Machine Learning in Healthcare Berlin

Name: Tackling Imperfect Data Environments via Robust and Explainable AI
Start: 2026-04-30T16:00:00.000Z
End: 2026-04-30T20:00:00.000Z
Location: Berlin Institute of Health (BIH)

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Über uns

We are very happy to announce that in 2026 we will be back with 4 larger than usual meetups themed around Trustworthy AI in Healthcare AI, mostly focused on topics such as robustness and transparency.

Save the dates for our 2025 meetups:
April 30th
June 11th
September 3rd
November 11th

The QUEST Centre for Responsible Research at the Berlin Institute of Health (BIH) will sponsor the 2025 Meetups, which will take place in the same BIH conference room "Atrium" as before on the 5th floor of the Spreepalais at Anna-Louisa-Karsch-Straße 2, 10178 Berlin.
The QUEST Centre focuses on improving biomedical research, especially in the areas of robustness and transparency, with the ultimate goal of improving translation into the clinical domain. Therefore, the Meetup will continue to showcase relevant research and cool startups in the field of machine learning in healthcare, but we will also ensure the focus on translation. 
We are very happy to be back and we look forward to seeing you at our 2025 meetups, where there will still be time for technical discussions, Q&A, networking and the classic "pizza and drinks"!

Vorgestelltes Event

Tackling Imperfect Data Environments via Robust and Explainable AI

30. Apr. 2026 18:00 CEST

Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178, Berlin, DE

Join us for our next Machine Learning in Healthcare meetup in Berlin — proudly sponsored by the QUEST Centre!

Pizza is still back!
Yes, we fought for it in 2025, and we continue to do so in 2026. Come hungry.

We are very happy to announce our speaker Prof. Grégoire Montavon, who is a Professor at Charité Universitätsmedizin Berlin at the Institute of AI in medicine (IKIM) and a Research Group Lead in the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received a Master's degree in Communication Systems from the École Polytechnique Fédérale de Lausanne in 2009 and a Ph.D. in Machine Learning from the Technische Universität Berlin in 2013.
His research focuses on methods and applications of Explainable AI (XAI). A particular goal is to develop approaches that integrate well with state-of-the-art machine learning (ML) models used in medical diagnosis and research, so that these models can be verified and explored for insights.

His talk´s title is:
Tackling Imperfect Data Environments via Robust and Explainable AI
Abstract: In this talk, I will present novel techniques for learning in imperfect data environments, specifically addressing challenges such as mislabeling and spurious correlations. Central to these new approaches is the design of new loss functions and Explainable AI techniques that can isolate artifact-inducing components during or after training, allowing them to be systematically removed from the final model.

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📍 Location: “Atrium” conference room, 5th floor, Spreepalais, Anna-Louisa-Karsch-Straße 2, 10178 Berlin
🕕 Doors open at 6:00 pm — Talks start at 7:00 pm!

6:00–7:00 pm — Arrival, food, and networking
7:00 pm — Welcome by the hosts, followed by the talk + Q&A
8:00 pm — More networking (and possibly more pizza)

⚠️ Pro tip: Sometimes only one of the four elevators is working!

📢 IMPORTANT: If you RSVP but later realize you can’t make it, please release your spot so others can join.

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This meetup is made possible with the kind support of the QUEST Centre for Responsible Research at BIH, who are providing both the venue and the delicious refreshments.

We look forward to seeing you there!x