Tim is Professor of Artificial & Collective Intelligence at Freie Universität Berlin where he teaches and studies different aspects of intelligent systems. In interdisciplinary projects, his lab investigates the individual and collective intelligence of model organisms, and develops new tools and ML algorithms. He is also active in the startup scene, as a mentor to several startups in Berlin and as a tech advisor to investors.

Working on agent-based fish models, using machine learning to bridge the gap between the swarm behavior and the individual behavior. Observer bias is avoided, by training the behavior directly from recordings of real fish. An additional topic is extending attribution methods to explain the models on different time- and swarm scales.

Working on machine learning methods that help us to better understand complex systems, in particular collective intelligence in honey bees in the BeesBook project.

PhD student in the BeesBook project, mainly busy developing machine learning solutions for behavioral recognition.

Khaled is working on developing the RoboFish robot and read-out system for the ElektroFish project. His research interests are in autonomous Systems, intelligent control, and long-term trajectory planning.

My goal is to develop interpretable ML methods that can be used for real-world scientific problems. To archive this goal, I work on a theoretical foundation of interpretable ML. An explanation with a correctness guarantee would be highly valuable to a scientist.

Marie is biologist by training and has previously worked with Randolf Menzel on the effects of nicotinoids on foraging behavior of honey bees. She is now working in the Hiveopolis project, evaluating hive-augmenting tech in living bee colonies.

Working on Reinforcement Learning in the RoboFish project to investigate fish interaction behavior.

Working on novel methods for intrinsic motivation in model-based Reinforcement Learning and applying them in the RoboFish project.

Modeling of weakly electric fish. Developing software, writing specification, maintaining continuous integration.

My PhD thesis focuses on explainable AI for graph neural networks with theoretical guarantees and the application of these methods to different domains like social networks and climate data. In parallel, I am working on the Petra-KIP (Persönliches transparentes KI-basiertes Portfolio für die Lehrerbildung) project which aims to assist students in their learning process with AI-supported self-reflection methods.

Pranav is working on the design and development of RoboBee for the EU H2020 project “Hiveopolis”. His research interests include, but are not limited to, swarm robotics, novel robots, and intelligent vehicles.

Sascha is software developer with a focus in machine learning and explainable AI. Currently, he is employed in project “PetraKIP” and responsible for the design, development and maintenance of the server infrastructure.

Diana is a student assistant, currently helping with the project Open Make regarding open source hardware. They are also a Master’s Student in Cognitive Neurosciences at the Freie Universität Berlin, with a background in Psychology and Mathematics. Their main interest lies in the study of neural networks, social psychology, and making science open and available to everyone.

Georg is a student assistant in the Robofish project.

He is, as of 2021, a Computer-Science Master student and is interested in ML and its application in body pose tracking.

Gianluca is a student assistant in the Elektrofish project.

He is currently enrolled as a Computer-Science Bachelor student and curious to deepen his knowledge about ML methods and their applications.

Luis is currently researching ways of improving the stability of neural network training for his Master Thesis at Tim’s lab. He has also been supporting Professor Landgraf with the Machine Learning lectures and is working on a collaboration between Professor Landgraf’s lab and Professor Eils’ lab at Berlin Institute of Health by Charité to use the power of Machine Learning for improving illness prediction from genetic data.

Youssef is a Data science Master student , He works on interpretability research and is interested in XAI and the crossing of symbolic and non-symbolic methods.