Dr Ruth Misener (she/her) is a Professor in the Computational Optimization Group.  Foundations of her research are in numerical optimization algorithms and computational software. Her applications focus on optimization challenges arising in industry, e.g. scheduling in manufacturing or experimental design in chemicals research. Ruth also contributes at the interface between operations research and machine learning.

Ruth is the BASF/RAEng Research Chair in Data-Driven Optimization (2022-27). She received the Macfarlane Medal as the overall winner of the 2017 RAEng Engineers Trust Young Engineer of the Year competition.

Ruth’s research team develops open-source code on GitHub, releases video presentations on YouTube, and announces new research on Twitter (Ruth @RuthMisener, Group @CogImperial).

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Projects

  • Global optimization of mixed-integer nonlinear programs

    This project develops algorithmic protocols and computational tools for solving mixed-integer nonlinear optimization problems; we focus on building effective solution strategies for engineering applications.

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  • Optimization over machine learning surrogates

    We’re hybridizing mechanistic, model-based optimization with data-driven optimization by developing mathematical programming formulations of machine learning surrogate models.

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  • Active learning & Experimental design

    We’re developing data-efficient strategies to reduce the number of experiments needed for chemical and pharmaceutical development and manufacturing.

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  • Optimization under uncertainty

    Safety-critical settings sometimes require optimization with respect to the worst-case realization of the problem setting. The adversarial, robust optimization setting is especially useful when errors are very costly.

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  • Explainable optimization

    Optimization solvers are often unexplainable black boxes where the reasoning for a solution is inaccessible to users. We use argumentation to empower the interaction between optimization solvers and users, supported by tractable explanations which certify or refute solutions.

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  • Scheduling & rescheduling

    In logistics applications such as our Royal Mail collaboration, a planned schedule is made in advance. But suppose something disruptive happens and we need to adapt quickly. How can we make rescheduling efficient?

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  • Designing heat recovery networks

    Heat exchanger networks reuse excess industrial process heat onsite and thereby improve energy recovery. We consider the resulting mixed-integer nonlinear optimization problem for designing heat recovery networks.

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  • Bioprocess optimization for stem cell tissue engineering

    This project considers robust superstructure design and operation of a bioreactor that produces red blood cells. Although we develop an optimization model for a specific bioreactor, the intellectual framework may generally apply to bioprocess optimization.

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  • Global optimization of petrochemical process networks

    This project optimizes fuel blending for energy systems on a feed-forward network of inputs, intermediate storage, and outputs; we investigate blending petrochemical feeds in a way that maximizes profit subject to environmental standards.

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  • Bayesian optimization with dimension scheduling: Application to biological systems

    Our Dimension Scheduling Algorithm reduces the computational burden of Bayesian optimization for many experiments. The key is optimizing the fitness function only along a small set of dimensions at each iteration.

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