About

Frederik Banis, PhD

Researcher in Uncertainty-Aware Modeling, Optimization, and Closed-Loop Decision Systems

I develop mechanistically grounded machine learning systems that actively decide how to acquire information under uncertainty, integrating probabilistic inference, adaptive sensing, and constrained control into closed-loop experimental platforms for physical and biomedical applications.

Biography

Frederik Banis is a researcher working on uncertainty-aware modeling, optimization, and closed-loop decision systems for physical and biomedical applications. His central question is how measurement and experimental systems should decide what data to collect next in order to maximize information about an underlying process, while respecting physical, operational, and safety constraints.

He received his PhD in System Optimization and Automation from the Technical University of Denmark (DTU), where he developed model predictive control and probabilistic system identification methods for microgrids and building energy systems. He then joined ETH Zurich as a postdoctoral researcher and later served as Scientific Officer and Data Steward for the Swiss National Centre of Competence in Research (NCCR) Automation, while teaching undergraduate courses in control and drone automation and managing the IfA/PSL laboratories.

His methods combine structured and mechanistic machine learning, probabilistic inference (Bayesian inference, variational methods), and sequential decision-making (optimal experimental design, model predictive control). Application domains have progressed from building energy and microgrid systems—where he validated algorithms in laboratory hardware-in-the-loop platforms—to current work on adaptive biomedical sensing and human-supervised closed-loop experimentation in antibacterial drug discovery.

He is a Guest Researcher at Hangzhou Institute of Technology (Xidian University) and a Researcher at the Gongshu Gongda Future Technology Research Institute (Hangzhou, Zhejiang University of Technology). He maintains active collaborations in Switzerland, Denmark, and China. His work is published in IET Renewable Power Generation, Energies, IFAC, and IEEE venues, with software released on Zenodo and GitHub.

Career Timeline

Education

Scholarly Profiles

• ORCID — persistent scholarly identifier
• LinkedIn
• Google Scholar — search “Frederik Banis”

Also known as Shuping. English is the canonical academic version of this page.