As mentioned in the homepage, I am in a “pre-postdoc” stage, where I am enjoying a great deal of intellectual freedom and the ability to explore new topics and write grant applications on some of them. So here I list —maybe more for myself than for any reader— some of the fields I have explored or may want to explore in the future.
Mathematical statistics, particularly techniques that are directly applicable to relevant problems in (molecular) biology, like high-dimensional inference or learning theory. Biostatistics would be another relevant keyword here, especially when it comes to (pre)clinical trials and observational studies.
Non-statistical biomathematics that actually models real problems, e.g. simulations and analyses that inform public health policy. A personal field of interest are epidemics and infectious outbreaks, and in particular STIs.
Molecular basis of ageing, stem cells and regenerative medicine. This is a topic that has fascinated me since a really long time ago and it is one of my earliest inspirations to become a scientist.
Some kinds of cancer research. I tend to read more about somewhat "basic" (i.e. not entirely clinical) approaches, and especially the ones that are not totally specific to a certain tissue. However, I can also see myself applying my skills to specific problems with a high potential for immediate impact.
Rigorous omics and bioinformatics. In silico (or dry-lab-based) human molecular biology is enthralling and extremely promising, even when one asks basic or fundamental questions. I feel that my skills are best used when digging a bit deep into method choice or development, without losing sight of the application.
AI explainability. Even if my background is in white boxes, I understand the usefulness and need for other learning approaches in 2026. A particularly interesting statistical challenge is that of uncertainty quantification for black-box models, which is crucial for deploying deep-learning methods responsibly.
Causal inference. To me, it is an extremely interesting paradigm that is yet to show us its full potential.
Conservation biology, ecology and climate research. I may well never work on any of these, but I often find myself reading research in plant science, zoology or the many anthropogenic factors that are contributing to the current climate emergency. Together with the molecular biology of ageing, this is my other early inspiration to become a scientist.
I will probably not be able to produce research output on all of the above topics during my career, but my interest and enjoyment when learning something new about them will always be with me. And well, statisticians “get to play in everyone's backyard,” so almost nothing is impossible in the future.