Interview with Nicolas Guelfi
Can you briefly introduce yourself?
I am full professor at University of Luxembourg. Since 20 years I have contributed to the creation of the university. I founded the Laboratory for Advanced Software Systems at University of Luxembourg which I directed for more than 10 years. I have been member of the ERCIM (European Research Consortium in Mathematics and Informatics) executive committee and founded the ERCIM Working Group on Rapid Integration of Software Engineering Techniques and the ERCIM Working Group on Software Engineering for Resilient Systems. I also have been expert for the courthouse of Luxembourg concerning trials on conformance questions for 10 years. I mainly have contributed to the field of software engineering as a researcher by publishing articles, editing books, acting as program chair or reviewing committee member. My research contributions has mainly focused on: requirements definition and specification using formal, semi-formal or informal methods; and system dependability and resiliency. My current main field of research is on development methods for deep learning. Finally, I am the head a new bachelor in Computer Science at the University of Luxembourg which opened in 2017 and I am planning to open a private academy in artificial intelligence and software engineering.
What are the main research challenges you are working on now?
Software engineers require a large amount of data for building neural network-based software systems. The engineering of these data is often neglected, though, it is a critical and time-consuming activity. In my team we try to develop a novel software engineering approach for dataset augmentation using neural networks. We propose a rigorous process for generating synthetic data to improve the training of neural networks.
What are in your opinion the main research challenges in AIs, robotics and reasoning?
I would say, first, mastering from a software engineering perspective the foundations of AI systems engineering. This means finding the right methods and tools based on sound theories to master the quality of AI based systems. Secondly introduce causality in probabilistic reasoning.
What is are favorite online resources?
IEEE and ACM websites for high quality publications in my research domains ResearchGate and linked in for networking AWS and GCP for online computing resources.
What are your aims outside of academia?
Not so much except …. health and family 🙂 🙂