Technology and Research on Artificial Intelligence Laboratory at LMU Munich (TRAIL)

Recent advances in Artificial Intelligence (AI) have enabled exciting applications, which are now playing important roles in everyday life ranging from language translation and image processing to recommender systems and autonomous driving. Most applications are based on Machine Learning (ML), which achieved great successes due to increasingly available computational resources and data. The current trend of AI offers numerous opportunities to contribute within areas of research, theory, technology, and application. In TRAIL, we investigate different directions of AI and ML to provide novel methods and insights to pave the way for future applications and technologies.

Structure

    1. Overview
    2. Projects and Activities
    3. Teaching
    4. The TRAIL Team of Experts

1. Overview

In TRAIL, we regard the term intelligence with respect to the behaviour of an entity. For simplification we assume an entity to be intelligent if it is able to learn from past experience, to think about future events or actions, and to act according to its knowledge, thoughts, and interaction with other entities. Our research in TRAIL focuses on these three main aspects:

Learn

Learning is the process of extracting knowledge from data which represents past experience. The knowledge can be used to identify salient patterns or structures in data or to make predictions. Machine Learning is currently the most active field in AI and has achieved tremendous progress in various domains over the last decade.

 
Publications

Think

The goal of Thinking is to solve problems via explicit reasoning given a problem model, rules, or a simulator. Planning and Scheduling represent common classes of problem solvers and are often used for complex tasks like routing, task allocation, and decision making.

 
Publications

Act

Acting of AI systems involves the process of making intelligent decisions based on knowledge learned from prior experience or explicit reasoning. Acting is also influenced by coexisting AI systems e.g., in a multi-agent system. Social interaction with humans is important to integrate AI into our everyday life.

 
Publications

2. Projects and Activities

Organized Events

 

Projects

Current Projects

  • Federated Decentralized Learning (Siemens)

 
 
 
Past Projects

  • Constrained Graphs For Optimal Flow (Telekom)

 
 
 

  • Dependability of Machine Learning in Industrial Environments 2 (Siemens)

 
 

  • Dependability of Machine Learning in Industrial Environments 1 (Siemens)

 
 

  • ErLoWa: Recognition and Localization of of Leaks in Water Networks (SWM)

 
 
 

  • Engineering of Decentralized Systems (Siemens)

 

3. Teaching

We offer a diverse set of lectures and practical courses with each covering different areas of AI.

 

Students eligible to attend these courses have the opportunity to learn about the theoretic aspects of AI and to practice their skills by implementing and evaluating algorithms. Courses are occasionally held in cooperation with industrial partners to enable our students to gain further experience. Especially skilled students are offered to actively participate in TRAIL by carrying out individual research projects or writing their theses about exciting topics in the field of AI.

4. The TRAIL Team of Experts


Thomy Phan

(Head)

Prof. Dr. Claudia Linnhoff-Popien
 

Thomas Gabor
 

Steffen Illium
 

Fabian Ritz
 

Robert Müller
 

Kyrill Schmid
 

Andreas Sedlmeier
 

Philipp Altmann