An interview with Associate Professor Hugo L. Hammer

Describe your journey into research from your youth up to the present. What foundational lessons did you learn from this journey? Why were you initially attracted to multimedia?

From an early age, I had the ability to focus and work individually and loved to develop new systems for all sorts of things, which probably was quite annoying for those around me. It turns out that it is these abilities to focus, being curious, and developing new systems is what drives my research today. When I started as a student in mathematics and statistics at the Norwegian University of Science and Technology (NTNU), I didn’t think of research as an alternative and was determined to find a job in the industry. Throughout the studies, I learned how little mathematics and statistics I had actually learned, which is why I decided to become a Ph.D. student. I expected to find a job in the industry after the Ph.D. period but ended up loving research, and that is why I am where I am today.

As a statistician, I have worked a lot with spatial and spatio-temporal data, such as geophysical observations. Such observations have striking similarities to multimedia content, such as images and videos. I have become very interested in machine learning methods used to process and make decisions from multimedia content and the potential for applying such methods towards other applications, such as geophysical applications. I also love working as a statistician within this field. A crucial part of my research is to try to combine methods from machine learning and statistics into new and exciting ways.

Tell us more about your vision and objectives behind your current roles? What do you hope to accomplish, and how will you bring this about? 

In my current position as an associate professor, I do both teaching and research. Teaching and research challenge me in different ways. I continuously try to develop and improve my teaching. I especially focus on how to do high quality, yet resource-efficient, teaching. I have, for example, worked a lot on how to activate students and improve learning when being a single teacher for hundreds of students.

Can you profile your current research, its challenges, opportunities, and implications?

My current research can roughly be divided into three directions. The first direction is about methods for real-time information processing and decision making, for example, from sensory information or video streams. The second direction is based on developing new machine learning models and methods, and as mentioned above, by taking advantage of my background in statistics. The third direction is doing more applied use of machine learning methods toward real-life multimedia data, in particular, medical data. Direction two and three go hand in hand. Having a background in statistics and working more and more with multimedia data is more of an opportunity than a challenge.

How would you describe your top innovative achievements in terms of the problems you were trying to solve, your solutions, and the impact it has today and into the future?

 I am proud of the research we have done on real-time information processing and decision making. Our developed methods are simple but still document state-of-the-art performance. In 2020, we plan to develop software packages to make the methods readily available and hopefully useful for many. We saw the potential of using machine learning, and in particular deep learning, towards geophysical data and problems quite early, and we are now able to operate at the forefront of this research. I’m also proud of our externally funded research projects and, for sure, our rejected research proposals.

Over your distinguished career, what are your top lessons you want to share with the audience?

Here is a lesson from my personal experience. I think it is easy to depend on or have too much respect for other researchers early in the career. Research is of course all about collaboration, but still, for me, it was useful early in the career to create a small research project where I did every step of the process myself (shaping ideas, collecting data, running simulation, writing, finding suitable publishing channels, revisions, etc.). It was hard work, but for sure, it made me a better and more independent researcher.

What is the best joke you know?

Daddy, what are clouds made of?

Linux servers, mostly.

If you were conducting this interview, what questions would you ask, and then what would be your answers?

One suggestion: What do you like to do in your spare time?

Research, right? 🙂 Working every day at an office, I try to find time for physical activity in my spare time. I love to run, bike, or go skiing in Nordmarka (a forest near Oslo, Norway) or in the mountains on the weekends.

Bio: Hugo L. Hammer is an associate professor in statistics at Oslo Metropolitan University. His main research interests are computational statistics, probabilistic forecasting, real-time analytics, and machine learning.