Projects
Selected research and engineering projects spanning energy systems control and adaptive biomedical sensing. Each case study links methodology, evidence, and outcomes.
The projects below trace a methodological thread: from model predictive control for microgrid frequency regulation, through data-driven system identification with Bayesian uncertainty quantification, to adaptive sensing and closed-loop discovery for biomedical applications. Earlier energy-systems work is presented as transferable evidence; current biomedical projects are ongoing.
See the Research page for the program overview, the Methods page for the four methodological pillars, and the Publications page for the complete publication record.
Adaptive Radar-Based Physiological Sensing
Sequential radar measurement selection to maximise information about physiological signals under real-time and physics constraints.
Closed-Loop Discovery for Synergistic Antibacterial Combinations
Uncertainty-aware active experiment selection for antibacterial combination discovery, with expert review gating for safety.
Quad-Rotors and Control Experiments Lab
Lecturer and laboratory lead for the undergraduate quad-rotor control course at ETH Zurich, combining control theory with hands-on experimental validation.
Grey-Box Modeling of Building Thermal Dynamics
Maximum-likelihood identification of grey-box models for radiator-water and building heat dynamics from school measurement data.
SINDYc and MCMC for Prosumer Response Estimation
A synthetic study combining sparse identification of prosumer response dynamics with MCMC parameter inference.
Three-Level Hierarchical Microgrid Control
Three-level scheduling, frequency-control, and local-control architecture demonstrated on a grid-connected laboratory microgrid.
Target-Adjusted MPC for Microgrid Frequency Control
Model Predictive Control with disturbance-based equilibrium target adjustment for microgrid frequency regulation.
Modular Energy Hub Modeling Framework
Open-source optimization framework for modular energy hub modeling, developed during Google Summer of Code 2016 at Empa.