On exchange with eSSENCE: Rahul Manavalan

How has the eSSENCE Exchange collaboration contributed to your network?

Significantly. The eSSENCE Exchange expanded my academic network beyond my home department at Lund University through a research visit to Uppsala University. It connected me with researchers in scientific computing and machine learning, including the ML for Life Sciences group, and introduced me to the radial basis function research community. In addition, interactions with a diverse group of PhD students at the chair strengthened my peer network through informal exchange and collegial discussions, creating clear pathways for future collaborations.

How has the collaboration contributed to your research?

The collaboration contributed directly to the development and positioning of my research on inverse problems and the statistical interpretation of scientific computing. While my primary background is in statistics and machine learning, with Gaussian processes as a core methodological tool, the exchange exposed me to the radial basis function (RBF) literature, where several problems considered open in my home community have already been addressed under different formulations. This insight helped clarify the true state of the art and sharpened the research frontier for my work. Moreover, discussions with other PhD students—particularly those working on applied problems—provided concrete application contexts for my method-development-oriented research, offering clear opportunities to translate theory into practice.

What do you hope the collaboration will lead to in the long term?

In the long term, I expect the collaboration to result in sustained research partnerships and joint publications. The exchange has already initiated a collaboration with Elisabeth Larsson, Professor at Department of Information Technology, Uppsala University, where ideas from operator theory are being used to precondition radial basis function approximators; a manuscript based on this work is currently in preparation.