohen
- ML-Based Time Series Regression: 10 concepts we learned from Demand Forecasting, by Felix Wick
Panel #2
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- Geometric and statistical methods in systems biology: the case of metabolic networks, by Haris Zafeiropoulos and Apostolos Chalkis
- When features go missing, Bayes’ comes to the rescue, by Narendra Mukherjee
- Uncertainty Quantification in Neural Networks with Keras, by Matias Valdenegro-Toro
- Bayesian Decision Science: A framework for making data informed decisions under uncertainty, by Ravin Kumar
- Modelling the extreme using quantile regression, by Massimiliano Ungheretti
Data Science in Production
Panel #1
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- Monitoring machine learning models in production, by Arnaud Van Looveren
- Better Code for Data Science, by Alexander CS Hendorf
- Thrifty Machine Learning, by Rebecca Bilbro
- Parallel processing in Python: The current landscape, by Aaron Richter
- Leveraging python and open-source for data-science on the buy-side., by James Munro
- Snap ML: Accelerated, Accurate, Efficient Machine Learning, by Haris Pozidis and Thomas Parnell
- Parallel processing in Python: The current landscape, by Aaron Richter
- Growing Machine Learning Platforms in the Enterprise, by Hussain Sultan and Ben Lindquist
Panel #2
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- Data processing pipelines for Small Big Data, by Esteban J. G. Gabancho and Anthony Franklin, PhD
- Transformation from Research Oriented Code into Machine Learning APIs with Python, by Tetsuya Jesse Hirata
- How to review a model, by Andy R. Terrel
- Speed Up Your Data Processing: Parallel and Asynchronous Programming in Data Science, by Chin Hwee Ong
Panel #3
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- Modern Time Series Analysis with STUMPY, by Sean Law
- Rethinking Software Testing for Data Science, by Eduardo Blancas
- Building one (multi-task) model to rule them all!, by Nicole Carlson and Michael Sugimura
- Hosting Dask: Challenges and Opportunities, by Matthew Rocklin
- Growing Machine Learning Platforms in the Enterprise, by Hussain Sultan and Ben Lindquist
Data Visualization and Interpretability
Panel #1
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- Quickly deploying explainable AI dashboards, by Oege Dijk
- Is a neural network better than Ash at detecting Team Rocket? If so, how?, by Juan De Dios Santos
- TimeSeries Forecasting with ML Algorithms and there comparisons, by Sonam Pankaj
- Visions: An Open-Source Library for Semantic Data, by Ian Eaves and Simon Brugman
Panel #2
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- ipywidgets for Education! Using Jupyter tools to make Math Visualization applets for the classroom, by Chiin-Rui Tan
- COVID-19 Visualizations, the Good, the Bad and the Malicious, by Rongpeng Li
- Opening the Black Box, by Ben Fowler and Chelsey Kate Meise
Julia For Python & Julia Users
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- Accelerating Differential Equations in R and Python using Julia’s SciML Ecosystem, by Chris Rackauckas
- An introduction to DataFrames.jl for pandas users, by Bogumił Kamiński
Lessons From Industry
Panel #1
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- Why I didn’t use deep learning for my image recognition problem, by Liucija Latanauskaite
- Feature drift monitoring as a service for machine learning models at scale, by Keira Zhou and Noriaki Tatsumi
- DevOps for science: using continuous integration for rigorous and reproducible analysis, by Elle O’Brien
- Skinny Pandas Riding on a Rocket, by Ian Ozsvald (PyDataLondon)
Panel #2
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- Basic Pitfalls in Waveform Analysis, by Yukio Okuda
- Entity matching at scale, by Lorraine D’almeida
- Building a Successful Data Science Team, by Justin J. Nguyen
- The Big Benefits of Small Data, by Christopher Lozinski
Open Science
Panel #1
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- Using Algorithm X to re-analyse the last UK general election, by Alex Glaser
- Taking Care of Parameters So You Don’t Have to with ParamTools, by Hank Doupe
- FlyBrainLab: An Interactive Open Computing Platform for Exploring the Drosophila Brain, by Mehmet Kerem Turkcan, Aurel A. Lazar and Yiyin Zhou
- Ensemble-X: Your personal strataGEM to build Ensembled Deep Learning Models for Medical Imaging, by Dipam Paul and Alankrita Tewari
- Autonomous Vehicles See More With Thermal Imaging: Multi-modal thin cross section Object Detection, by Laisha Wadhwa
Panel #2
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- Streamlit: The Fastest Way to build Data Apps, by Steven Kolawole
- Climate Change: analyzing remote sensing data with Python, by Luis Lopez
- Using EOLearn to build a machine learning pipeline to detect plastics in the ocean., by Stuart Lynn
- Cardinal: A metrics based Active Learning framework, by Alexandre Abraham
Miscellaneous
Panel #1
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- Visual data: abundant, relevant, labelled, cheap. Pick two?, by Irina Vidal Migallon
- pandas.(to/from)_sql is simple but not fast, by Uwe Korn
- pyodide: scientific Python compiled to WebAssembly, by Roman Yurchak
- Dirty Data science: machine-learning on non-curated data, by Gaël Varoquaux
- What’s new in pandas?, by Joris Van den Bossche and Tom Augspurger
Panel #2
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- Taking a Close Look in the Mirror: Data Literacy for Data Experts, by Laura J Ludwig
- Complex Network Analysis with NetworkX, by K. Jarrod Millman
- Separation of ~concerns~ scales in software, by Thomas A Caswell
- Computational Social Science with Python, and how Open Source transforms Academia and Research, by Bhargav Srinivasa Desikan
- Scalable cross-filtering dashboards with Panel, HoloViews and hvPlot, by Philipp Rudiger and James A. Bednar
- Building Large-Scale Multilingual Fuzzy Matching Framework, by Abdulrahman Althobaiti
Panel #3
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- Scalable cross-filtering dashboards with Panel, HoloViews and hvPlot, by Philipp Rudiger and James A. Bednar
- Supercharge Scientific Computing in Python with Numba, by Ankit Mahato
- Inventing Curriculum using Python and spaCy, by Gajendra Deshpande
- How to guarantee your machine learning model will fail on first contact with the real world., by Jesper Dramsch
- Modern Time Series Analysis with STUMPY, by Sean Law