Automated decision making via optimization and machine learning

本研究项目将在逆优化、鲁棒优化和机器学习的交叉领域开发一个新的自动化决策平台。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能使用通过机器学习预测的辅助参数。为了训练机器学习模型，我们需要参数的数据。然而，直接获得所需解决方案的数据往往更容易，也更相关。因此，我的“发现”项目的目标是为这种“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的理想特性。然后，我将开发新的逆优化方法来学习一个鲁棒优化模型的参数，该模型可以生成具有期望属性的解。从某种意义上说，框架将是自动化的，人们所需要的只是与解决方案质量相关的基本协变量；有了这些数据，机器学习，然后逆优化，然后鲁棒优化可以在没有人为干预的情况下实现。本研究项目的主要应用是放射治疗的治疗计划。放射治疗是治疗癌症的主要方法之一。目前设计一种治疗方法的过程效率低下，依赖于人工试错。挑战源于难以在规划软件中确定适当的参数，以产生可接受的处理。使用我的预测-重建方法，我将演示自动生成治疗计划。一个深度学习模型经过历史治疗训练后，仅根据影像学数据就能预测出患者临床可接受的剂量分布。然后，逆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期望特征的可交付治疗计划。我的自动化规划方法将在不牺牲治疗个性化的前提下，同时提高效率和流程标准化。虽然示例应用是放射治疗，但一般方法将广泛适用。优化和机器学习是快速发展的领域，毕业生供不应求。我的学员将培养高度可就业的技能，这些技能将在医疗保健、交通运输、制造业、金融、供应链管理、能源和国防等数据驱动的行业中得到重视。我的研究应用于放射治疗也有很大的经济潜力。放射疗法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，并允许大规模提供放射治疗，这在发展中国家尤其重要。因此，我的研究代表了一项具有全球影响的加拿大制造创新。

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.