ÖAW AI Winter School 2026

16 Feb 2026, 08:30 → 20 Feb 2026, 18:00 Europe/Zurich

Seminar room 1 + 2 (Campus Akademie, Bäckerstraße 13, 1010 Wien)

Claudius Krause (MBI Vienna (ÖAW))

Welcome to the 5th AI winter school of the Austrian Academy of Sciences (ÖAW) and the Machine Learning Thematic Platform MLA²S, which is held on February 16 – 20, 2026 in Vienna. Here you can find some general information about the event.

The detailed program is currently under construction, but confirmed topics and speakers are already listed under the "Lecturers and Courses" tab.

This time, the winter school is aimed in particular at people who do not yet have in-depth knowledge of ML or AI, but serves to impart basic knowledge, especially for humanities scholars.

The school will be open primarily to people affiliated with or working closely with institutes of the Austrian Academy of Sciences. All levels of seniority are welcome: students (MSc, PhD), PostDocs, faculty and staff. External participants will also be permitted if spaces are available.

Registration will be possible between the end of December 2025 and January 25th, 2026.

The participation fee is 50€.  

The language of the school will be English.

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We offer a zoom connection to stream the lectures at 

Join Zoom Meeting
https://oeaw-ac-at.zoom.us/j/69816146446?pwd=5jMIIU0BLNQYZl9aekuR1pMqBrUASf.1

Meeting ID: 698 1614 6446
Passcode: 8zxrp8

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Local organizers:

• Kati Heinrich (IGF)
• Nicki Holighaus (ARI)
• Elisabeth Königshofer (ACDH)
• Claudius Krause (MBI)
• Jan Odstrčilík (IMAFO)
• Claus Trost (ESI)
• Brigitte DeMonte (HEPHY)

Monday 16 February

11:30 → 12:00 Registration

12:00 → 12:30 Welcome

Speakers: Dr Claudius Krause (MBI Vienna (ÖAW)), Claus Trost (Erich Schmid Institute of Materials Science of theAustrian Academy of Sciences), Jan Odstrčilík (Institut für Mittelalterforschung, ÖAW), Kati Heinrich (IGF | ÖAW), Nicki Holighaus (Acoustics Research Institute, Austrian Academy of Sciences)

12:30 → 14:00 What is AI? Overview lecture: Part I

1) This lecture introduces what “AI” means today and how it relates to machine learning, with an emphasis on large language models (LLMs) and AI agents. We’ll build intuition for core ML ideas—supervised vs. self-supervised learning, loss functions, optimization, and generalization.

regression, classification, generation, language models, etc.

Speaker: Michal Hradiš (Brno University of Technology)

2026-02-16 ÖAW AI Winter School 2026.pdf

google slides link

google slides link

14:00 → 14:30 Coffeebreak

14:30 → 15:30 AI and ML at the Austrian Academy of Sciences

Speaker: Dr Claudius Krause (MBI Vienna (ÖAW))

MLatOeAW.C.Krause.pdf

15:30 → 16:00 Coffeebreak

16:00 → 18:00 Overview lecture: Part II

2) This lecture explains how today’s language models work at a high level: key building blocks, typical training stages, and the resulting capabilities and limitations. We’ll finish with a brief Python demo using the OpenAI API and a local model via Ollama.

LLMs and LLM architecture, transformers, supervised and unsupervised training in more detail

Speaker: Michal Hradiš (Brno University of Technology)

Tuesday 17 February

09:00 → 10:30 Vibe Coding & „Promptotyping”: Part I

Speaker: Christopher Pollin (DH Craft Graz)

10:30 → 11:00 Coffeebreak

11:00 → 12:30 Vibe Coding & „Promptotyping”: Part II

Speaker: Christopher Pollin (DH Craft Graz)

12:30 → 14:00 Lunchbreak

14:00 → 15:30 Vibe Coding & „Promptotyping”: Part II

Speaker: Christopher Pollin (DH Craft Graz)

15:30 → 16:00 Coffeebreak

16:00 → 18:00 Vibe Coding & „Promptotyping”: Part III

Speaker: Christopher Pollin (DH Craft Graz)

Wednesday 18 February

09:30 → 11:00 Bias in Machine Learning

Speaker: Philip Winter (VRVis)

11:00 → 11:30 Coffeebreak

11:30 → 12:30 OpenEuroLLM and other developments on AI infrastructures

Speaker: Patrick Rarivoson

12:30 → 14:00 Lunchbreak

14:00 → 14:45 AI Strategy at the Austrian Academy of Sciences (ÖAW)

Speaker: Bernd Wachmann (ÖAW)

14:45 → 15:45 Governing Machines: From Moderation to Alignment

Speaker: Charlotte Spencer-Smith (CMC)

15:45 → 16:15 Coffeebreak

16:15 → 17:15 AI Policy at the Vienna University

Speaker: Laura Gandlgruber (Vienna University)

19:00 → 22:00 Dinner at Stöckl im Park

Thursday 19 February

09:00 → 11:00 From idea to final project: Part I - Data & Code Management

Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’ activities, expending some time actively thinking about them, and setting them up can save a lot of time and set you up for success in your research endeavour.

This workshop aims to introduce you to these topics. By the end of the workshop you will be prepared to independently self-teach these topics.
We will begin by exploring some academic research project examples and how RDM and RDM planning could be approached.
Moving on, I will introduce software version control via Git, with remotes on the developer platform, GitHub, and touching on working as a team on GitHub. This exercise will also explore automation via GitHub Actions and Docker.
We finish with data cleaning and wrangling concepts, and a practical demonstration in Python.
Participation by following along is encouraged but not expected.

Github management, workflows, exercises

Speaker: Stavrina Dimosthenous (University of Manchester)

11:00 → 11:30 Coffeebreak

11:30 → 12:30 From idea to final project: Part II - Data & Code Management

Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’ activities, expending some time actively thinking about them, and setting them up can save a lot of time and set you up for success in your research endeavour.

This workshop aims to introduce you to these topics. By the end of the workshop you will be prepared to independently self-teach these topics.
We will begin by exploring some academic research project examples and how RDM and RDM planning could be approached.
Moving on, I will introduce software version control via Git, with remotes on the developer platform, GitHub, and touching on working as a team on GitHub. This exercise will also explore automation via GitHub Actions and Docker.
We finish with data cleaning and wrangling concepts, and a practical demonstration in Python.
Participation by following along is encouraged but not expected.

Github management, workflows, exercises

Speaker: Stavrina Dimosthenous (University of Manchester)

12:30 → 14:00 Lunchbreak

14:00 → 15:30 From idea to final project: Part III - Data cleaning, reproducability

Delving into the world of data-driven research presents exciting possibilities. However, there are a few topics that need to be addressed before one can jump right in.
Research data management (RDM) planning, software version control, automation, and data cleaning might appear dry at first glance, and they might indeed be to many. Despite these topics and practices seeming like ‘checkbox’ activities, expending some time actively thinking about them, and setting them up can save a lot of time and set you up for success in your research endeavour.

This workshop aims to introduce you to these topics. By the end of the workshop you will be prepared to independently self-teach these topics.
We will begin by exploring some academic research project examples and how RDM and RDM planning could be approached.
Moving on, I will introduce software version control via Git, with remotes on the developer platform, GitHub, and touching on working as a team on GitHub. This exercise will also explore automation via GitHub Actions and Docker.
We finish with data cleaning and wrangling concepts, and a practical demonstration in Python.
Participation by following along is encouraged but not expected.

Speaker: Stavrina Dimosthenous (University of Manchester)

15:30 → 16:00 Coffeebreak

16:00 → 17:30 Introduction to CLIP

Speaker: Felix Schmitt (ÖAW | CLIP)

Friday 20 February

09:00 → 11:00 Explainability of Neural Networks in STEM fields

Due to their predictive accuracy, machine learning models are
increasingly employed in high-stakes decision making and in scientific
contexts. These predictive capabilities often come at the price of
interpretability: The models are too complex to be inherently
intelligible by their human users.
In an attempt to restore this interpretability, a broad range of methods
have been developed, often subsumed under the umbrella of the term
Explainable AI (XAI). In this session, we will give the attendees an
introduction to the field. We will start by mapping the different
approaches using the standard taxonomies, before we formally introduce
some of the most popular explanation methods.
Over the course of the session we will focus on the core challenges that
practitioners face: (1) Translating the outputs of XAI methods into
concise statements about model and data, and (2) choosing the
explanation technique that is most suitable for a given task. We will
tackle these questions with a particular focus on the task of learning
about feature target associations, thereby preparing attendees to employ
XAI methods in their own work.

Speaker: Gunnar König

11:00 → 11:30 Coffeebreak

11:30 → 12:30 Explainability of Neural Networks in STEM fields

Machine learning (ML) models are increasingly deployed in high-stakes
environments, e.g., in the health domain, where ethical and legal
considerations require models to be interpretable. Despite substantial
progress in interpretable ML (IML), several key challenges remain. These
include distinguishing between interpreting the model and using the
model to interpret the data-generating process, dealing with
heterogeneous data, and providing meaningful recourse recommendations. I
argue that we can address these challenges by adopting a causal
perspective on IML. In particular, I demonstrate how the causal
structures of the model and the data-generating process jointly affect
model interpretation techniques. I then provide an overview of our
recent work on leveraging causal techniques to improve model
interpretation and render recourse recommendations more meaningful.

Speaker: Moritz Grosse-Wentrup

12:30 → 13:30 Lunchbreak

13:30 → 15:00 Evaluation and Interpretability of NLP systems

Speaker: Pia Sommerauer (Vrije Universiteit Amsterdam)

15:00 → 16:00 Wrapup & Networking