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Monday, 11 April

09:00 Registration & Coffee
10:20 Opening Session
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Learning from Experience Database Ethics Geo R to Python / Causality Tutorial pytest Part 1 @ 11:20 Tutorial @ 11:20
11:25 What I learned from monitoring more than 30 Machine Learning Use Cases
Lina Weichbrodt

11:25-12:10
Fundamentals of relational databases
Katharina Rasch

11:25-12:10
The Myth of Neutrality: How AI is widening social divides
Stefanie Stoppel

11:25-12:10
Processing Open Street Map Data with Python and PostgreSQL
Travis Hathaway

11:25-12:10
Rewriting your R analysis code in Python
Helena Schmidt

11:25-12:10
pytest - simple, rapid and fun testing with Python (3 hours)
Florian Bruhin

11:20-12:50
sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz Kiraly

11:20-12:50
12:20 Building a Sign-to-Speech prototype with TensorFlow, Pytorch and DeepStack: How it happened & What I learned
Steven Kolawole

12:20-12:50
Building an ORM from scratch
Jonathan Oberländer, Patrick Schemitz

12:20-13:05
You shall not share!
Gönül Aycı

12:20-13:05
Creating 3D Maps using Python
Martin Christen

12:20-12:50
Introduction to Uplift Modeling
Dr. Juan Orduz

12:20-13:05
12:40 Lunch
13:40 Announcements
13:45 Beyond the basics: Contributor experience, diversity and culture in open source projects
Melissa Weber Mendonça

13:45-14:30
14:30 Coffee Break
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Career Insights from Big Data Python Dask/DataFrames Inclusion / DS Management Tutorial pytest Part 2 @ 14:45 Tutorial @ 14:45
15:00 Career Panel
Katharine Jarmul, Matteo Guzzo, Sieer Angar, Marielle Dado, Emily Gorcenski

15:00-16:00
A Smooth Ride: Online Car Buying and Selling at mobile.de
Ricardo Kawase, Marlene Hense

15:00-15:30
Python 3.10: Welcome to pattern matching!
Laysa Uchoa

15:00-15:30
Introducing the Dask Active Memory Manager
Guido Imperiale

15:00-15:30
Impact of Cultivating a Diverse and Inclusive Workplace
Riya Bansal

15:00-15:30
pytest - simple, rapid and fun testing with Python (3 hours)
Florian Bruhin

14:45-16:15
Inpsect and try to interpret your scikit-learn machine-learning models
Guillaume Lemaitre

14:45-16:15
15:40 Seeing the needle AND the haystack: single-datapoint selection for billion-point datasets
Jean-Luc Stevens

15:40-16:10
Trojan Source Malware - Can we trust open-source anymore?
Cheuk Ting Ho

15:40-16:10
Data Apis: Standardization of N-dimensional arrays and dataframes
Stephannie Jimenez Gacha

15:40-16:10
The secret sauce of data science management
Shir Meir Lador

15:40-16:40
16:20 Lightning Talks
18:30 bcc closed

Tuesday, 12 April

08:30 Doors Open
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Toxic Code & People Speed / AI in Enterprise Code Quality Streamlit in Practice NLP / Sci-Kit Tutorial @ 9:00
09:00 Biases in Language Models
sonam

09:00-09:30
Speeding up Python with Zig
Adam Serafini

09:00-09:30
Secure ML: Automated Security Best Practices in Machine Learning
Alejandro Saucedo

09:00-09:30
How a simple streamlit dashboard will help to put your machine learning model in production
Daniël Willemsen, Welmoet Verbaan

09:00-09:30
deepdoctection - An open source package for document intelligence
Janis Meyer

09:00-09:30
Faster Workflow with Testdriven Development
Torsten Zielke

9:00-10:30
Data Science at Scale with Dask
Richard Pelgrim

09:00-10:30
09:40 How to deal with toxic people
Gina Häußge

09:40-10:05
Unsupervised shallow learning for fraud detection on marketplaces
Andreu Mora

09:40-10:25
conda-forge: supporting the growth of the volunteer-driven, community-based packaging project
Wolf Vollprecht, Jannis Leidel, Jaime Rodríguez-Guerra

09:40-10:25
Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans

09:40-10:25
`python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn
Sylvain Marié

09:40-09:55
10:20 Coffee Break
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  AI in Enterprise Database & FastAPI Code Quality Prediction Visualisation Tutorial @10:45
10:50 5 Things You Want to Know About AI Adoption in the Enterprise
Alexander CS Hendorf

10:50-11:20
Using a database in a data science project - Lessons learned in production
Jacopo Farina

10:50-11:20
Unclear Code Hurts
Dario Cannone

10:50-11:20
Predictive Maintenance and Anomaly Detection for Wind Energy
Tobias Hoinka

10:50-11:20
Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos

10:50-11:20
ML Communication 101: How to talk about Machine Learning with anyone
Julia Ostheimer

10:45-12:15
We know what your app did last summer. Do you? Observing Python applications using Prometheus.
Jessica Greene (she/her), Vanessa Aguilar

10:50-12:20
11:30 Do we really need Data Scientists?
Dr. Setareh Sadjadi

11:30-12:00
5 Things we've learned building large APIs with FastAPI
Maarten Huijsmans

11:30-12:15
A data scientist's guide to code reviews
Alexandra Wörner

11:30-12:00
Making Machine Learning Applications Fast and Simple with ONNX
Jan-Benedikt Jagusch, Christian Bourjau

11:30-12:15
Sankey Plots with Python
Daniel Ringler

11:30-12:00
12:00 Lunch Reserved for PyLadies
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Model Drift & Labeling Web PyData Testing Jupyter DevOps Tutorial @ 13:00
13:10 Detecting drift: how to evaluate and explore data drift in machine learning systems
Emeli Dral

13:10-13:40
Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.
Dom Weldon

13:10-13:40
What are data unit tests and why we need them
Theodore Meynard

13:10-13:40
Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains Datalore Team
Alena Guzharina

13:10-13:40
Battle of Pipelines - who will win python orchestration in 2022?
Jannis Grönberg

13:10-13:40
Aspect-oriented Programming - Diving deep into Decorators
Mike Müller

13:00-14:30
Reproducible machine learning and science with python
Prabhant Singh

13:10-14:40
Reserved for PyLadies
13:50 Efficient data labelling with weak supervision
Maria Mestre

13:50-14:20
But this is an OAuth, is it not?
Sara Jakša

13:50-14:20
Machine Learning Testing Ecosystem of Python
Yunus Emrah Bulut

13:50-14:35
JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python
Jeremy Tuloup

13:50-14:25
The state of DevOps for Python projects
Tobias Heintz

13:50-14:20
14:20 Coffee Break
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  PyLadies Python Web / Django Prediction Computer Vision Tutorial @ 14:45
14:50 Python for Everyone - PyLadies' Insights Panel Discussion
Jessica Greene (she/her)

14:50-15:50
Stupid Things I've Done With Python
Mark Smith

14:50-15:20
Web based live visualisation of sensor data
Jannis Lübbe

14:50-15:20
Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
Florian Wilhelm

14:50-15:20
Challenge Accepted - How to Escape the Quicksand While Engineering a Computer Vision Application
Bettina Heinlein

14:50-15:15
Introduction to MLOps with MLflow
Tobias Sterbak

14:45-16:15
Easily build interactive plots and apps with hvPlot
Philipp Rudiger, Maxime Liquet

14:50-16:20
15:30 Slack bots 101: An introduction into slack bot-based workflow automation
Jordi Smit

15:30-16:00
Make the most of Django
Paolo Melchiorre

15:30-16:00
My forecast is better than yours! What does that even mean?
Illia Babounikau

15:30-16:00
Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian Kluge

15:30-16:00
15:50 Coffee Break
16:20 Announcements
16:30 5 Years, 10 Sprints, A scikit-learn Open Source Journey
Reshama Shaikh

16:30-17:15
17:15 Lightning Talks
18:30 bcc closed
19:00 Social Event

Wednesday, 13 April

08:30 Doors Open
09:00 Morning Announcements
09:10 Python 3.11 in the Web Browser - A Journey
Christian Heimes

09:10-09:55
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  "Easy" is hard Imaging / Network Inference Data Use Cases 🐍 <3 C IoT / Synthetic Data Tutorial @10:00
10:05 It is all about files and HTTP
Efe Öge

10:05-10:35
Easy and flexible imaging with the Core Imaging Library
Vaggelis Papoutsellis, Dr. Jakob Sauer Jørgensen

10:05-10:50
How to build a Python-based Research Cloud Platform from scratch
Andre Fröhlich

10:05-10:50
Fast native data structures: C/C++ from Python
Stefan Behnel

10:05-10:50
Introduction to OPC-UA and industrial IoT: Liberate machines from the proprietary clutches of Big Hardware with the power of opcua-asyncio
Joey Faulkner

10:05-10:50
(Serious) Time for Time Series
Marysia Winkels, James Hayward

10:00-11:30
Refactoring
Dr. Kristian Rother

10:05-11:35
11:00 "Easy Python": lies, damned lies, and metaclasses
Grigory Petrov, Maxim Danilov

11:00-11:30
Optimize your network inference time with OpenVINO
Adrian Boguszewski

11:00-11:30
Squirrel - Efficient Data Loading for Large-Scale Deep Learning
Dr. Thomas Wollmann

11:00-11:45
Demystifying Python's Internals: Diving into CPython by implementing a pipe operator
Sebastiaan Zeeff

11:00-11:30
Do I need to be Dr. Frankenstein to create real-ish synthetic data?
Gatha

11:00-11:30
11:30 Coffee Break
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Django Repositories Work Pandas Performance Inclusion / Python Magics Tutorial @ 11:45
12:00 Securing Django Applications
Gajendra Deshpande

12:00-12:30
Forget Mono vs. Multi-Repo - Building Centralized Git Workflows with Python
David Melamed

12:00-12:30
Come as you are: Transitioning from Science to Data Science
Dr. Hannah Bohle

12:00-12:30
On Blocks, Copies and Views: updating pandas' internals
Joris Van den Bossche

12:00-12:30
Can you Read This? (Or: how I Improved Text Readability on the Web for the Visually Impaired)
Asya Frumkin

12:00-12:30
PPML: Machine Learning on Data you cannot see
Valerio Maggio

11:45-13:15
An Introduction to Inter Process Communication and Synchronization using Python
Tanmoy Bandyopadhyay

12:00-13:30
12:40 Advanced Django ORM
Bas Steins

12:40-13:10
Quitting pip: How we use git submodules to manage internal dependencies that require fast iteration
Philipp Stephan

12:40-13:10
XAI meets Natural Language Processing
Larissa Haas

12:40-13:10
canceled The Magic of Python Objects
Coen de Groot

12:40-13:25
13:10 Lunch
time Kuppelsaal B09 B07-B08 B05-B06 A1 A03-A04 A05-A06 C04 Open Space (at bcc only)
  Refactoring Deep Learning Speed Up Docs & Config NLP in practice Tutorial @ 14:00
14:00 How to Find Your Way Through a Million Lines of Code
Jürgen Gmach

14:00-14:30
Financial Portfolio Management with Deep Reinforcement Learning
T-Berger

14:00-14:30
5 Steps to Speed Up Your Data-Analysis on a Single Core
Jonathan Striebel

14:00-14:30
cancelled Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?
Sebastian Cattes

14:00-14:30
Making MLOps uncool again
David

14:00-15:30
Practical graph neural networks in Python with TensorFlow and Spektral
Aleksander Molak

14:00-15:30
14:40 There Are Python 2 Relics in Your Code
Miroslav Šedivý

14:40-15:10
How to Trust Your Deep Learning Code
Tilman Krokotsch

14:40-15:10
Navigating the limitations of Python’s concurrency model in web services
Tarek Mehrez

14:40-15:25
jsonargparse - Say goodbye to configuration hassles
Marianne Stecklina

14:40-15:10
Transformer based clustering: Identifying product clusters for E-commerce
Sebastian Wanner, Christopher Lennan

14:40-15:25
15:10 Coffee Break
16:00 Closing Session
16:30 Goodbye
17:00 bcc closed