Automated decision making via optimization and machine learning

该研究项目将开发一个集逆优化、稳健优化和机器学习于一身的新型自动化决策平台。许多决策问题结合了优化和机器学习。例如，生成决策的优化模型可以使用通过机器学习预测的辅助参数。要训练机器学习模型，需要有关参数的数据。然而，直接获得所需解决方案的数据往往更容易、更相关。因此，我的发现计划的目标是为这种预测-然后重建-范式开发新的计算工具。特别是，我将使用机器学习来预测最优解决方案的理想特性。然后，我将开发新的逆优化方法来学习稳健优化模型的参数，该模型可以生成具有所需属性的解。该框架将在某种意义上实现自动化，即所有需要的都是与解质量相关的基本协变量；有了这些数据，机器学习，然后是反向优化，然后可以在没有人工干预的情况下实现稳健优化。 本研究项目的主要应用是放射治疗治疗计划。放射治疗是治疗癌症的主要方法之一。目前设计治疗方法的过程效率低下，依赖于人工反复试验的努力。这一挑战源于在规划软件中确定将产生可接受治疗的适当参数的困难。使用我的先预测后重建的方法，我将演示自动生成治疗计划。根据历史治疗训练的深度学习模型将预测患者的临床可接受剂量分布，仅给出成像数据。然后，反向优化模型将学习稳健优化模型的参数，该模型将创建具有临床所需特征的可交付治疗计划。我的自动化计划方法将同时提高效率和流程标准化，而不会牺牲治疗的个性化。虽然实例应用是放射治疗，但一般方法将广泛适用。 优化和机器学习是快速增长的领域，毕业生短缺。我的实习生将发展高度可就业的技能，这些技能将在医疗保健、交通运输、制造、金融、供应链管理、能源和国防等数据驱动型行业得到重视。将我的研究应用于放射治疗也具有重大的经济潜力。放射治疗被用于治疗一半的癌症患者，但设计和提供治疗的熟练人员的短缺迫在眉睫。我的研究有助于缩小供需缺口，并使放射治疗得以规模化提供，这在发展中国家尤其重要。因此，我的研究代表了一项加拿大制造的创新，具有全球影响力。

This research program will develop a new automated decision making platform at the intersection of inverse optimization, robust optimization and machine learning. Many decision making problems combine optimization and machine learning. For example, an optimization model that generates decisions may use auxiliary parameters predicted via machine learning. To train the machine learning model, one needs data on the parameters. However, it is often easier and more relevant to obtain data on the desired solutions directly. Thus, the goal of my Discovery program is to develop new computational tools for this predict-then-reconstruct paradigm. In particular, I will use machine learning to predict desirable characteristics of an optimal solution. Then, I will develop new inverse optimization methods to learn parameters of a robust optimization model that can generate a solution with the desired properties. The framework will be automated in the sense all that one needs are basic covariates that relate to solution quality; with this data, machine learning, then inverse optimization, then robust optimization can be implemented without human intervention. The main application in this research program is radiation therapy treatment planning. Radiation therapy is one of the primary ways to treat cancer. The current process to design a treatment is inefficient, relying on manual trial-and-error effort. The challenge stems from the difficulty in determining appropriate parameters in the planning software that will produce an acceptable treatment. Using my predict-then-reconstruct approach, I will demonstrate automated treatment plan generation. A deep learning model trained on historical treatments will predict a clinically acceptable dose distribution for a patient, given only imaging data. Then, an inverse optimization model will learn parameters of a robust optimization model that will create a deliverable treatment plan with clinically desirable characteristics. My automated planning approach will simultaneously improve efficiency and process standardization without sacrificing treatment personalization. While the example application is radiation therapy, the general methodology will be broadly applicable. Optimization and machine learning are fast-growing fields that are in short supply of graduates. My trainees will develop highly employable skills that will be valued in data-driven industries like healthcare, transportation, manufacturing, finance, supply chain management, energy and defense. The application of my research to radiation therapy also has major economic potential. Radiation therapy is used to treat half of all cancer patients, but there is a looming shortage of skilled personnel to design and deliver treatments. My research helps close the demand-supply gap and allows radiation therapy to be delivered at scale, particularly important in developing countries. Thus, my research represents a made-in-Canada innovation with global impact.