Meet Sri Lankan Researcher — Rushani Wijesuriya

What are you currently working on or worked on before?

Over the past few years, I have been working mainly on statistical methodological research. For my undergraduate research, I evaluated a class of statistical tests called the randomization tests against the classical tests which are routinely used for hypothesis testing.

Here I aimed to understand which ones better perform under different distributional assumptions. I am currently a Biostatistics Ph.D. candidate and a casual academic tutor at Murdoch Children’s Research Institute, University of Melbourne. As a part of my Ph.D. research, I am now currently researching approaches for handling missing data, particularly focusing on a method called multiple imputations which have now become popular over the past few years.

You can read up all about my work and publications on my personal website.

What encouraged you to pursue your research topic?

I have always been interested in statistical methodical research as these methods are widely applied in almost all research areas, and hence research on these methods is critical to better understand them and to ensure valid results. My current research focus is on methods for handling missing data. Missing data is a very common problem in all research, but the problem is particularly exacerbated in longitudinal studies which are central in modern health and medical research as they observe and collect data from individuals over time.

Multiple imputations (MI) have become one of the most popular approaches that are used to handle missing data. While many implementations of the approach are available in many software which can be used in simpler settings quite easily, MI can be not so straightforward to carry out when the data structures become more and more complex (for example clustered data structures ). This is because the methods for doing so are quite recent or are still being developed, and there are important decisions to be made (the answers to which, are not quite clear yet).

This might compel applied researchers to use unprincipled methods such as case deletion (i.e. deleting those with missing data) to handle missing values which can introduce bias to the study results. Through my research, I aim to provide guidance as to which MI approaches to use to appropriately handle missing values in those very complex settings and make the methods, in general, are more accessible for the applied researchers.

Where do you find your best inspiration for your work?

I think I am inspired very easily, and by many things/people. I am constantly inspired by my supervisors at work and find them to be amazing mentors. I am also inspired by the things I read on a daily basis. These include other people’s research, books in general, and the popular media I consume. I have come to find a lot of inspiring people (especially academics) and their ideas on platforms like Twitter.

What’s one of your biggest personal achievements so far?

I recently was invited to present some of my work to a group of experts in missing data methodology and I think this definitely is one of my biggest achievements. When starting my Ph.D. I used to read a lot of their papers, which were significant contributions to missing data methodology.

So presenting my work to them itself was a very big challenge. I used to underestimate the impact of my research up until then and didn’t really think I was making a significant contribution. But, the fact that a group of experts was interested in hearing about my research made me realize the work I do is somewhat significant.

What lessons would you share with a budding researcher?

As an early career researcher, it is critical to make sure that you choose the right supervisors who can mentor you and advise you in these early stages. Research your potential supervisors and choose wisely.

Research can at times be hard and a grueling process. You are constantly going to be challenged in this field as you are trying to discover new knowledge. So learn to be comfortable not knowing things and don’t beat yourself over it. Keep putting in the work, be curious, and always keep learning.

What motivated you to be a researcher?

I was first interested in research as I enjoyed teaching a lot and therefore I was sure I wanted to be in academia. But now I think I love to research more than teaching as it has given me the opportunity to constantly learn new things.

According to your opinion, what are the changes that the Sri Lankan education system needs to do, in order to meet the requirement of the international industry and academia?

I can’t speak for the other disciplines but I think a lot of us coming from a STEM background has a lot of theoretical knowledge but limited applied knowledge. We need to move slightly away from using the grading system as the sole metric of assessing a student’s competence and give more opportunities for students to apply themselves and solve real-world problems using what they have learned. After all, it’s not knowledge learned if it's not knowledge applied.