About Hans Korving

Hans Korving is Machine Learning-expert, cross bucket modelleur and hydrologist, he does not believe in 'one bucket per modeller'. His career spans hydrology, system identification, data assimilation, Bayesian inference (a statistical approach that allows you to investigate and quantify cause-and-effect relationships between variables), applied Machine Learning (ML), Deep Learning (DL), and causal modelling. Each field has its strengths and its blind spots.

Hans believes that real insight comes from treating these disciplines as a palette to draw from, rather than as silos to stay within. The intersection where physical understanding meets machine learning and statistical reasoning. From this combined perspective, he works on extreme hydrological events such as floods, droughts, and water quality challenges.

He is committed to choosing the right tools and combining them transparently to deliver models and results that decision-makers can trust.