CBE Fall Seminar Series presents Ruth Misener, Imperial College, London

The data science and statistical machine learning communities typically focus on models learned solely from observed data. But chemical engineering applications may also require explicit, parametric models, e.g. modeling known process constraints, operations constraints, and cost objectives. We consider new three probabilistic modeling applications and extend methodologies to meet these hybrid situations.