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Smart City Analytics (MBA Elective)

Recent technological advances have enabled opportunities for smart city development. In smart cities, data is being collected and processed at unprecedented volume, velocity and scope. On the one hand, this development allows real-time monitoring and control of key infrastructure and efficient delivery of public services adaptive to users' needs; on the other hand, this leads to a number of new challenges in the planning and operations of urban services that arise in the data-rich environment. 

Designed around practical case studies, the course aims to help participants develop managerial insights into some of these challenges, as well as analytics tools that may help overcome them. A key feature of the course is the use of intensive, case-based problem solving exercises involving real data, designed to help participants sharpen their data analytics skills while addressing business-relevant questions. In the first iteration of this new course (Summer 2017), we focused on the topics of smart energy and smart mobility. Questions that we addressed include:

  • How to integrate renewable power into a fossil-fuel-heavy energy mix? How does renewable (e.g., wind) power interact with conventional generation sources like coal and gas, which have very different characteristics?

  • Do wind power and energy storage complement each other, and how well?

  • Is vehicle-to-grid (V2G) energy storage as valuable as grid-level storage to a smart grid?

  • How to identify and visualize temporal-spatial usage patterns in on-demand car sharing systems?

  • Does on-demand car sharing work well under time-varying travel patterns?

  • How to efficiently manage car sharing fleets to counter spatial imbalance of commuting patterns?

Analytics: (MBA and EMBA Core)

In the current competitive environment, it is important to understand the relationships between different business factors, to forecast trends, to appreciate the risks arising from management actions, and to optimize investment strategies. Decisions are often taken under considerable uncertainty and time pressure. Therefore, managers need to be able to grasp the range of uncertainty rapidly and make rational decisions, which are both flexible and robust. This core course aims to enhance participants' ability to apply modern decision technology and statistical methods to decision-making in business context, and to provide students with conceptual exposure to contemporary analytics methods such as machine learning and artificial intelligence. The course is multidisciplinary, with links to accounting, economics, finance, marketing and operations management. 

Past Teaching

Prior to joining the Saïd Business School, I had taught various courses at the University of California at Berkeley and the Hong Kong University of Science & Technology, at the undergraduate, master's and doctoral levels:

  • Supply chain design and operations

  • Logistics Planning and Service Management

  • Logistics and Freight Transportation Operations

  • Transportation Systems

  • Making Smart Decisions in Daily Life with Engineering Ideas

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