My (CV) research studies (see list of my publications) different incarnations of Machine Learning (ML), and various applications of those. My interests are found in theoretical, algorithmic and application-oriented aspects of the question: ‘What makes an ML algorithm work for a given case?’ This is a non-applied question really, but applications play a fundamental role here.
It is fair to say that ML (or data-based techniques) are evolving partly due to the ever-increasing availability of computational power. But while the availability of fast hardware is mostly regulating the latter, the limits of the former are often dictated by the availability of proper algorithms. A large class of problems do not require massive hardware resources. Properly designed algorithms are (1) theoretically sound, (2) in-tune with its intended application aim, and (3) computationally attractive. These topics are invariantly present underlying trending topics as neural networks, kernel machines, compressed sensing, deep learning, business analytics and Big Data.
A particular focus of mine is to see how this fits within the context of dynamic systems and automatic control. The task of estimation in this context is studied under the name of system identification. It is not surprising that this branch of electrical engineering finds wide overlap with the aims as set out in the machine learning arena.
Presently, I started to look into the research question how one should design algorithms for societies sake. A pertinent and politically charged question really!