Monthly Archives: December 2019
As we are approaching the beginning of 2020, it is worth taking a minute to reflect over the year 2019. People first! We are thrilled to have welcomed two new PhD students in our group this year: Sooho Kim , … Continue reading
We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations. The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models. In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.
Amina presented her recent work Learning to Augment with Feature Side-information in this year's edition of ACML, which took place in the beautiful Nagoya, in Japan. Attending this conference has been a great opportunity to follow the latest advancements of the field, … Continue reading