Who are you and how did you become interested in AI and the technology around it?
I would say I am a person with a good level of curiosity and a desire to learn. At school, I was mostly interested in math, philosophy, and literature. Then I decided to enroll in engineering, assuming it would have given me better job opportunities. In my third year, I discovered the world of “Signal Processing” and I loved it … it is basically applied math/statistics, and it is behind most of the technology we use every day.
What is the most important thing you do at work?
Research: includes writing project proposals for research funds (also in collaboration with research institutions and industries), developing and validating new methodologies, coordinating the work of other researchers (including supervision of doctoral students), disseminating the results through scientific publications.
Teaching: I am currently responsible for introducing the basic concepts of statistical signal processing (estimation, detection, and classification) to the students in the study program Electronic Systems Design and Innovation at NTNU.
What do you focus on in technology/innovation?
I have long experience with data fusion in sensor networks, mostly algorithms design, and performance evaluation. Recently, I am focusing more on sensor-data processing for monitoring applications mostly in the energy and maritime domain using both model-based and data-driven approaches.
Why is it exciting?
It has a good level of abstraction (I like math ) and also a direct impact on practical applications. Basically, it offers a large variety of opportunities from theoretical modeling to experimental research.
What do you think are the most interesting controversies?
If I should select one, maybe the skepticism towards AI/ML (Artificial Intelligence/Machine Learning) approaches, often quickly and unfairly labeled “black-box” with negative emphasis. XAI (Explainable AI) and uncertainty quantification will be crucial for the deployment of AI/ML-based solutions in safety-critical applications.
What do you think is relevant knowledge for the future?
As (in my opinion) it has always been: the ability to learn (possibly fast and deep). Assuming that literacy nowadays includes basic knowledge of digital tools, then the ability to acquire and master new knowledge remains the most important issue. Such ability can be developed by means of any topic, e.g. studying literature, math, music, philosophy, etc.
What do we do uniquely well in Norway within AI?
There are many talents in various AI-related fields, so naming one field would be unfair to many others. However, I would like to stress again the strategic relevance of Norwegian research in the maritime domain. To make an explicit example, even though it is relevant to contribute to the research enabling autonomous cars, I assume that no one expects that Norway will be leading this sector. Differently, Norway has good chances to be the world leader in autonomous ships.