Meet the Academic - Martin Benning
In this blog post, Dr Martin Benning, Lecturer in Optimisation / Machine Learning at Queen Mary University of London, talked about his own research interests, his teaching style and the Statistics and Data Science research group, of which he is a member.
What do you enjoy about teaching at the School of Maths at Queen Mary?
At the School of Maths, I enjoy teaching a very diverse cohort of students. It makes teaching more interesting on a day-to-day basis, as it challenges my ideas of how to teach and what to teach. I also enjoy that I can teach modules that focus on topics that are close to own my research, such as Machine Learning with Python and Advanced Machine Learning, as part of the MSc in Data Analytics.
What are your main research interests?
My main research interests are the theoretical and computational handling of inverse and ill-posed problems. In inverse problems, an unknown quantity – such as the image of the interior of a human body – is only accessible indirectly through the inversion of a mathematical operator. In nearly all relevant applications, this inversion process is highly unstable with respect to measurement errors. A remedy is the approximation of the inverses via families of continuous operators, known as regularisations. My research focuses on the theoretical analysis and numerical realisation of such regularisations. In recent years, regularisations have also become more and more relevant in connection with modern machine learning concepts such as invertible deep neural networks.
Tell us a bit more about the Statistics and Data Science group, of which you are a member.
The Statistics and Data Science (SDS) group is relatively new and is comprised of researchers from both statistics and data science (as the name suggests). I have been part of the Complex Networks group prior to being a member of SDS, but because of my shift in both research and teaching towards data science, I decided to join SDS once it was brought to life. In the SDS group, we have regular meetings at which we discuss recent developments that are relevant to the group, invite guest researchers to provide stimulating guest lectures, or have social gatherings to also talk about matters that are not necessarily work-related.
Have you always known you wanted to be a lecturer?
Not at all. When I finished my PhD, my aim was to move into the industry. I wanted to become a researcher, which limited my options at the time. I then received an offer for a postdoc position in Chemical Engineering at the University of Cambridge that excited me, and I continued the pursuit of an academic career instead. The ability to pursue research questions that both interest and fascinate me is what convinced me into wanting to be a lecturer.
What is your teaching style like?
I want students to get the impression that I am approachable as a teacher so that they will not feel the need to hold back with some of their pressing questions. I certainly follow the course material during my lectures, but I am more than happy to take a diversion if students want to explore a topic, for which we have only scratched the surface, in greater detail. I teach mostly students of the MSc in Data Analytics, which are a very diverse cohort with different levels of mathematical training. If time permits, I like to provide them with course material that also addresses these different levels.