About

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.

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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—including integrated control demonstrated on a physical laboratory microgrid—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.

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Career Timeline

Period	Role	Institution
2026–present	Researcher	Gongshu Gongda Future Technology Research Institute (Zhejiang University of Technology), Hangzhou
March 2026–present	Guest Researcher	Hangzhou Institute of Technology Xidian, Xidian University
February 2026–present	Foundation Algorithm Library Developer (part-time)	HRK-Data
2020–2024	Postdoctoral Researcher in energy systems, automation, modeling, and control	ETH Zurich
2020–2024	Scientific Officer and Data Manager	NCCR Automation / ETH Zurich
2023–2024	Lecturer and Laboratory Lead — Quad-Rotors and Control Experiments Lab	ETH Zurich
2016–2020	PhD Researcher — efficient operation, modeling, optimization, and control of energy grids	DTU Compute, Technical University of Denmark
2016	Google Summer of Code Developer — Modular Energy Hub Modeling Framework	Empa
2014	Research Assistant — solar-thermal control and automated measurement systems	IAR and IFK, University of Stuttgart
2013	IAESTE Technical Exchange — engineering modeling and technical communication	Quito, Ecuador
2011–2013	Intern and Part-time Employee — project and technical support	blumartin, Munich

Education

Period	Qualification	Institution
2016–2020	PhD, System Optimization and Automation	DTU Compute, Technical University of Denmark
2013–2016	M.Sc., Sustainable Energy Systems	Hochschule für Technik Stuttgart
During M.Sc.	Research / thesis period	KTH Royal Institute of Technology
2009–2013	B.Eng., Renewable Energy Technology	Hochschule Weihenstephan-Triesdorf

Scholarly Profiles

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

Download

• Academic CV (PDF) — public version, updated March 2026

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