We improve early diagnosis and prevention of diseases of the nervous system using artificial intelligence.
Jonas Beck
Mechanistic models allow to make precise and testable predictions about the underlying biophysical mechanisms that influence neural activity.
Prof. Dr. Philipp Berens
We apply machine learning algorithms to enable and accelerate discoveries in neuroscience and ophthalmology, which will ultimately allow us to diagnose diseases earlier and treat them better.
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.
Dr. Sebastian Damrich
Annotated data is a scarce resource. Unsupervised and self-supervised learning techniques enable researchers to discover structure in unlabelled data sets.
Kerol Djoumessi
My best motivation is when I don't understand something or when something goes wrong.
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.
Dr. Sam Gijsen
Clinical neuroscience will benefit from deep learning's versatility rather than from raw scale and compute.
Rita González Márquez
Visualization techniques are able to capture aspects of the data that would not be easily observable with other analysis methods and are therefore great tools for data exploration.
Dr. Ziwei Huang
I read it somewhere... that programming is like incantations: you speak out some arcane words, then magic happens.
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. Dmitry Kobak
Unsupervised data exploration plays an important role in many areas of science and becomes increasingly crucial as collected datasets grow in size and complexity.
Dr. Lisa Koch
Trustworthy AI will transform patient care in the coming years. In our research, we make sure that AI-enabled healthcare is safe and provides real benefit to patients and clinicians.
Chiu Yi Lam
Being a member of the rapidly expanding and dynamic research team brings me exciting new challenges and opportunities for professional growth.
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. Samuel Ofosu Mensah
My research aims to develop cutting-edge deep learning models for disease detection in retina images.
Sarah Müller
Generative models can help us understand and explore the main factors of variation in medical images.
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.
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.
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.
Marija Tochadse
Collaborating with therapists and engineers creates new ways to apply neuroscience to patients’ 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.
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 using computational models.
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
On September 29th, Kry Kadhim and Jonas Beck boarded a train headed to Frankfurt with two posters to present at the annual Bernstein Conference for Computational Neuroscience.
News
The Hertie AI is pleased to announce the appointment of Prof. Dr. Kerstin Ritter as new Professor and Director of the Department of Machine Learning in Clinical Neurosciences.
News
Indu Ilanchezian successfully defended her PhD thesis on visually explaining deep neural network classifiers for ophthalmology.
Congratulations Indu and good luck for your future!
News
Learn more about the ICML 2024 in Vienna from the point of view of Hertie Ai PhD student Jonas.
News
During the first week of July 400 academic researchers as well as corporate representatives gathered at the Sorbonne University in Paris to discuss recent advances in Medical Image Deep Learning. Patrick Köhler, who went there alongside his…
News
Lisa Schmors and Fabio Seel attended the AREADNE 2024 conference on the Greek island of Milos from June 25-28, fostering deep engagement through its unique program, interactive poster sessions, and exceptional presentations. They shared their…
Highlights
Sep 26, 2024
Hertie AI warmly welcomes Professor Dr. Kerstin Ritter as lead of the Department “Machine Learning for Clinical Neurosciences”
The Hertie AI is pleased to announce the appointment of Prof. Dr. Kerstin Ritter as new Professor and Director of the Department of Machine Learning in Clinical Neurosciences.
Jun 16, 2023
Browsing the landscape of biomedical research from a birds-eye perspective
Based on an AI-based analysis of more than 20,000,000 paper abstracts using large language models, researchers at Hertie AI have created an interactive online platform in collaboration with Nomic AI that allows a bird’s-eye view of the entire…
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.
Join the Hertie AI team as an early career research group leader
To strengthen and complement our research portfolio, the Hertie AI is seeking to recruit:
Two early career research group leaders (f/m/d)
For details about the positions visit our Early Career page.
early career research group leader
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.
Mit dem Hertie Institute for AI in Brain Health (Hertie AI) entsteht in Tübingen ein deutschlandweit einzigartiges Institut, das sich mit Hilfe von Methoden der Künstlichen Intelligenz mit der Früherkennung von Erkrankungen des Nervensystems und deren Prävention beschäftigt.
Our Partners
