We improve early diagnosis and prevention of diseases of the nervous system using artificial intelligence.
Azhar Akhmetova
Large-scale neural datasets are becoming increasingly available, from gene expression to morphology and spiking activity. Learning aligned representations of these data may help us study how these modalities relate and enable joint analyses.
Jonas Beck
Mechanistic models allow to make precise and testable predictions about the underlying biophysical mechanisms that influence neural activity.
Prof. Dr. Philipp Berens
The eye is one of the best-studied neural systems, yet most of its data remains untapped. We build the computational tools and datasets to unlock it — from single synapses to population-scale disease patterns.
Dr. Christina Bürger
Working at Hertie AI allows me to contribute towards achieving our vision, fostering innovative research, collaboration, and international recognition in the dynamic field of artificial intelligence.
Paula Buzduga
Guided by medical expertise, open-source image analysis creates accessible solutions with strong potential for real-world impact in healthcare.
Dr. Sebastian Damrich
Annotated data is a scarce resource. Unsupervised learning enables researchers to discover structure in unlabelled data sets.
Meghal Dani
Medicine is multimodal, messy, and high-stakes — and I ask one question of every AI system I build: will this work in a real clinical setting?
Kerol Djoumessi
My best motivation is when I don't understand something or when something goes wrong.
Dr. Simone Ebert
Combining modeling techniques at various different scales can aid us to investigate how neuronal processes - from cellular mechanisms to network interactions - act in concert to form visual perceptions.
Dr. Lydia Federmann
AI opens up new approaches for us to explore risk and protective factors that impact brain health.
Shreyash Garg
Mental health disorders like addiction intrigue me because they are so heterogeneous and complex, and there is so much we still do not know.
Laura Geiger
Learning is one of humanity’s greatest strengths, and education has the potential to foster curiosity, reflection, adaptability, and lifelong growth.
Dr. Sam Gijsen
Clinical neuroscience will benefit from deep learning's versatility rather than from raw scale and compute.
Rita González Márquez
More text data is available today than ever before -- from scientific literature to social media to clinical records. Learning representations of this data is key to making it searchable, interpretable, and ultimately useful for discovering patterns that would be impossible to find manually.
Erik Großkopf
Recent advancements in technology and machine learning hold tremendous potential within the medical domain to empower clinical experts and ultimately help patients.
Verena Jasmin Hallitschke
Vision-language models enable us to facilitate intuitive interactions with medical images.
Dr. Ziwei Huang
I read it somewhere... that programming is like incantations: you speak out some arcane words, then magic happens.
Larisa Ivanova
Responsible AI starts with informed people – curious, critical, willing to learn and adapt. Building the conditions for that, across disciplines and backgrounds, is the work that matters.
Kyra Kadhim
I enjoy working with people from all different backgrounds and walks of life to tackle fundamental problems in computational neuroscience such as how visual information is processed in biological systems.
Dr. Florian Kofler
Biomedical AI succeeds not by replacing experts, but by keeping them firmly in the loop.
Dr. Georgia Koppe
The goal is not just to forecast mental health trajectories, but to uncover their underlying mechanisms and ultimately shape them through personalized optimal control strategies.
Chiu Yi Lam
Being a member of the rapidly expanding and dynamic research team brings me exciting new challenges and opportunities for professional growth.
Dr. Jan Lause
Statistical models of the RNA sequencing process help me to understand why two brain cells show different gene activity. Is the difference just a coincidence, or a sign of new, exciting biology?
Marina Lex
Generalizability isn't just a goal—it's the key to making our research meaningful, reliable, and relevant beyond the confines of a single dataset.
Dr. Dr. Stefanie Liebe
How the human brain gives rise to cognition—and how these processes break down in neurological and psychiatric disorders—is one of science's greatest challenges. We harness AI to translate brain activity into biological insight and clinical understanding.
Dr. Samuel Ofosu Mensah
My research aims to develop cutting-edge deep learning models for disease detection in retina images.
Mohammed Raees MK
Neuro-AI offers one of the most promising paths to understanding how the brain works, and might accidentally improve ML algorithms along the way.
Sarah Müller
Generative models can help us understand and explore the main factors of variation in medical images.
Nicolas Münster
Modern machine learning methods offer us the potential to uncover the underlying dynamics that describe the world that surrounds us.
Dr. Trang-Anh Estelle Nghiem
As a physicist, I am fascinated with emergence: how something microscopic like an ion channel on a neuron can shape something human-scale, like how we view the world and interact in others
Ifeoma Veronica Nwabufo
Being able to contribute to the well-being of people using Data Science is fulfilling.
Dr. Jonathan Oesterle
The retina is the perfect tissue to study the marvels of the central nervous system.
Miriam Peleman
I value the combination of meaningful work, international collaboration and the opportunity to keep learning every day.
Muthu Jeyanthi Prakash
I use computational approaches to analyse single-neuron recordings and local field potentials in the human medial temporal lobe, studying how neural firing, oscillations, and interactions between regions support cognitive processes.
Roshan Rane
To truly understand mental health, we must understand how different brains and their psyche interact with the demands of our environment. I believe, with the current advancements in ML and data science, we can finally address these questions.
Prof. Dr. Kerstin Ritter
Using AI to assess brain health bridges the gap between rapid technology advances and the need for precise care in neurology and psychiatry.
Camila Roa
Interpretability of ML models is a critical aspect when designing a system to support clinical decision-making.
Dr. Lisa Schmors
I am interested in the fundamental principles of neural coding in larger networks and neural populations.
Fabio Seel
The interdisciplinary work at the intersection of AI and Neuroscience makes it an exciting field of research: Understanding how the eye processes information inspires new approaches in computer vision and vice versa.
Moritz Seiler
If statistics is the grammar of science, we should use it to try to understand the different factors of neurological disorders.
Nys Tjade Siegel
Validating explainable AI methods on brain data is key to unlocking deep learning’s full potential in clinical neuroimaging.
Dr. Sacha Sokoloski
Modern datasets are massive, and the models we apply to them are complex and difficult to decipher. Simple, principled theories are not things of the past, but more important than ever.
Dr. Didem Stark
In medical AI applications, fairness is fundamental to ensuring equitable benefits for everyone in society.
Harini Sudha J G
The first step to build truly intelligent systems is to understand vision and perception. How do vision systems, across species, stay robust in a chaotic world?
Aleksejs Timčenko
What came first: dynamical systems or computations? Probably neither, but it’s fun to study both.
Marija Tochadse
Collaborating with therapists and engineers creates new ways to apply neuroscience to patients’ needs.
Nadia Vohwinkel
Success in medical AI isn't just about high accuracy; it's about building models that respect clinical constraints and address real-world needs
Departments & Research Groups
Data Science
We generate knowledge from data in neuroscience and ophthalmology using machine learning algorithms, mechanistic modeling and data visualization. We integrate multimodal data to uncover the principles of how the nervous system works and how diseases can be better diagnosed and treated.
Independent Research Groups
We support postdocs who are leading their own research group for the first time in building their independent scientific careers. Through our Independent Research Groups, we provide them with the opportunity to pursue innovative approaches in data science and artificial intelligence to advance their own research projects. We foster knowledge exchange and provide access to cutting-edge algorithms and interdisciplinary resources, enabling them to address scientific and clinical questions while developing sustainable, independent research.
Machine Learning
We develop advanced machine learning and deep learning models to extract clinical information from neuroimaging, clinical, psychometric, smartphone, and omics data. We focus on challenges like explainability, robustness, confounding factors, causal inference, multimodal integration and adapting neural architectures (e.g., convolutional neural networks, transformers) to different data domains.
Research Groups
Representation Learning and Data Visualization
We develop machine learning tools for visualizing high-dimensional data such as single cell transcriptomics, clinical images, or scientific texts.
Machine Learning for Safe Medical Diagnostics
We work on robust and interpretable algorithms that enable machine learning algorithms to be applied in the clinical workflow in ophthalmology.
Neuronal Modeling
We gain insights into the healthy and diseased nervous system by developing and using computational models.
Research Groups
Human Electrophysiology and AI
We investigate how neural activity gives rise to human cognition and behavior, and how its disruption leads to neurological disease such as epilepsy.
Hertie AI Fellow Program
Hertie AI Fellows enrich the Hertie AI community with their expertise, perspectives, and innovative work.
Quantitative Biomedical Image Analysis
We use AI for quantitative biomedical image analysis, translating complex imaging data into biological and clinical understanding.
Neural Data Representation
We develop machine learning tools for analyzing, representing, and visualizing high-dimensional neuroscience data such as single-cell omics, clinical images, or time series.
Computational Neuropsychiatry
We develop AI-based brain simulations to uncover the mechanisms underlying altered behavior in psychiatric disorders and guide treatment targeting the root causes of each patient's symptoms.
Research Groups
Precision Brain Science
We analyze neuroimaging, clinical, psychometric, smartphone, and neurobiological data within a clinical framework to derive meaningful insights and improve patient outcomes in neurology and psychiatry.
Translational Barriers
We explore challenges in applying machine learning to neuroscience and psychiatry, focusing on explainability, data noise, confounders, and generalizability to enhance clinical applications.
Multimodal Modelling
We employ and adapt multimodal machine learning in medical neuroscience and psychiatry to advance insight and prediction.
News & Events
News
When technologies that track and interpret our emotions measures sadness or flags anxiety as abnormal, it changes more than the treatment. The new BMFTR-funded project ShiftAffect asks how these technologies reshape what we consider normal, healthy,…
News
Hertie AI celebrated ten years of Cyber Valley in Tübingen on 9 September and we were there to present our work on mapping retinal circuits. It is inspiring to see how much basic research can turned into start-ups and real-world change.
News
Hertie AI postdoc Lisa Schmors attended and taught at this year's Deep Learning Indaba in Lagos, Nigeria, and organised the satellite workshop on NeuroAI. The workshop included keynote talks, a hands-on tutorial, and a panel discussion.
News
Hertie AI director Kerstin Ritter hosts a podcast where she talks with guests about how AI works and where it is already being used in clinical practice. In the current season, "AI in Brain Health," looks at how AI is being used to understand and…
News
We are pleased to introduce the Health Innovation Lab for the 2026/2027 winter semester: a hybrid, project-based course designed to bridge the gap between clinical healthcare and AI.
News
Hertie AI Ph.D. student Nicolas Münster spent a week in Sicily at the 20th International Computer Vision Summer School, where lectures, poster sessions, and around 200 fellow students offered a broad view of where computer vision is heading.
Highlights
Aug 03, 2026
Hertie AI goes Podcast
Hertie AI director Kerstin Ritter hosts a podcast where she talks with guests about how AI works and where it is already being used in clinical practice. In the current season, "AI in Brain Health," looks at how AI is being used to understand and…
Jul 27, 2026
Health Innovation Lab: Bridging Medicine and Machine Learning
We are pleased to introduce the Health Innovation Lab for the 2026/2027 winter semester: a hybrid, project-based course designed to bridge the gap between clinical healthcare and AI.
May 05, 2026
Team Appreciation Tuesday
Congratulations to Dr. Kerol Djoumessi for successfully defending his PhD!
Join the Hertie AI team as a PhD or Postdoc!
Are you passionate about advancing brain health through artificial intelligence?
Explore exciting career opportunities at the Hertie AI.
Visit our Early Career page to find currently open positions and learn how you can contribute to our innovative research and diverse team.
Our Institute
With the Hertie Institute for AI in Brain Health (Hertie AI), an institute is being established in Tübingen that is unique in Germany and focuses on the early diagnosis of diseases of the nervous system and their prevention with the help of artificial intelligence methods.
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