In times of change, I want to empower people to keep up and participate,
to sustain a culture of human wonder, connection, and meaning in the age of AI.
I'm excited to be working on a mission that is close to my heart: helping people build the skills they need to thrive in our world. We don't just need smarter systems, but AI that truly benefits humans (in fact, every living being). We don't want a future where "only the algorithms are having fun". So let's build AI with sustainability in mind for a future that we can all enjoy together.
Hands-on machine learning explorations
A collection of interactive demos illustrating key machine learning concepts. This was built to make AI tangible and explorable for everyone.
full online course book/script
This course is for students who want to build practical data science skills. You will learn to run a full data science project from question to conclusion, guided by the CRISP-DM process throughout. By the end, you'll have gathered experience in discovering worthwhile challenges to address (innovation mindset), cleaning and exploring real datasets, training and evaluating models, and communicating results honestly and well with stakeholders. The technical layer is only a middle layer: The before and after are at least as important.
Chinese stories with reading aids
A reading app for Chinese learners: HSK-graded stories with tap-to-reveal sentence translations (English and German), an offline dictionary, pinyin with tone coloring, and hanzi charts with stroke-order animation.
Eine "Hack the Paradise!" Hackathon Challenge
Beim Hackathon Hack the Paradise! in Jena war ich als Mentor bei der WattsUp-Challenge dabei. Ein Blick auf die Datenseite: warum Netzbetreiber Prognosen brauchen, was Pegelmessstellen mit Kraftwerksleistung zu tun haben, und was mich an dieser Aufgabe aus Data-Science-Sicht fasziniert hat.
A Comprehensive Review
Giving people realistic expectations and AI literacy can improve discussions about the benefits and costs of AI technology, how and when it should be used, and what we want our future with AI to look like. Written for a broad audience.
Fine-Grained Sampling in Stochastic Segmentation Networks
In this work, we explore Stochastic Segmentation Networks for image segmentation. These networks predict segmentation uncertainty, which we structure into meaningful components. This adds a layer of explainability that allows humans to fine-tune segmentation by adjusting these components individually.