My primary background is in algorithm design. In 2010 I have successfully defended a doctoral thesis on the topic of model-based diagnosis (artificial intelligence/computer science). The main application of model-based diagnosis algorithms is for monitoring and intelligent reasoning about the health of physical systems. These physical systems can be analogue electrical circuits, complex digital circuits, thermodynamic systems, mechanical systems, and many others.
In addition to algorithm design for model-based diagnosis, I have experience in various topic from artificial intelligence such as Boolean satisfiability, knowledge representation, machine learning, automated planning, constraint satisfaction and optimization, and probabilistic reasoning.
I am actively publishing in selective conference proceedings such as IJCAI (International Joint Conference on Artificial Intelligence), AAAI (American Association for Artificial Intelligence), PHM (Prognostics and Health-Management). I am a Program Committee member for a number of computer science events and an organizer for, amongst others, the International Workshop on Principles of Diagnosis (DX). I am very interested in AI competitions and I am one of the founders and organizers of the International Diagnostic Competition (DXC).
After my graduation I have spent two years in Switzerland, working on a project for UWB (Ultrawide-Band) localization for mobile robots. In this project I have integrated the whole system, implemented the signal-processing firmware in VHDL, designed and implemented the localization algorithms, assessed the performance of the whole system and submitted a number of papers on robotics and localization.
My interests are model-based diagnosis, automated fault isolation and recovery, model-based prognosis, testing and test generation, stochastic local search, satisfiability, constraint optimization, abduction and non-monotonic reasoning, reverse engineering, general problem solving and others.