课堂教学 Teaching

上海交通大学 Shanghai Jiao Tong University

  • ECE4530J:智慧城市中的决策问题 Decision making in smart cities
    Introduction to key applications in smart cities and relevant decision-making problems. Concepts of connected and autonomous vehicles, intelligent transportation systems, smart grid, smart living, smart environment, smart economy, smart governance. Formulation of decision-making problems embedded in smart city applications, including linear, nonlinear, stochastic, and game-theoretic control/optimization/learning problems. Computer simulation of the above applications and problems. Basic concepts in control/optimization/learning theories. Suitable for junior/senior students interested in preliminary knowledge of smart cities and decision-making theories. Prepares students for more advanced courses on control, optimization, and learning.
    授课学期 Offered in: 2021夏季 Summer

  • VE558:随机控制与强化学习 Stochastic Control and Reinforcement Learning
    Control and optimization of discrete-time and continuous-time Markov processes. Probability model, convergence of random variables. Countable-state Markov chains, continuous-state Markov chains, Markov decision processes, dynamic programming, Monte-Carlo method, temporal-difference method, approximate dynamic programming. Continuous-time Markov processes, Poisson processes, queuing theory, infinitesimal generator, piecewise-deterministic Markov processes. Applications include connected and autonomous vehicles, intelligent transportation systems, computer and communication systems, social networks, epidemics, and finance.
    授课学期 Offered in: 2021秋季 Fall

纽约大学 New York University

  • 工程系统随机模型与方法 Stochastic models and methods for engineering systems
    Basic theory of stochastic processes and random graphs with a variety of transportation applications. Random variables, events, laws of large numbers; Finite-state Markov chains, steady-state distribution, exponential convergence; Poisson process, Little’s theorem, M/M/1 queues, queuing networks, hybercube model, fluid model; Branching process, Erdős–Rényi model, geometric random graph; Applications in connected vehicles, intersections, highway traffic, transit, patrol, emergency services, air transportation, infrastructure maintenance, urban development.

  • 智慧城市数据分析学习方法 Analytics and learning methods for smart cities
    Basics of analytics and learning methods, with applications in smart cities. Focuses on introduction of algorithms in their very basic forms and their smart city applications. Topics include probability review, inference, linear regression, classification, neural networks, and introduction to reinforcement learning. Applications include autonomous vehicles, traffic control, public transit, ride-sharing, urban emergency response, smart grid, and smart buildings.