Grantee story: István Gábor Hatvani

Grantee story: István Gábor Hatvani

With the support of the WATSON Conference Grant, I had the opportunity to attend the 12th Geostats Congress, one of the flagship gatherings in the field of earth sciences data analysis. The congress, first held half a century ago, is organized every four years. This time, it was held in Portugal, with nearly 200 scientists and geostatisticians from 24 different countries and 150 scientific contributions. Some of the biggest names in the field were also present.

I presented an oral talk at the end of the second day, on 3 September 2024, in the Hydrogeology section, with about 25 participants. I shared the results of the poster presentation in front of almost all the attendees on the same day and got into deeper discussion with ~10 people. The two topics I elaborated on were (i) the recalibration of the precipitation stable isotope monitoring network across Slovenia and Hungary (oral), and (ii) the machine learning estimation of a European-scale monthly precipitation isoscape (poster). The attendees at my talk included both eminent scientists and early-career researchers, including those from industry and academia, working on similar topics. This made it highly beneficial to discuss the current challenges I face and potential solutions. It was also valuable to learn about the software they use in their research, along with the “hidden tricks and tweaks.” I also exchanged contacts with colleagues from Europe and North America, which will undoubtedly support my future scientific endeavors. On the other hand, I engaged with multiple young career scientist and gave them ideas/suggestions to help them and also shared the ideas of the WATSON project with them.

In general, the congress was very interesting and helpful in my progress and future contributions to WATSON-related work, as I was able to broaden my knowledge on topics such as accounting for uncertainty in machine learning, exploring climate extremes in space, and quantifying and interpreting uncertainties in hydrogeological layer models, among others.

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