Home | Research | Area B

B | Perception, Vision, and Natural Language Processing

forms a dynamic research domain at the intersection of computer science and cognitive sciences. This field explores the synergies between diverse sensory inputs, visual information processing, and language understanding.

B1 | Computer Vision

In the thriving era of Computer Vision, MCML researchers tackle key challenges by innovating beyond convolutional neural networks. They focus on novel models capturing both pixel relationships and high-level interactions, explore unsupervised learning techniques, and extend analysis beyond 2D to understand the 3D world observed through cameras.

Link to Zeynep Akata

Zeynep Akata

Prof. Dr.

Interpretable and Reliable Machine Learning (TUM)

Link to Daniel Cremers

Daniel Cremers

Prof. Dr.

Computer Vision & Artificial Intelligence (TUM)

Link to Angela Dai

Angela Dai

Prof. Dr.

Machine Learning of 3D Scene Geometry (TUM)

Link to Matthias Nießner

Matthias Nießner

Prof. Dr.

Visual Computing (TUM)

Link to Björn Ommer

Björn Ommer

Prof. Dr.

Machine Vision & Learning (LMU)

Link to Nils Thuerey

Nils Thuerey

Prof. Dr.

Physics-based Simulation (TUM)

Link to Rüdiger Westermann

Rüdiger Westermann

Prof. Dr.

Computer Graphics & Visualization (TUM)

B2 | Natural Language Processing

Natural Language Processing (NLP) focuses on understanding and generating natural language text, greatly influenced by recent advances in deep learning. Despite substantial progress, our MCML researchers address key challenges like enhancing deep language understanding through structural biases, developing common sense in models through experimental environments, and improving sample efficiency for more effective learning from large datasets.

Link to Alexander Fraser

Alexander Fraser

Prof. Dr.

Data Analytics & Statistics (TUM)

Link to Barbara Plank

Barbara Plank

Prof. Dr.

Artificial Intelligence and Computational Linguistics (LMU)

Link to Hinrich Schütze

Hinrich Schütze

Prof. Dr.

Statistical NLP and Deep Learning (LMU)

Link to Michael Hedderich

Michael Hedderich


JRG Leader

JRG Leader NLP (LMU)

B3 | Multimodal Perception

The ability for an intelligent, mobile actor to understand ego­motion as well as the surroundings are a fundamental pre­requisite for the choice of actions to take. However, vast challenges remain to achieve the necessary levels of safety, which are deeply rooted in research that MCML aims to carry out: Multi­sensor ego­motion estimation and environment mapping, scene representations suitable for interaction in an open­-ended environment, understanding and forecasting motion and events, and the the role of uncertainty in ML blocks as modular elements.

Link to Matthias Althoff

Matthias Althoff

Prof. Dr.

Cyber Physical Systems (TUM)

Link to Stefan Leutenegger

Stefan Leutenegger

Prof. Dr.

Machine Learning for Robotics (TUM)

Link to Angela P. Schöllig

Angela P. Schöllig

Prof. Dr.

Safety, Performance and Reliability of Learning Systems (TUM)

Link to Björn Schuller

Björn Schuller

Prof. Dr.

Health Informatics (TUM)

A | Foundations of Machine Learning

aims at strengthening the competence in Statistical Foundations and Explainability, Mathematical Foundations, and Computational Methods. These fields form the basis for all methodological advances.

C | Domain-specific Machine Learning

shows an immense potential, as both universities have several highly visible scientific domains with internationally renowned experts. This area facilitates translating ML concepts and technologies to many different domains.


Check out the publications by our members.