printer friendly pageMS&E 323: Stochastic Simulation
Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.
Terms: Win
| Units: 3
Instructors:
Glynn, P. (PI)
MS&E 324: Stochastic Methods in Engineering (CME 308, MATH 228)
The basic limit theorems of probability theory and their application to maximum likelihood estimation. Basic Monte Carlo methods and importance sampling. Markov chains and processes, random walks, basic ergodic theory and its application to parameter estimation. Discrete time stochastic control and Bayesian filtering. Diffusion approximations, Brownian motion and an introduction to stochastic differential equations. Examples and problems from various applied areas. Prerequisites: exposure to probability and background in analysis.
Terms: Spr
| Units: 3
MS&E 325: Optimal Transport in Operations Research, Statistics, and Economics
Current stochastic models, motivated by a wide range of applications in engineering, business, and science, as well as the design and analysis of associated computational methods for performance analysis and control of such stochastic systems.
Last offered: Winter 2020
MS&E 326: Advanced Topics in Applied Data Science
Advanced Topics in Applied Data Science
| Repeatable
for credit
(up to 99 units total)
MS&E 328: Foundations of Causal Machine Learning
Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics may include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and double/debiased machine learning, theoretical foundations of high-dimensional linear regression, theoretical foundations of non-linear regression models, such as random forests and neural networks, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation, adaptive experimentation and inference.
Terms: Aut
| Units: 3
Instructors:
Syrgkanis, V. (PI)
MS&E 332: Security and Risk in Computer Networks
Risk management of large scale computing and networking systems with respect to security, data integrity, performance collapse, and service disruption. Qualitative and analytical basis for assessment, modeling, control, and mitigation of network risks. Stochastic risk models. Contact process. Random fields on networks. Virus and worm propagation dynamics and containment. Denial of service attacks. Intruder detection technologies. Distributed network attacks and countermeasures. Disaster recovery networks. Network protection services and resource placement. Autonomic self-defending networks. Economics of risk management. Emphasis is on analytics and quantitative methods.
Last offered: Winter 2021
MS&E 334: Topics in Social Data
This Ph.D. course will study advanced topics in causal inference, with a focus on nuances of experimental design and policy evaluation, particularly in settings with interference. We will emphasize discussion of a range of experimental designs, as well as applications in networks and marketplaces. The course will be taught in a seminar format, with an emphasis on in-depth discussion of recent research papers at the frontiers of this area. The class is restricted to Ph.D. students; exceptions require instructor approval.
Terms: Spr
| Units: 3
Instructors:
Johari, R. (PI)
;
Ugander, J. (PI)
MS&E 335: Queueing and Scheduling in Processing Networks
Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.
Terms: Aut
| Units: 3
Instructors:
Bambos, N. (PI)
MS&E 336: Computational Social Choice (CS 366)
An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or
CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Win
| Units: 3
Instructors:
Goel, A. (PI)
MS&E 337: Large Networks and Graph Limits
Random graph theory, Erdos-Renyi, and other network models, the algebra of graph homomorphisms, limits for dense and sparse graphs, and applications in algorithm design, graph representation learning, and others.
Terms: Aut
| Units: 3
Instructors:
Saberi, A. (PI)
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