计算机科学哲学博士-人工智能和机器学习

无人驾驶汽车，智能助手和智能家居设备–我们致力于改善和发现能源，金融，医疗保健，教育，交通等领域的AI技术。我们开发交互式决策工具，并创建算法来应对存在不确定性的跨学科领域：从施工管理到火星探测器。我们调查围绕学生持久性的复杂问题，并深入研究机器学习。我们的部门拥有架构，人工智能，云计算，网络安全，数据科学，分布式系统，嵌入式和多媒体系统，图形和可视化，人机交互，机器学习，软件工程，虚拟环境和无线网络方面的专家。我们在科学可视化方面的研究将大数据转变为可理解的数据集，而虚拟现实研究人员则在研究人类认知与虚
Self-driving cars, intelligent assistants, and smart-home deviceswe work to improve and discover AI technology in the fields of energy, finance, health care, education, transportation and more. We develop interactive decision-making tools, and create algorithms to respond to interdisciplinary fields where uncertainty is present: from construction management to the Mars rover. We investigate complex issues surrounding student persistence, and dig deep into machine learning.<br>This CRISP (Computer Retrieval of Information on Scientific Projects) project addresses the challenges associated with the rapid evolution of the electricity grid to a highly distributed infrastructure. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels for delivering electricity to customers, to distribution microgrids, entities that actively manage local production, storage and use of electricity, with participation from individual customers. Distribution microgrids combine the advantages of the traditional electricity grid with the advantages of emerging distributed technologies, including the ability to produce and use power locally in the event of grid outages. The project will result in a unified model that incorporates key aspects of power generation and delivery, information flow, market design and human behavior. The model predictions can be used by policymakers to guide a transition to clean energy via distribution microgrids. The expectation is to enable at least 50% of electric power to come from renewable resources. This cannot be done with either the traditional grid, due to its limited capacity to accommodate intermittent renewable power sources, or with fully decentralized approaches, which would not be affordable for most utility customers.