Conference Agenda
Agenda Overview
Date: Monday, 08/June/2026
8:00am
-
8:45am
Registration
Location:
Helmholtz Munich Campus
8:45am
-
9:00am
Welcome
Location:
Helmholtz Munich Campus
9:00am
-
11:00am
TT 1 (1/4) - Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Location:
Track 1
WS 2a (1/2) - Building Agentic ML Tools for Science: What Works and What Doesn’t
Location:
Track 2
WS 3a - Reproducible Benchmarking and Multi-Omics Integration Using the Multiverse Framework
Location:
Track 3
TT 4a (1/2) - From Prompts to AI Applications: A Hands-On Introduction to RAG and LLM Systems
Location:
Track 4
WS 5a - Translating Research Concepts into GDPR-Compliant Projects: A Six-Step Process and Three Cases for Hands-on Practice
Location:
Track 5
WS 6a - Powering Helmholtz AI: HAICORE Infrastructure & AI Platform at HZDR
Location:
Track 6
Start at 10:00 WS 7a (1/2) - AI in environmental research
Location:
Track 7
WS 8a (1/2) - Causal Inference and Causal AI for Complex Dynamic Systems in Medicine and Biology
Location:
Track 8
TT 9a - Deep Learning with Bayesian Principles
Location:
Track 9
11:00am
-
11:15am
Coffee break
Location:
Foyer
11:15am
-
1:15pm
TT 1 (2/4) - Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Location:
Track 1
WS 2a (2/2) - Building Agentic ML Tools for Science: What Works and What Doesn’t
Location:
Track 2
WS 3b - Responsible AI in Industrial Production: Practices and Methods for Predictive Models
Location:
Track 3
TT 4a (2/2) - From Prompts to AI Applications: A Hands-On Introduction to RAG and LLM Systems
Location:
Track 4
WS 5b - AI, Brussels and how to participate in decision-making and science policy
Location:
Track 5
WS 6b - Novel Helmholtz Imaging Tools for AI image processing along the pipeline
Location:
Track 6
WS 7a (2/2) - AI in environmental research
Location:
Track 7
WS 8a (2/2) - Causal Inference and Causal AI for Complex Dynamic Systems in Medicine and Biology
Location:
Track 8
WS 9b - Curiosity, Exploration, and Meta-Reinforcement Learning: Learning What to Learn
Location:
Track 9
1:15pm
-
2:15pm
Lunch
Location:
Mensa
2:15pm
-
4:15pm
TT 1 (3/4) - Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Location:
Track 1
WS 2b (1/2) - Open Challenges in Simulation-Based Inference
Location:
Track 2
WS 3c (1/2) - Medical Foundation Models: From Pretraining to Clinical Impact (MedFM @ HAICON26)
Location:
Track 3
TT 4b - A Practical Tour of PEFT & Co.
Location:
Track 4
WS 5c - The Science of Successful AI Communication
Location:
Track 5
TT 6c - Qubits all the way down: A Gentle Dive into Quantum Machine Learning Theory
Location:
Track 6
WS 7b (1/2) - Current status of the benchmarking field: lessons learned from the first half of the UNLOCK initiative
Location:
Track 7
TT 8b (1/2) - TwinWeaver: Generative Artificial Intelligence and Digital Twins for Longitudinal Modelling
Location:
Track 8
TT 9c - Uncertainty Quantification for Neural Networks: Make your model predictions trustworthy
Location:
Track 9
4:15pm
-
4:30pm
Coffee break
Location:
Foyer
4:30pm
-
6:30pm
TT 1 (4/4) - Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Location:
Track 1
WS 2b (2/2) - Open Challenges in Simulation-Based Inference
Location:
Track 2
WS 3c (2/2) - Medical Foundation Models: From Pretraining to Clinical Impact (MedFM @ HAICON26)
Location:
Track 3
TT 4c - Small and Locally Deployed VLMs under Evaluation: A Case Study of Image Captioning
Location:
Track 4
WS 5d - Introduction to Prototyping
Location:
Track 5
TT 6d - Coding the Quantum Machine Learning Future: A hands-on Tutorial
Location:
Track 6
WS 7b (2/2) - Current status of the benchmarking field: lessons learned from the first half of the UNLOCK initiative
Location:
Track 7
TT 8b (2/2) - TwinWeaver: Generative Artificial Intelligence and Digital Twins for Longitudinal Modelling (End at 17:30)
Location:
Track 8
7:30pm
-
10:00pm
Self-paid dinner in downtown Munich