About me

Research approach

My research agenda is centered on data-driven science and engineering. More precisely, I am fusing mathematical modelling, control theory, data fusion, machine learning, and computing systems to design and implement end-to-end solutions across domains (robotics, embedded systems, biomedicine, virtual reality, cloud computing). In the last 15 years, I have consolidated an applied research agenda and developed tools and a framework for optimizing dynamical systems (i.e. biological, technical) to achieve adaptability, autonomic calibration, and reinforced performance. Fundamentally, the key approach I am using is a domain-informed (e.g. physics-informed, biology-informed) machine learning framework that amounts to introducing appropriate observational, inductive or learning biases that can steer the learning process towards identifying domain-consistent solutions.


Short vita

After earning a PhD in Neuroscience and Robotics from TUM in 2016, I’ve spent one more year as Research Fellow in Neuromorphic Engineering with the TUM Center of Competence Neuroengineering before joining Huawei Research Center in Munich. Since 2017 I am Staff Research Engineer in Enterprise Intelligence for Cloud Solutions with Huawei’s largest research center outside China. At the same time, I am Head of the Laboratory and Principal Scientist at the Audi Konfuzius-Institut Ingolstadt, a Sino-German research initiative focused on Human-centered Intelligence Data Processing for Biomedicine and Biomechanics. Each term, I teach AI and ML for undergrads as Lecturer at Technical University of Ingolstadt.

I am a seasoned researcher with 15 years of academic research and 7+ years of industrial research experience. My research was disseminated in 40+ peer-reviewed publications and 10+ patents.