课堂教学 Teaching

上海交通大学 Shanghai Jiao Tong University

• VV556: 应用数学方法 Applied Math Methods
  Introduction to the theory of functional analysis, including review on math basics, normed spaces, measure theory, and Hilbert spaces. Review on calculus and linear algebra, basics of real analysis and complex analysis. Vector spaces, norms, Banach spaces. Lebesgue integral, sigma algebra, measure, measurable functions. Inner product, Hilbert spaces, orthonormal bases, Fourier series. Bounded linear operators, spectrum, adjoints, spectral theorem. Applications in control, optimization, and machine learning.
  秋季学期开设 Offered in fall

• 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. Learning-based methods with exact or approximate solutions. Probability model, convergence of random variables. Countable-state Markov chains, continuous-state Markov chains, Foster-Lyapunov stability theory, Markov decision processes, dynamic programming. Continuous-time Markov processes, Poisson processes, queuing theory, infinitesimal generator, piecewise-deterministic Markov processes. Monte-Carlo method, temporal-difference method, approximate dynamic programming, Q learning, learning-based adaptive control. Applications include connected and autonomous vehicles, intelligent transportation systems, computer and communication systems, social networks, epidemics, and finance.
  秋季学期开设 Offered in fall

• 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 controloptimizationlearning problems. Computer simulation of the above applications and problems. Basic concepts in controloptimizationlearning 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.
  夏季学期开设 Offered in summer

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