Welcome to the world of Cybernetics and Intelligent Systems

This is the personal website of the research group Cybernetic Intelligent Systems, a part of the Business Information Systems chair at the FHNW University of Applied Sciences and Arts Northwestern Switzerland.

Dear visitor, we are glad to introduce ourselves, our projects, interests and engagements to you. Our team is dedicated and motivated to drive innovation across a wide range of industries and application areas through the use of artificial intelligence and the development of intelligent systems.
Feel free to contact us at manuel.renold@fhnw.ch

Our team applies data-driven machine learning and artificial intelligence algorithms to operations research, focusing on industry needs. The interests and competences of our team are vast, exemplified by the list of our current and past projects. These projects utilise a broad array of methods, allowing us to exploit methods from the field of classical statistics, causality, reinforcement learning, computer vision and generative modelling. Our latest research projects AI4REALNET and brAIght focus heavily on the development of reinforcement learning solutions for process optimisation in aviation, railway and energy systems.

Picture: David Augsburger

Our fields of expertise range from classical statistical methods to stochastic and machine-based optimization (e.g., evolutionary multi-objective optimization), macroeconomic and microeconomic analyses, and the modelling and simulation of complex processes. In addition to the development of conventional simulation models in the fields of discrete-event and agent-based simulation, we also conduct research on new methods in machine learning and artificial intelligence (GANs, Deep Reinforcement Learning with Human Feedback, etc.). We apply these methods to complex operational problems across a variety of industries.

Our current activities include the simulation of processes such as passenger flow dynamics or baggage flow, capacity analyses and management, delay propagation, revenue management, hub and spoke load balancing and network analyses. We also explore multi-agent systems and human-AI co-learning approaches. The methods and solutions developed provide a basis for decision-making on complex problems as part of operations management for partners across different industries and their stakeholders.