Rodrigo Alves

"Matrix Completion and Learning for the Sciences: When a blockbuster helps coefficient predictions"

The aim of this project is to research, improve and apply machine learning methods based on matrix completion for pressing problems from the sciences. Scientific questions that we want to tackle are, e.g.: How to predict the breeding value of the plants in plant sciences? How can we predict the activity reaction of chemical compounds? These and many other problems in the sciences can be modelled through matrices or tensors, of which only a subset of the entries are observed, either because they can be expensive or impossible to obtain. In the PhD project, we are researching methods that predict the completion of the respective matrices or tensors and will investigate their application in the sciences, including plant sciences and chemistry. In this talk we will present the first results and the research perspectives.