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Research Associate
Research Associate at the University of Augsburg developing generative models for system identification, state estimation, and control.
GermanyResearch Associate at University of Augsburg
Developed and validated deep-generative-model-based methods for Bayesian parameter identification, state estimation, dynamics modeling, optimal control, and RL policy under uncertainty. Applied these methods in industrial applications such as MEMS testing, steering system state estimation, anomaly detection, robotics control, and autonomous driving path planning.
Generative ModelsBayesian Inference (PyMC)State EstimationModel Predictive ControlReinforcement LearningSystem IdentificationPythonPyTorchJaxTensorFlowROS2DockerGitLinuxSlurm
Doctoral researcher developing probabilistic and generative models for dynamical systems, with a dissertation on deep Bayesian inference for control.
Master of Science in Electrical Engineering with a focus on signal processing and machine learning, including a thesis on feature selection using attention mechanisms.
Bachelor of Engineering in Mechatronics as an exchange program, with a thesis on algorithms for avoiding singularity in industrial robots.
Bachelor of Engineering in Mechatronics with a focus on control technology, electronics, and information technology.
Developed a computer-vision-based surrogate modeling approach for finite-element simulations supporting process-reliable part removal in automated laser cutting.
Computer VisionSurrogate ModelingFinite Element AnalysisPython
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