Wolfgang Maass Unit Director | ELLIS Fellow

• Learning Processes in the Brain through Theory and Large-scale Models
• Computation and Learning in Biological Neuronal Systems

Robert Legenstein ELLIS Fellow | BilAI BoD Member

• Brain-inspired Computing & Energy-efficient ML
• Computation and Learning in Biological Neuronal Systems

Elisabeth Lex ELLIS Scholar

• Recommender Systems & Behavioural Analytics
• Domain-specialized ML & Trust

Robert Peharz ELLIS Scholar | GraML Spokesperson

• Probabilistic Models
• Neurosymbolic AI
• AI for Science

Ozan Özdenizci ELLIS Member

• Robustness, Security & Pricacy in ML
• Efficient Learning Algorithms & Architectures

Olga Saukh ELLIS Member

• Embedded Machine Learning & Sensing Systems
• Trustworthy & Adaptive AI

Thomas Pock ELLIS Member

• Non-smooth & Convex Optimization
• Computer Vision

Horst Bischof ELLIS Member | Rector TU Graz

• Computer Vision
• Online & Lifelong Learning

Franz Pernkopf ELLIS Member

• Resource-efficient Models for Intelligent Systems
• Dependable AI for Industry, Medicine and Speech

Bernhard Geiger ELLIS Member

• Information Theory for ML
• Physics-Informed ML

Randi Goertz Unit Coordinator

Mathematics & Efficiency of Deep Learning public ELLIS Reading Group

This reading group aims to help onboard young scientists interested in the topic of efficient ML and offers researchers at all levels a platform for an open dialog to foster collaboration, and stay up-to-date with rapid developments in the field. We welcome and discuss fresh research findings published as a pre-print or recently presented at research venues.

GraML Graz Research Center for Machine Learning

In order to promote the recognition of AI and ML as the most important tools for the future, TU Graz has established the Graz Center for Machine Learning research network. Interdisciplinary work is currently being carried out here to support the further development of machine learning, whether this support enables us to draw efficient and meaningful conclusions from Big Data, to identify the best combination of materials, or to make the systems themselves just a bit smarter.

Bilateral AI FWF Cluster of Excellence

The Austrian-wide Cluster of Excellence “Bilateral AI” aims at lifting artificial intelligence (AI) to the next level. It will combine two of the most important types of AI which have been developed separately so far, symbolic and sub-symbolic AI. This integration, resulting in a Broad AI, is intended to mirror something that humans do naturally, the simultaneous use of cognition and reasoning skills.