Ruth Misener is Professor in Computational Optimization in the Imperial College London Department of Computing. Ruth holds the BASF / Royal Academy of Engineering Research Chair in Data-Driven Optimization (2022 - 2027) and is also an Early Career Research Fellow (2017 - 2022) of the Engineering & Physical Sciences Research Council.
Ruth received an SB from MIT and a PhD from Princeton. Foundations of her research are in numerical optimization algorithms. Applications include decision-making under uncertainty, energy efficiency, process network design & operations, and scheduling. Ruth’s research team makes their software contributions available open source (https://github.com/cog-imperial). Ruth received the 2017 Macfarlane Medal from the Royal Academy of Engineering and the 2020 Outstanding Young Researcher Award from the AIChE Computing & Systems Technology Division.
This talk introduces OMLT (https://github.com/cog-imperial/OMLT), an open source software package incorporating surrogate models, which have been trained using machine learning, into larger optimisation problems. Computer science applications include maximizing a neural acquisition function and verifying neural networks. Engineering applications include the use of machine learning models to replace complicated constraints in larger design/operations problems. OMLT 1.0 supports GBTs through an ONNX (https://github.com/onnx/onnx) interface and NNs through both ONNX and Keras interfaces. We discuss the advances in optimisation technology that made OMLT possible and show how OMLT seamlessly integrates with the python-based algebraic modeling language Pyomo (http://www.pyomo.org). The literature often presents different optimization formulations as competitors, but in OMLT, competing formulations become alternatives: users can select the best for a specific application. We provide examples including neural network verification, autothermal reformer optimization, and Bayesian optimization.