MS&E 20:
Discrete Probability Concepts And Models
Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation. The course is fast-paced, but it has no prerequisites.
Terms: Sum
| Units: 4
| UG Reqs: WAY-FR
MS&E 52:
Introduction to Decision Making
How to ensure focus, discipline, and passion when making important decisions. Comprehensive examples illustrate Decision Analysis fundamentals. Consulting case studies highlight practical solutions for real decisions. Topics: declaring when and how to make a decision, framing and structuring the decision basis, defining values and preferences, creating alternative strategies, assessing unbiased probabilistic judgments, developing appropriate risk/reward and portfolio models, evaluating doable strategies across the range of uncertain future scenarios, analyzing relevant sensitivities, determining the value of additional information, and addressing the qualitative aspects of communication and commitment to implementation. Required for all students are three problem sets, three in-class exams, and a take-home final exam. Students taking the course for 4 units of credit must also complete and present a team project that analyzes a decision currently being made by an organization of their choice. Not intended for MS&E majors.
Terms: Sum
| Units: 3-4
MS&E 92Q:
International Environmental Policy
Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U.S. Congress.
Terms: Win
| Units: 3
MS&E 93Q:
Nuclear Weapons, Energy, Proliferation, and Terrorism
Preference to sophomores. At least 20 countries have built or considered building nuclear weapons. However, the paths these countries took in realizing their nuclear ambitions vary immensely. Why is this the case? How do the histories, cultures, national identities, and leadership of these countries affect the trajectory and success of their nuclear programs? This seminar will address these and other questions about nuclear weapons and their proliferation. Students will learn the fundamentals of nuclear technology, including nuclear weapons and nuclear energy, and be expected to use this knowledge in individual research projects on the nuclear weapons programs of individual countries. Case studies will include France, UK, China, India, Israel, Pakistan, North Korea, South Africa, Libya, Iraq, and Iran, among others. Please note any language skills in your application. Recommended: 193 or 293.
Terms: Spr
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
MS&E 108:
Senior Project
Restricted to MS&E majors in their senior year. Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results. Service Learning Course (certified by Haas Center). Satisfies the WIM requirement for MS&E majors.
Terms: Win
| Units: 5
MS&E 111:
Introduction to Optimization (ENGR 62, MS&E 211)
Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
MS&E 111X:
Introduction to Optimization (Accelerated) (ENGR 62X, MS&E 211X)
Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.
Terms: Aut, Win
| Units: 3-4
MS&E 112:
Mathematical Programming and Combinatorial Optimization (MS&E 212)
Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: 111 or MATH 103, CS 106A or X.
Last offered: Winter 2017
| Units: 3
MS&E 120:
Probabilistic Analysis
Concepts and tools for the analysis of problems under uncertainty, focusing on focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: CME 100 or MATH 51.
Terms: Aut
| Units: 5
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
MS&E 121:
Introduction to Stochastic Modeling
Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite: 120, 125, or equivalents.
Terms: Spr
| Units: 4
| UG Reqs: GER:DB-EngrAppSci
MS&E 125:
Introduction to Applied Statistics
An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information, however, requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands-on course, we learn to explore and analyze real-world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results. Prerequisite: 120, CS 106A, or equivalents.
Terms: Win
| Units: 4
MS&E 130:
Information Networks and Services
Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer-to-peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisites: 111, 120, and CS 106A.
Terms: Win
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
MS&E 135:
Networks
This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".
Terms: Spr
| Units: 3
MS&E 140:
Accounting for Managers and Entrepreneurs (MS&E 240)
Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career. Enrollment limited. Admission by order of enrollment.
Terms: Win, Sum
| Units: 3-4
MS&E 140X:
Financial Accounting Concepts and Analysis
Introductory course in financial accounting. Accounting is referred to as the language of business. Developing students ability to read, understand, and use business financial statements. Understanding the mapping between the underlying economic events and financial statements, and how this mapping can affect inferences about future firm profitability. Introduction to measuring and reporting of the operating cycle; the process of preparing and presenting primary financial statements; the judgment involved and discretion allowed in making accounting choices; the effects of accounting discretion on the quality of the (reported) financial information; and the fundamentals of financial statement analysis. Class time will be allocated to a combination of lectures, cases and discussions of cases. Capstone project analyzing a company's financials at the end of the quarter. Enrollment limited. Admission by order of enrollment.
Terms: Spr
| Units: 2
MS&E 145:
Introduction to Investment Science
Introduction to the financial concepts and empirical evidence that are useful for investment decisions. Time-value of money: understanding basic interest rates, evaluating investments with present value and internal rates of return, fixed-income securities. Risk-return tradeoff and pricing models: mean variance optimization and portfolio choice, capital asset pricing theory and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisites: basic preparation in probability, statistics, and optimization as covered e.g. in 111 and 120.
Terms: Aut
| Units: 4
MS&E 146:
Corporate Financial Management
Key functions of finance in both large and small companies, and the core concepts and key analytic tools that provide their foundation. Making financing decisions, evaluating investments, and managing cashflow, profitability and risk. Designing performance metrics to effectively measure and align the activities of functional groups and individuals within the firm. Structuring relationships with key customers, partners and suppliers. Prerequisite: 145, 245A, 245G or equivalent.
Terms: Win
| Units: 4
MS&E 147:
Finance and Society for non-MBAs (ECON 143, IPS 227, POLISCI 127A, PUBLPOL 143)
The financial system is meant to help people, businesses, and governments fund, invest, and manage risks, but it is rife with conflicts of interests and may allow people with more information and control to harm those with less of both. In this interdisciplinary course we explore the forces that shape the financial system and how individuals and society can benefit most from this system without being unnecessarily harmed and endangered. Topics include the basic principles of investment, the role and ¿dark side¿ of debt, corporations and their governance, banks and other financial institutions, why effective financial regulations are essential yet often fail, and political and ethical issues in finance. The approach will be rigorous and analytical but not overly technical mathematically. Prerequisite: Econ 1
Terms: Win
| Units: 4
MS&E 148:
Ethics of Finance
Explores the ethical reasoning needed to make banking, insurance and financial services safer, fairer and more positively impactful. Weighs tradeoffs in how money is created, privileging some, under-privileging others, using market mechanisms for transforming and trading financial risk, return, maturity and asset types. Technology is changing banks, financial markets, insurance and money. Like technology for medicine, finance is being rebuilt as machine learned code, algorithmic investment rules and regulatory monitoring. Risk models can be built to detect fraud and ethical lapses, or to open doors for them. Investment valuation models can optimize short term or long term returns, by optimizing or ignoring environmental and social impacts. Transparency or opacity can be the norm. Transforming finance through engineering requires finding, applying and evolving codes of professional conduct to make sure that engineers use their skills within legal and ethical norms. Daily, financial engineers focus on two horizons: on the floor, we stand on the bare minimum standards of conduct, and on the ceiling, we aim for higher ethical goals that generate discoveries celebrated though individual fulfillment and TED Talks. Stanford engineers, computer scientists, data scientists, mathematicians and other professionals are building systems for lending, investment and portfolio management decisions that determine future economic and social growth. This course uses the case method to preview intersecting codes of conduct, legal hurdles and ethical impact opportunities, and creates as a safe academic setting for seeing career-limiting ethical stop signs (red lights) and previewing ¿what¿s my life all about¿ events, as unexpected threats or surprising ah-ha moments. Guest speakers will highlight real life situations, lawsuits and other events where ethics of financial engineering was a predominant theme, stumbling block or humanitarian opportunity.
Terms: Aut
| Units: 2
MS&E 149:
Hedge Fund Management
Introduction to hedge fund management. Students actively manage the $1MM Stanford Kudla Fund employing Equity Long/Short, Macro and Quantitative Investment Strategies. Modeled after a hedge fund partnership culture, participation involves significant time commitment, passion for investing, and uncommon teamwork and communication skills. Open to advanced undergraduate and graduate students with continuing participation expectation. Limited to 12 students. Enrollment by application and permission of Instructor.May be repeat for credit.
Terms: Aut, Win, Spr
| Units: 1-2
| Repeatable
15 times
(up to 30 units total)
MS&E 152:
Introduction to Decision Analysis
How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
MS&E 175:
Innovation, Creativity, and Change
Problem solving in organizations; creativity and innovation skills; thinking tools; creative organizations, teams, individuals, and communities. Limited enrollment.
Terms: Win
| Units: 3-4
MS&E 177:
Creativity Rules
This ambitious course focuses on factors that contribute to creativity and innovation in individuals and groups within organizations. Uses a wide variety of experiential methods, including workshops, case studies, simulations, and team projects, supported by guest speakers and readings. Sample topics include how to frame and re-frame problems, how to challenge assumptions, how to work on creative teams, how to generate and test ideas, and how to tell a compelling story to communicate your ideas. Students also take on a quarter-long, team-based challenge that allows them apply the tools they learn to a real-world problem. Limited enrollment. Admission by application.
Terms: Spr
| Units: 4
MS&E 178:
The Spirit of Entrepreneurship
Is there more to entrepreneurship than inventing the better mouse trap? This course uses the speakers from the Entrepreneurial Thought Leader seminar (MS&E472) to drive research and discussion about what makes an entrepreneur successful. Topics include venture financing, business models, and interpersonal dynamics in the startup environment. Students meet before and after MS&E 472 to prepare for and debrief after the sessions. Enrollment limited to 60 students. Application available at first class session.
Terms: Aut, Win, Spr
| Units: 2
| Repeatable
for credit
MS&E 180:
Organizations: Theory and Management
For undergraduates only; preference to MS&E majors. Classical and contemporary organization theory; the behavior of individuals, groups, and organizations. Limited enrollment. Students must attend and complete an application at the first class session.
Terms: Aut
| Units: 4
MS&E 181:
Issues in Technology and Work
How changes in technology and organization are altering work and lives, and how understanding work and work practices can help design better technologies and organizations. Topics include job and organization design; collaboration and networking tools; distributed and virtual organizations; project work, taskification, and the platform economy; the blurring of boundaries between work and private life; monitoring and surveillance in the workplace; trends in skill requirements and occupational structures; downsizing and its effects on work systems; the growth of contingent employment, telecommuting, and the changing nature of labor relations. Limited enrollment.
Last offered: Winter 2017
| Units: 3
| UG Reqs: WAY-SI
MS&E 183:
Leadership in Action
Leadership in action is designed with a significant lab component in which students will be working on leadership projects throughout the quarter. The projects will provide students with hands on experience trying out new leadership behaviors in a variety of situations, along with the opportunity to reflect on these experience and, in turn, expand their leadership skills. Limited enrollment. Students must attend first class session.
Terms: Aut
| Units: 4
MS&E 184:
Future of Work: Issues in Organizational Learning and Design
The nature of work is changing, with consequences for how we structure jobs, careers, teams, organizations, and labor markets. This class teaches analytical tools from organizational behavior, social psychology, and socially distributed cognition to empower students to analyze and understand the changes and their consequences. Enrollment Limited. Prerequisite: 180.
Terms: Win
| Units: 4
MS&E 185:
Global Work
Issues, challenges, and opportunities facing workers, teams, and organizations working across national boundaries. Topics include geographic distance, time zones, language and cultural differences, technologies to support distant collaboration, team dynamics, and corporate strategy. Limited enrollment. Recommended: 180.
Terms: Spr
| Units: 4
MS&E 188:
Organizing for Good
Grand challenges of our time will demand entirely new ways of thinking about when, how, and under what conditions organizations are "doing good" and what effects that has. This class will focus on the role of organizations in society, the challenges organizations face in attempting to "do good", limitations to current ways of organizing, and alternative ways to organize and lead organizations that are "good".
Terms: Spr
| Units: 4
MS&E 190:
Methods and Models for Policy and Strategy Analysis
Guest lectures by departmental practitioners. Emphasis is on links among theory, application, and observation. Environmental, national security, and health policy; marketing, new technology, and new business strategy analyses. Comparisons between domains and methods.
Terms: Spr
| Units: 3
MS&E 193:
Technology and National Security (GS 167, GS 267, MS&E 293)
Explores the relation between technology, war, and national security policy from early history to modern day, focusing on current U.S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges. Topics include the interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U.S.; and the U.S. response and adaptation to new technologies of military significance.
Terms: Spr
| Units: 3
| UG Reqs: WAY-SI
MS&E 201:
Dynamic Systems
Provides a solid foundation in understanding and modeling the dynamics of change. Differential equations are used as a mathematical language to facilitate discussions on dynamic phenomena. Develop mathematical tools to analyze the dynamic models, and use such tools to think about and manage the dynamics of change. The analytical part of the course will be on mathematical analysis of linear and nonlinear dynamic systems. The notions of equilibrium, stability, growth and limit cycle will be introduced and discussed in terms of examples in business competition, organizational hierarchy, population dynamics, social interactions, ecology and spread of epidemics. Introduction to Catastrophe Theory, which provides a mathematical model for certain discontinuous phenomena like the crash of the stock market and the extinction of species. Required project in dynamic system modeling. Prerequisite: Math 104 or equivalent.
Terms: Win
| Units: 3-4
MS&E 202:
Optimal Control of Dynamic Systems
Provide a solid foundation in optimal control theory that can be applied to various disciplines. Topics covered are Maximum Principle, Dynamic Programming, Riccati Equation, Infinite time Optimal Control with time discounting, Infinite time differential games. The theory will be applied to optimal economic growth, resource management, production planning, dynamic pricing, financial planning, oligopoly competition, network platform competition. Prerequisite: MS&E 201.
Terms: Spr
| Units: 3
MS&E 208A:
Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
MS&E 208B:
Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
MS&E 208C:
Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
MS&E 208D:
Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
MS&E 208E:
Part-Time Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Course may be repeated for credit. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
15 times
(up to 15 units total)
MS&E 211:
Introduction to Optimization (ENGR 62, MS&E 111)
Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.
Terms: Spr
| Units: 3-4
MS&E 211X:
Introduction to Optimization (Accelerated) (ENGR 62X, MS&E 111X)
Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.
Terms: Aut, Win
| Units: 3-4
MS&E 212:
Mathematical Programming and Combinatorial Optimization (MS&E 112)
Combinatorial and mathematical programming (integer and non-linear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NP-completeness. Hands-on exercises. Prerequisites: 111 or MATH 103, CS 106A or X.
Last offered: Winter 2017
| Units: 3
MS&E 213:
Introduction to Optimization Theory (CS 269O)
Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.
Terms: Spr
| Units: 3
MS&E 220:
Probabilistic Analysis
Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: multivariable calculus and linear algebra. Recommended: knowledge of spreadsheets.
Terms: Aut, Sum
| Units: 3-4
MS&E 221:
Stochastic Modeling
Focus is on time-dependent random phenomena. Topics: discrete and continuous time Markov chains, renewal processes, queueing theory, and applications. Emphasis is on building a framework to formulate and analyze probabilistic systems. Prerequisite: 220 or equivalent, or consent of instructor.
Terms: Win
| Units: 3
MS&E 223:
Simulation
Discrete-event systems, generation of uniform and non-uniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiency-improvement techniques, decision making using simulation, applications to systems in computer science, engineering, finance, and operations research. Prerequisites: working knowledge of a programming language such as C, C++, Java, Python, or FORTRAN; calculus-base probability; and basic statistical methods.
Terms: Spr
| Units: 3
MS&E 226:
"Small" Data
This course is about understanding "small data": these are datasets that allow interaction, visualization, exploration, and analysis on a local machine. The material provides an introduction to applied data analysis, with an emphasis on providing a conceptual framework for thinking about data from both statistical and machine learning perspectives. Topics will be drawn from the following list, depending on time constraints and class interest: approaches to data analysis: statistics (frequentist, Bayesian) and machine learning; binary classification; regression; bootstrapping; causal inference and experimental design; multiple hypothesis testing. Class lectures will be supplemented by data-driven problem sets and a project. Prerequisites: CME 100 or MATH 51; 120, 220 or STATS 116; experience with R at the level of CME/STATS 195 or equivalent.
Terms: Aut
| Units: 3
MS&E 231:
Introduction to Computational Social Science (SOC 278)
With a vast amount of data now collected on our online and offline actions -- from what we buy, to where we travel, to who we interact with -- we have an unprecedented opportunity to study complex social systems. This opportunity, however, comes with scientific, engineering, and ethical challenges. In this hands-on course, we develop ideas from computer science and statistics to address problems in sociology, economics, political science, and beyond. We cover techniques for collecting and parsing data, methods for large-scale machine learning, and principles for effectively communicating results. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas. Prerequisites: introductory course in applied statistics, and experience coding in R, Python, or another high-level language.
Terms: Aut
| Units: 3
MS&E 233:
Networked Markets
An introduction to economic analysis for modern online services and systems. Topics include: Examples of networked markets. Online advertising. Recommendation and reputation systems. Pricing digital media. Network effects and network externalities. Social learning and herd behavior. Markets and information. Prerequisites: CME 100 or Math 51, and probability at the level of MS&E 220 or equivalent. No prior economics background will be assumed; requisite concepts will be introduced as needed.
Last offered: Spring 2014
| Units: 3
MS&E 234:
Data Privacy and Ethics
This course engages with difficult ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy will raise both practical and theoretical considerations. As part of the module on experimentation, students will be required to complete the Stanford IRB training for social and behavioral research. The course will assume a strong familiarity with the practice of machine learning and and data science. Recommended: MS&E 226, MS&E 231, CS 229, or equivalents.
Terms: Spr
| Units: 3
MS&E 235:
Network Analytics
Explores the underlying network structure of social, economic, and technological world using techniques from graph theory and economics, as well as machine learning and data analysis. Prerequisite: 226, CME 195, or equivalents.
Terms: Win
| Units: 3
MS&E 238:
Leading Trends in Information Technology
Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.
Terms: Sum
| Units: 3
MS&E 238A:
Leading Trends in Information Technology
Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.
Terms: Sum
| Units: 1
MS&E 240:
Accounting for Managers and Entrepreneurs (MS&E 140)
Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career. Enrollment limited. Admission by order of enrollment.
Terms: Win, Sum
| Units: 3-4
MS&E 241:
Economic Analysis
Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. Recommended: 211, ECON 50.
Terms: Win
| Units: 3-4
MS&E 243:
Energy and Environmental Policy Analysis
Concepts, methods, and applications. Energy/environmental policy issues such as automobile fuel economy regulation, global climate change, research and development policy, and environmental benefit assessment. Group project. Prerequisite: MS&E 241 or ECON 50, 51.
Terms: Spr
| Units: 3
MS&E 244:
Economic Growth and Development
Formerly 249. What generates economic growth. Emphasis is on theory accompanied by intuition, illustrated with country cases. Topics: the equation of motion of an economy; optimal growth theory; calculus of variations and optimal control approaches; deriving the Euler and Pontriaguine equations from economic reasoning. Applications: former planned economies in Russia and E. Europe; the present global crisis: causes and consequences; a comparative study of India and China. The links between economic growth and civilization; the causes of the rise and decline of civilizations; lessons for the future. Intended for graduate students. Prerequisite: multivariate calculus and permission of instructor. To receive permission, submit an application at http://web.stanford.edu/~lcottle/forms/244app.fb
Last offered: Summer 2017
| Units: 3
MS&E 245A:
Investment Science
Basic concepts of modern quantitative finance and investments. Focus is on the financial theory and empirical evidence that are useful for investment decisions. Topics: basic interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility and value at risk; designing optimal portfolios; risk-return tradeoff: capital asset pricing model and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisite: basic preparation in probability, statistics, and optimization.
Terms: Aut
| Units: 3-4
MS&E 245B:
Advanced Investment Science
Formerly MS&E 342. Topics: forwards and futures contracts, continuous and discrete time models of stock price behavior, geometric Brownian motion, Ito's lemma, basic options theory, Black-Scholes equation, advanced options techniques, models and applications of stochastic interest rate processes, and optimal portfolio growth. Computational issues and general theory. Teams work on independent projects. Prerequisite: 245A.
Terms: Win
| Units: 3
MS&E 246:
Financial Risk Analytics
Practical introduction to financial risk analytics. The focus is on data-driven modeling, computation, and statistical estimation of credit and market risks. Case studies based on real data will be emphasized throughout the course. Topics include mortgage risk, asset-backed securities, commercial lending, consumer delinquencies, online lending, derivatives risk. Tools from machine learning and statistics will be developed. Data sources will be discussed. The course is intended to enable students to design and implement risk analytics tools in practice. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Matlab, or similar computational/statistical package.
Terms: Win
| Units: 3
MS&E 250A:
Engineering Risk Analysis
The techniques of analysis of engineering systems for risk management decisions involving trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis (fault trees, event trees, systems dynamics); economic analysis of failure consequences (human safety and long-term economic discounting); and case studies such as space systems, nuclear power plants, and medical systems. Public and private sectors. Prerequisites: probability, decision analysis, stochastic processes, and convex optimization.
Terms: Win
| Units: 3
MS&E 250B:
Project Course in Engineering Risk Analysis
Students, individually or in groups, choose, define, formulate, and resolve a real risk management problem, preferably from a local firm or institution. Oral presentation and report required. Scope of the project is adapted to the number of students involved. Three phases: risk assessment, communication, and management. Emphasis is on the use of probability for the treatment of uncertainties and sensitivity to problem boundaries. Limited enrollment. Prerequisites: MS&E 250A and consent of instructor.
Terms: Spr
| Units: 3
MS&E 251:
Introduction to Stochastic Control with Applications (EE 266)
Focuses on conceptual foundation and algorithmic methodology of Dynamic Programming and Stochastic Control with applications to engineering, operations research, management science and other fields. Elaborates on the concept of probing, learning and control of stochastic systems, and addresses the practical application of the concept and methodology through the use of approximations. Prerequisites: 201, 221, or equivalents.
Terms: Spr
| Units: 3
MS&E 252:
Decision Analysis I: Foundations of Decision Analysis
Coherent approach to decision making, using the metaphor of developing a structured conversation having desirable properties, and producing actional thought that leads to clarity of action. Socratic instruction; computational problem sessions. Emphasis is on creation of distinctions, representation of uncertainty by probability, development of alternatives, specification of preference, and the role of these elements in creating a normative approach to decisions. Information gathering opportunities in terms of a value measure. Relevance and decision diagrams to represent inference and decision. Principles are applied to decisions in business, technology, law, and medicine. See 352 for continuation.
Terms: Aut
| Units: 3-4
MS&E 254:
The Ethical Analyst
The ethical responsibility for consequences of professional analysts who use technical knowledge in support of any individual, organization, or government. The means to form ethical judgments; questioning the desirability of physical coercion and deception as a means to reach any end. Human action and relations in society in the light of previous thought, and research on the desired form of social interactions. Attitudes toward ethical dilemmas through an explicit personal code.
Terms: Spr
| Units: 1-3
MS&E 256:
Technology Assessment and Regulation of Medical Devices (BIOE 256)
Regulatory approval and reimbursement for new health technologies are critical success factors for product commercialization. This course explores the regulatory and payer environment in the U.S. and abroad, as well as common methods of health technology assessment. Students will learn frameworks to identify factors relevant to the adoption of new health technologies, and the management of those factors in the design and development phases of bringing a product to market through case studies, guest speakers from government (FDA) and industry, and a course project.
Terms: Spr
| Units: 3
MS&E 256A:
Technology Assessment and Regulation of Medical Devices
Regulatory approval and reimbursement for new medical technologies as a key component of product commercialization. The regulatory and payer environment in the U.S. and abroad, and common methods of health technology assessment. Framework to identify factors relevant to adoption of new medical devices, and the management of those factors in the design and development phases. Case studies; guest speakers from government (FDA) and industry.
Terms: Spr
| Units: 1
MS&E 260:
Introduction to Operations Management
Operations management focuses on the effective planning, scheduling, and control of manufacturing and service entities. This course introduces students to a broad range of key issues in operations management. Topics include determination of optimal facility location, production planning, optimal timing and sizing of capacity expansion, and inventory control. Prerequisites: basic knowledge of Excel spreadsheets, probability.
Terms: Aut, Sum
| Units: 3
MS&E 262:
Supply Chain Management
Definition of a supply chain; coordination difficulties; pitfalls and opportunities in supply chain management; inventory/service tradeoffs; performance measurement and incentives. Global supply chain management; mass customization; supplier management. Design and redesign of products and processes for supply chain management; tools for analysis; industrial applications; current industry initiatives. Enrollment limited to 50. Admission determined in the first class meeting. Recommended: 260 or 261.
Last offered: Spring 2017
| Units: 3
MS&E 263:
Healthcare Operations Management
With healthcare spending in the US exceeding 17% of GDP and growing, improvements in the quality and efficiency of healthcare services are urgently needed. This class focuses on the use of analytical tools to support efficient and effective delivery of health care. Topics include quality control and management, capacity planning, resource allocation, management of patient flows, and scheduling. Prerequisites: basic knowledge of Excel spreadsheets, probability, and optimization.
Terms: Win
| Units: 3
MS&E 265:
Product Management Fundamentals
Introduction to Product Management (PM). PM's define a product's functional requirements and lead cross functional teams responsible for development, launch, and ongoing improvement. The course uses a learning-by-doing approach covering the following topics: changing role of a PM at different stages of the product life cycle; techniques to understand customer needs and validate demand; user experience design and testing; role of detailed product specifications; waterfall and agile methods of software development. Group projects involve the specification of a software technology product though the skills taught are useful for a variety of product roles. No prior knowledge of design, engineering, or computer science required. Limited enrollment. Application deadline March 15: https://goo.gl/HoiXXk. Syllabus: https://goo.gl/TWPFfU.
Terms: Spr
| Units: 3
MS&E 267:
Service Operations and the Design of Marketplaces
The service sector accounts for approximately 80% of GDP and employment in the US. It is therefore imperative to develop efficient and effective operations of services. The management of service operations can require quite different constraints and objectives than manufacturing operations. The course examines both traditional and new approaches for achieving operational competitiveness in service businesses including (online) marketplaces. Topics include the service concept and operations strategy, the design of effective service delivery systems, capacity management, queuing, quality, revenue management as well as concepts from the design of marketplaces such as matching, congestion and auctions.
Terms: Win
| Units: 3
MS&E 270:
Strategy in Technology-Based Companies
For graduate students only. Introduction to the basic concepts of strategy, with emphasis on high technology firms. Topics: competitive positioning, resource-based perspectives, co-opetition and standards setting, and complexity/evolutionary perspectives. Limited enrollment. Students must attendnand complete an application at the first class session.
Terms: Aut
| Units: 3-4
MS&E 271:
Global Entrepreneurial Marketing
Skills needed to market new technology-based products to customers around the world. Case method discussions. Cases include startups and global high tech firms. Course themes: marketing toolkit, targeting markets and customers, product marketing and management, partners and distribution, sales and negotiation, and outbound marketing. Team-based take-home final exam. Limited enrollment.
Terms: Win
| Units: 3-4
MS&E 272:
Entrepreneurship without Borders
How do you create a start-up outside of the U.S.? What are the unique issues involved in creating a successful startup in emerging economies such as China or India? What is entrepreneurial leadership in a venture that spans country borders? Is Silicon Valley-style entrepreneurship possible in other places? How does an entrepreneur act differently when creating a company in a less-developed institutional environment? Learn through forming teams, a mentor-guided startup project focused on developing students' startups in international markets, case studies, research on the international aspects of the entrepreneurial process, and networking with top entrepreneurs and venture capitalists who work across borders. For graduate students only, with a preference for engineering and science majors who seek to understand the formation of high-impact start-ups in emerging economy contexts. Limited enrollment.
Terms: Win
| Units: 3-4
MS&E 273:
Technology Venture Formation
Open to graduate students interested in technology driven start-ups. Provides the experience of an early-stage entrepreneur seeking initial investment, including: team building, opportunity assessment, customer development, go-to-market strategy, and IP. Teaching team includes serial entrepreneurs and venture capitalists. Student teams validate the business model using R&D plans and financial projections, and define milestones for raising and using venture capital. Final exam is an investment pitch delivered to a panel of top tier VC partners. In addition to lectures, teams interact with mentors and teaching team weekly. Enrollment by application: http://www.stanford.edu/class/msande273. Recommended: 270, 271, or equivalent.
Terms: Aut
| Units: 3-4
MS&E 274:
Dynamic Entrepreneurial Strategy
Primarily for graduate students. How entrepreneurial strategy focuses on creating structural change or responding to change induced externally. Grabber-holder dynamics as an analytical framework for developing entrepreneurial strategy to increase success in creating and shaping the diffusion of new technology or product innovation dynamics. Topics: First mover versus follower advantage in an emerging market; latecomer advantage and strategy in a mature market; strategy to break through stagnation; and strategy to turn danger into opportunity. Modeling, case studies, and term project.
Terms: Win
| Units: 3
MS&E 275:
Foundations for Large-Scale Entrepreneurship
Explore the foundational and strategic elements needed for startups to be designed for "venture scale" at inception. Themes include controversial and disruptive insights, competitive analysis, network effects, organizational design, and capital deployment. Case studies, expert guests, and experiential learning projects will be used. Primarily for graduate students. Limited enrollment. Admission by application. Recommended: basic accounting.
Terms: Spr
| Units: 3
MS&E 276:
Entrepreneurial Management and Finance
For graduate students only, with a preference for engineering and science majors. Emphasis on managing high-growth, early-stage enterprises, especially those with innovation-based products and services. Students work in teams to develop skills and approaches necessary to becoming effective entrepreneurial leaders and managers. Topics include assessing risk, understanding business models, analyzing key operational metrics, modeling cash flow and capital requirements, evaluating sources of financing, structuring and negotiating investments, managing organizational culture and incentives, managing the interplay between ownership and growth, and handling adversity and failure. Limited enrollment. Admission by application. Prerequisite: basic accounting.
Terms: Spr
| Units: 3
MS&E 277:
Creativity and Innovation
Experiential course explores factors that promote and inhibit creativity and innovation in individuals, teams, and organizations. Teaches creativity tools using workshops, case studies, field trips, expert guests, and team design challenges. Enrollment limited to 40. Admission by application. See http://dschool.stanford.edu/classes.
Terms: Aut
| Units: 3-4
MS&E 278:
Patent Law and Strategy for Innovators and Entrepreneurs (ME 208)
This course teaches the essentials for a startup to build a valuable patent portfolio and avoid a patent infringement lawsuit. Jeffrey Schox, who is the top recommended patent attorney for Y Combinator, built the patent portfolio for Twilio (IPO), Cruise ($1B acquisition), and 250 startups that have collectively raised over $2B in venture capital. This course is equally applicable to EE, CS, and Bioengineering students. For those students who are interested in a career in Patent Law, please note that this course is a prerequisite for ME238 Patent Prosecution.
Terms: Aut
| Units: 2-3
MS&E 280:
Organizational Behavior: Evidence in Action
Organization theory; concepts and functions of management; behavior of the individual, work group, and organization. Emphasis is on cases and related discussion. Enrollment limited.
Terms: Aut
| Units: 3-4
MS&E 282:
Transformational Leadership
The personal, team-based and organizational skills needed to become a transformative leader. Case method discussions and lectures. Themes include: personal transformation; the inside-out effect, positive intelligence, group transformation; cross-functional teams; re-engineering; rapid - non-profit and for profit - organizational transformation; and social transformation. Limited enrollment; preference to graduate students. Prerequisite: 180 or 280, or relevant work experience.
Terms: Spr
| Units: 3
MS&E 284:
Designing Modern Work Organizations
This practice-based experiential lab course is geared toward MS&E masters students. Students will master the concepts of organizational design, with an emphasis on applying them to modern challenges (technology, growth, globalization, and the modern workforce). Students will also gain mastery of skills necessary for success in today's workplace (working in teams, communicating verbally, presenting project work). Guest speakers from industry will present real-world challenges related to class concepts. Students will complete a quarter-long project designing and managing an actual online organization.
Terms: Win
| Units: 3
MS&E 292:
Health Policy Modeling
Primarily for master's students; also open to undergraduates and doctoral students. The application of mathematical, statistical, economic, and systems models to problems in health policy. Areas include: disease screening, prevention, and treatment; assessment of new technologies; bioterrorism response; and drug control policies.
Terms: Win
| Units: 3
MS&E 293:
Technology and National Security (GS 167, GS 267, MS&E 193)
Explores the relation between technology, war, and national security policy from early history to modern day, focusing on current U.S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges. Topics include the interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U.S.; and the U.S. response and adaptation to new technologies of military significance.
Terms: Spr
| Units: 3
MS&E 294:
Systems Modeling for Climate Policy Analysis
Design and application of formal analytical methods in climate policy development. Emphasis on integrated use of modeling tools from diverse methodologies and application of these modeling tools towards policy-making. Students will work with one of several widely-used climate policy models for the course project. Issues addressed include model selection, instrument design, technology development, resource management, multiparty negotiation, and dealing with complexity and uncertainty. Links among art, theory, and practice. Prerequisites: 211, 241, 252, or equivalents, or permission of instructor.
Terms: Win
| Units: 3
MS&E 295:
Energy Policy Analysis
Design and application of formal analytical methods for policy and technology assessments of energy efficiency and renewable energy options. Emphasis is on integrated use of modeling tools from diverse methodologies and requirements for policy and corporate strategy development. Prerequisites: ECON 50, MS&E 211, MS&E 252, or equivalents, or permission of instructor.
Last offered: Winter 2017
| Units: 3
MS&E 297:
"Hacking for Defense": Solving National Security issues with the Lean Launchpad
In a crisis, national security initiatives move at the speed of a startup yet in peacetime they default to decades-long acquisition and procurement cycles. Startups operate with continual speed and urgency 24/7. Over the last few years they've learned how to be not only fast, but extremely efficient with resources and time using lean startup methodologies. In this class student teams will take actual national security problems and learn how to apply lean startup principles, ("business model canvas," "customer development," and "agile engineering) to discover and validate customer needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with actual military, Department of Defense and other government agency end-users. Team applications required in February, see hacking4defense.stanford.edu. Limited enrollment. Course builds on concepts introduced in MS&E 477.
Terms: Spr
| Units: 3-4
MS&E 298:
Hacking for Diplomacy: Tackling Foreign Policy Challenges with the Lean Launchpad (IPS 232)
At a time of significant global uncertainty, diplomats are grappling with transnational and cross-cutting challenges that defy easy solution including: the continued pursuit of weapons of mass destruction by states and non-state groups, the outbreak of internal conflict across the Middle East and in parts of Africa, the most significant flow of refugees since World War II, and a changing climate that is beginning to have impacts on both developed and developing countries. While the traditional tools of statecraft remain relevant, policymakers are looking to harness the power of new technologies to rethink how the U.S. government approaches and responds to these and other long-standing challenges. In this class, student teams will take actual foreign policy challenges and learn how to apply lean startup principles, ("mission model canvas," "customer development," and "agile engineering¿) to discover and validate agency and user needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with officials in the U.S. State Department and other civilian agencies. Team applications required at the end of shopping period. Limited enrollment.
Last offered: Autumn 2016
| Units: 3-4
MS&E 299:
Voluntary Social Systems
Ethical theory, feasibility, and desirability of a social order in which coercion by individuals and government is minimized and people pursue ends on a voluntary basis. Topics: efficacy and ethics; use rights for property; contracts and torts; spontaneous order and free markets; crime and punishment based on restitution; guardian-ward theory for dealing with incompetents; the effects of state action-hypothesis of reverse results; applications to help the needy, armed intervention, victimless crimes, and environmental protection; transition strategies to a voluntary society.
Terms: Win
| Units: 1-3
MS&E 300:
Ph.D. Qualifying Tutorial or Paper
Restricted to Ph.D. students assigned tutorials as part of the MS&E Ph.D. qualifying process. Enrollment optional.
Terms: Aut, Win, Spr, Sum
| Units: 1-3
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Chiu, S. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Goel, S. (PI);
Hausman, W. (PI);
Hinds, P. (PI);
Howard, R. (PI);
Johari, R. (PI);
Katila, R. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Shachter, R. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Tse, E. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
MS&E 301:
Dissertation Research
Prerequisite: doctoral candidacy.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Barley, S. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Chiu, S. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Goel, S. (PI);
Hausman, W. (PI);
Hinds, P. (PI);
Howard, R. (PI);
Johari, R. (PI);
Katila, R. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Saunders, M. (PI);
Shachter, R. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Tse, E. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
MS&E 302:
Fundamental Concepts in Management Science and Engineering
Each course session will be devoted to a specific MS&E PhD research area. Advanced students will make presentations designed for first-year doctoral students regardless of area. The presentations will be devoted to: illuminating how people in the area being explored that day think about and approach problems, and illustrating what can and cannot be done when addressing problems by deploying the knowledge, perspectives, and skills acquired by those who specialize in the area in question. Area faculty will attend and participate. During the last two weeks of the quarter groups of first year students will make presentations on how they would approach a problem drawing on two or more of the perspectives to which they have been exposed earlier in the class. Attendance is mandatory and performance will be assessed on the basis of the quality of the students¿ presentations and class participation. Restricted to first year MS&E PhD students.
Terms: Aut
| Units: 1
MS&E 310:
Linear Programming
Formulation of standard linear programming models. Theory of polyhedral convex sets, linear inequalities, alternative theorems, and duality. Variants of the simplex method and the state of art interior-point algorithms. Sensitivity analyses, economic interpretations, and primal-dual methods. Relaxations of harder optimization problems and recent convex conic linear programs. Applications include game equilibrium facility location. Prerequisite: MATH 113 or consent of instructor.
Terms: Aut
| Units: 3
MS&E 311:
Optimization (CME 307)
Applications, theories, and algorithms for finite-dimensional linear and nonlinear optimization problems with continuous variables. Elements of convex analysis, first- and second-order optimality conditions, sensitivity and duality. Algorithms for unconstrained optimization, and linearly and nonlinearly constrained problems. Modern applications in communication, game theory, auction, and economics. Prerequisites: MATH 113, 115, or equivalent.
Terms: Win
| Units: 3
MS&E 312:
Advanced Methods in Numerical Optimization (CME 334)
Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.
Last offered: Autumn 2015
| Units: 3
| Repeatable
for credit
MS&E 313:
Almost Linear Time Graph Algorithms (CS 269G)
Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.
Last offered: Winter 2017
| Units: 3
MS&E 314:
Linear and Conic Optimization with Applications (CME 336)
Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.
Last offered: Winter 2015
| Units: 3
MS&E 316:
Discrete Mathematics and Algorithms (CME 305)
Topics: Basic Algebraic Graph Theory, Matroids and Minimum Spanning Trees, Submodularity and Maximum Flow, NP-Hardness, Approximation Algorithms, Randomized Algorithms, The Probabilistic Method, and Spectral Sparsification using Effective Resistances. Topics will be illustrated with applications from Distributed Computing, Machine Learning, and large-scale Optimization. Prerequisites: CS 261 is highly recommended, although not required.
Terms: Win
| Units: 3
MS&E 317:
Algorithms for Modern Data Models (CS 263)
We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of CS 261.
Last offered: Spring 2015
| Units: 3
MS&E 318:
Large-Scale Numerical Optimization (CME 338)
The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basis factorization and updates. Interior methods. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335.
Terms: Spr
| Units: 3
MS&E 319:
Approximation Algorithms
Combinatorial and mathematical programming techniques to derive approximation algorithms for NP-hard optimization problems. Prossible topics include: greedy algorithms for vertex/set cover; rounding LP relaxations of integer programs; primal-dual algorithms; semidefinite relaxations. May be repeated for credit. Prerequisites: 112 or CS 161.
Last offered: Autumn 2016
| Units: 3
| Repeatable
for credit
MS&E 321:
Stochastic Systems
Topics in stochastic processes, emphasizing applications. Markov chains in discrete and continuous time; Markov processes in general state space; Lyapunov functions; regenerative process theory; renewal theory; martingales, Brownian motion, and diffusion processes. Application to queueing theory, storage theory, reliability, and finance. Prerequisites: 221 or STATS 217; MATH 113, 115. (Glynn)
Terms: Spr
| Units: 3
MS&E 322:
Stochastic Calculus and Control
Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the Hamilton-Jacobi-Bellman equation, and statistical inference of SDEs. Applications to finance and queueing theory. Prerequisites: 221 or STATS 217: MATH 113, 115.
Last offered: Spring 2017
| Units: 3
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:
Advanced Topics in Applied Probability
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.
Terms: Win
| Units: 3
MS&E 326:
Advanced Topics in Game Theory with Engineering Applications
Advanced Topics in Game Theory with Engineering Applications
Terms: Spr
| Units: 3
| Repeatable
for credit
(up to 99 units total)
MS&E 330:
Law, Order & Algorithms (CSRE 230, SOC 279)
Data and algorithms are transforming law enforcement and criminal justice, a shift that is ripe for rigorous empirical and narrative exploration. This class is centered around several data-driven projects in criminal justice, with the goal of fostering greater understanding, transparency, and public accountability. Students work in interdisciplinary teams, using a combination of statistical and journalistic methods. Some of the work may be published by news organizations or may be used to advance data journalism investigations. Students with a background in statistics, computer science, law, public policy or journalism are encouraged to participate. Enrollment is limited, and project teams will be selected during the first week of class.
Terms: Spr
| Units: 3
MS&E 332:
Topics in Social Algorithms
In depth discussion of selected research topics in social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social computing, and social choice theory. The class will include a theoretical project and a paper presentation. Prerequisites: CS 261 or equivalent; understanding of basic game theory.
Last offered: Winter 2016
| Units: 3
MS&E 333:
Social Algorithms
This seminar will introduce students to research in the field of social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social choice theory, and models of influence and contagion.
Last offered: Spring 2015
| Units: 1
MS&E 334:
Topics in Social Data
This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. There will be a particular focus on recent developments in discrete choice theory and preference learning. Connections will be made to graph-theoretic investigations common in the study of social networks. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students with an interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.
Terms: Aut
| Units: 3
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.
Last offered: Autumn 2015
| Units: 3
MS&E 336:
Platform and Marketplace Design
The last decade has witnessed a meteoric rise in the number of online markets and platforms competing with traditional mechanisms of trade. Examples of such markets include online marketplaces for goods, such as eBay; online dating markets; markets for shared resources, such as Lyft, Uber, and Airbnb; and online labor markets. We will review recent research that aims to both understand and design such markets. Emphasis on mathematical modeling and methodology, with a view towards preparing Ph.D. students for research in this area. Prerequisites: Mathematical maturity; 300-level background in optimization and probability; prior exposure to game theory.
Last offered: Winter 2015
| Units: 3
| Repeatable
for credit
MS&E 338:
Reinforcement Learning
Advanced material in this area is sometimes taught for the first time as a topics course. Prerequisite: consent of instructor.
Terms: Spr
| Units: 3
MS&E 347:
Credit Risk: Modeling and Management
Credit risk modeling, valuation, and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Point processes and their compensators. Structural, incomplete information and reduced form approaches. Single name products: corporate bonds, equity, equity options, credit and equity default swaps, forwards and swaptions. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Implementation, calibration and testing of models. Industry and market practice. Data and implementation driven group projects that focus on problems in the financial industry.
Terms: Win
| Units: 3
MS&E 348:
Optimization of Uncertainty and Applications in Finance
How to make optimal decisions in the presence of uncertainty, solution techniques for large-scale systems resulting from decision problems under uncertainty, and applications in finance. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Projects involving the practical application of optimization under uncertainty to financial planning.
Terms: Win
| Units: 3
MS&E 349:
Financial Statistics
Topics in financial statistics with focus on current research: Time-series modeling, volatility modeling, high-frequency statistics, large-dimensional factor modeling and estimation of continuous-time processes. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents.
Terms: Spr
| Units: 3
MS&E 350:
Doctoral Seminar in Risk Analysis
Limited to doctoral students. Literature in the fields of engineering risk assessment and management. New methods and topics, emphasizing probabilistic methods and decision analysis. Applications to risk management problems involving the technical, economic, and organizational aspects of engineering system safety. Possible topics: treatment of uncertainties, learning from near misses, and use of expert opinions.
Last offered: Spring 2005
| Units: 3
| Repeatable
for credit
MS&E 351:
Dynamic Programming and Stochastic Control
Markov population decision chains in discrete and continuous time. Risk posture. Present value and Cesaro overtaking optimality. Optimal stopping. Successive approximation, policy improvement, and linear programming methods. Team decisions and stochastic programs; quadratic costs and certainty equivalents. Maximum principle. Controlled diffusions. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. MATLAB. Prerequisites: MATH 113, 115; Markov chains; linear programming.
Last offered: Autumn 2016
| Units: 3
MS&E 352:
Decision Analysis II: Professional Decision Analysis
How to organize the decision conversation, the role of the decision analysis cycle and the model sequence, assessing the quality of decisions, framing decisions, the decision hierarchy, strategy tables for alternative development, creating spare and effective decision diagrams, biases in assessment, knowledge maps, uncertainty about probability. Sensitivity analysis, approximations, value of revelation, joint information, options, flexibility, bidding, assessing and using corporate risk attitude, risk sharing and scaling, and decisions involving health and safety. See 353 for continuation. Prerequisite: 252.
Terms: Win
| Units: 3-4
MS&E 353:
Decision Analysis III: Frontiers of Decision Analysis
The concept of decision composite; probabilistic insurance and other challenges to the normative approach; the relationship of decision analysis to classical inference and data analysis procedures; the likelihood and exchangeability principles; inference, decision, and experimentation using conjugate distributions; developing a risk attitude based on general properties; alternative decision aiding practices such as analytic hierarchy and fuzzy approaches. Student presentations on current research. Goal is to prepare doctoral students for research. Prerequisite: 352.
Terms: Spr
| Units: 3
MS&E 355:
Influence Diagrams and Probabilistics Networks
Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulation. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Prerequisites: 220, 252, or equivalents, or consent of instructor.
Terms: Win
| Units: 3
MS&E 365:
Advanced Topics in Market Design
Primarily for doctoral students. Focus on quantitative models dealing with sustainability and related to operations management. Prerequisite: consent of instructor. May be repeated for credit.
Terms: Spr
| Units: 3
| Repeatable
for credit
MS&E 371:
Innovation and Strategic Change
Doctoral research seminar, limited to Ph.D. students. Current research on innovation strategy. Topics: scientific discovery, innovation search, organizational learning, evolutionary approaches, and incremental and radical change. Topics change yearly. Recommended: course in statistics or research methods.
Terms: Win
| Units: 2-3
| Repeatable
for credit
MS&E 372:
Entrepreneurship Doctoral Research Seminar
Classic and current research on entrepreneurship. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor.
Terms: Spr
| Units: 1-3
MS&E 374:
Cross Border Regional Innovation
This is an advanced research seminar class that is restricted to students that had taken MS&E 274. Disruptive innovation is the realization of new value proposition through establishment of a new ecosystem. Value proposition depends on the culture and social value in a particular region; while the ability to establish the ecosystem to realize the value proposition is highly dependent on the firm¿s knowledge and skills to operate effectively under the political, social, and economic structure of that particular region. Therefore cross border and regional innovations in different regions will take different path. This course will examine cases that cover innovations in developing economy, cross border e-commerce, and international business groups.
Last offered: Spring 2016
| Units: 3
MS&E 376:
Strategy Doctoral Research Seminar
Classic and current research on business and corporate strategy. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor. Course may be repeated for credit.
Last offered: Autumn 2016
| Units: 3
| Repeatable
for credit
MS&E 379:
Social Data Analysis
Applied introduction to good empirical research and causal inference for social scientists and others analyzing social data. Designed to provide an introduction to some of the most commonly used quantitative techniques for causal inference in social data including: survey design and inference, regression and propensity score matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and the analysis of big data. Applications: organizations, entrepreneurship, public policy, innovation, economics, online education, visual representations, communication, critique and design of figures, graphs. Does not explicitly cover social network structure or machine learning as these topics are well-covered elsewhere. Students work in groups and individually to design and carry out a small research project based on the use of analytics, large data sets, or other digital innovations related to business or other organizations. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects. Course prioritizes a thorough substantively grounded understanding of assumptions over mathematical proofs and derivations. Aimed at PhD students, but open by permission to Master's students and to students in other Stanford programs with relevant coursework or experience in analytics and statistics.
Last offered: Spring 2017
| Units: 3
MS&E 380:
Doctoral Research Seminar in Organizations
Limited to Ph.D. students. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.
Last offered: Autumn 2013
| Units: 3
| Repeatable
for credit
MS&E 381:
Research in Work, Technology, and Organization
Enrollment limited to Ph.D. students. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.
Terms: Spr
| Units: 2-3
| Repeatable
for credit
MS&E 383:
Doctoral Seminar on Ethnographic Research
For graduate students; upper-level undergraduates with consent of instructor. Interviewing and participant observation. Techniques for taking, managing, and analyzing field notes and other qualitative data. Methods texts and ethnographies offer examples of how to analyze and communicate ethnographic data. Prerequisite: consent of instructor.
Last offered: Spring 2016
| Units: 3
MS&E 384:
Groups and Teams
Research on groups and teams in organizations from the perspective of organizational behavior and social psychology. Topics include group effectiveness, norms, group composition, diversity, conflict, group dynamics, temporal issues in groups, geographically distributed teams, and intergroup relations.
Terms: Win
| Units: 3
MS&E 387:
Design of Field Research Methods
Field research involves collecting original data (qualitative and/or quantitative) in field sites. This course combines informal lecture and discussion with practical exercises to build specific skills for conducting field research in organizations. Readings include books and papers about research methodology and articles that provide exemplars of field research. Specific topics covered include: the role of theory in field research, variance versus process models, collecting and analyzing different kinds of data (observation, interview, survey), levels of analysis, construct development and validity, blending qualitative and quantitative data (in a paper, a study, or a career), and writing up field research for publication. Students will develop intuition about the contingent relationship between the nature of the research question and the field research methods used to answer it as a foundation for conducting original field research.
Last offered: Spring 2016
| Units: 3
MS&E 388:
Contemporary Themes in Work and Organization Studies
Doctoral research seminar, limited to Ph.D. students. Current meso-level field research on organizational behavior, especially work and coordination. Topics: work design, job design, roles, teams, organizational change and learning, knowledge management, performance. Focus on understanding theory development and research design in contemporary field research. Topics change yearly. Recommended: course in statistics or research methods.
Last offered: Winter 2017
| Units: 3
MS&E 389:
Seminar on Organizational Theory (EDUC 375A, SOC 363A)
The social science literature on organizations assessed through consideration of the major theoretical traditions and lines of research predominant in the field.
Terms: Aut
| Units: 5
MS&E 390:
Doctoral Research Seminar in Health Systems Modeling
Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in health policy, health systems modeling, and health innovation. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1-3
| Repeatable
for credit
MS&E 391:
Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis
Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in energy and environmental modeling and analysis. Current emphasis on approaches to incorporation of uncertainty and technology dynamics into complex systems models. May be repeated for credit.
Terms: Spr
| Units: 1-3
| Repeatable
for credit
MS&E 403:
Integrative Modeling
Modeling approaches for examining real life problems: how to get started. Critical thinking in framing and problem formulation leading to actionable solutions and communication of results to decision makers. Models to identify and evaluate multiple objectives/metrics. Models examined include both deterministic and probabilistic components. Overview of optimization and probability, decomposition principles to model large scale problems, appropriate integration of uncertainties into model formulations. Primarily team-project based assignments, with three to four group projects. Project topics drawn from applications with real data. Sample project topics include: optimizing group phone plans for large corporations, life insurance business models, making sense of the health care debate, logistic decision problems. Project teams will critically grade other teams¿ project reports using provided guidelines. Project presentations throughout the quarter. Prerequisites: 211, 220.
Last offered: Winter 2016
| Units: 3
MS&E 408:
Directed Reading and Research
Directed reading and research on a subject of mutual interest to student and faculty member. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Byers, T. (PI);
Chiu, S. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Goel, S. (PI);
Hausman, W. (PI);
Hecker, S. (PI);
Hinds, P. (PI);
Howard, R. (PI);
Johari, R. (PI);
Katila, R. (PI);
McGinn, R. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Saunders, M. (PI);
Seelig, T. (PI);
Shachter, R. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Tse, E. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
MS&E 441:
Policy and Economics Research Roundtable (PERR)
Research in progress or contemplated in policy and economics areas. Emphasis depends on research interests of participants, but is likely to include energy, environment, transportation, or technology policy and analysis. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 442:
Energy Efficiency: Technology, Policy, and Investment (PUBLPOL 342)
Provides students with a basic understanding of the technologies, policies, and investments behind energy efficiency. Explores each of these dimensions, and their interplay, through structured lectures and expert perspectives from leading professionals and practitioners. The seminar covers the following energy efficiency topics: fundamental concepts; history and achievements; role of policy and new policy frameworks; investment, strategy and finance; evolving digital/analytical and platform tools; low income programs; international development; relationship between efficiency and climate change; energy efficiency and the changing grid; and new entrants and business models. Limited to 30 students. Prerequisite: at least one ¿ or equivalent ¿ of CEE 100, CEE 226A, CEE 107A/207A, ENVRES 280, LAW 2503, GSBGEN 532.
Terms: Spr
| Units: 1-2
MS&E 446A:
Mathematical and Computational Finance Seminar (CME 242, STATS 239)
May be repeat for credit.
Last offered: Spring 2017
| Units: 1
| Repeatable
for credit
MS&E 447:
Systemic and Market Risk : Notes on Recent History, Practice, and Policy
The global financial crisis of 2007-8 threw into sharp relief the ongoing challenges of understanding risk, the financial system, links with the global economy, and interactions with policy. We will explore elements of the crisis, a few other key events, and ongoing debates about systemic risk. Group projects will explore in more detail past events and current topics in systemic risk. Supplements a rigorous technical curriculum in modern finance with select aspects relevant to understanding the practice and broader context of modern financial activities such as derivatives, financial engineering, and risk management.
Terms: Aut
| Units: 3
MS&E 448:
Big Financial Data and Algorithmic Trading
Project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets. Due to the fact that it is near impossible to perform experiments in finance, there is a need for empirical inference and intuition, any model should also be justified in terms of plausibility that goes beyond pure econometric and data mining approaches. Introductory lectures, followed by real-world type projects to get a hands-on experience with realistic challenges and hone skills needed in the work place. Work in groups on selected projects that will entail obtaining and cleaning the raw data and becoming familiar with techniques and challenges in handling big data sets. Develop a framework for modeling and testing (in computer languages such as Python, C++ , Matlab and R) and prepare presentations to present to the class. Example projects include optimal order execution, developing a market making algorithm, design of an intra-day trading strategy, and modeling the dynamics of the bid and ask. Prerequisites: MS&E 211, 242, 342, or equivalents, some exposure to statistics and programming. Enrollment limited. Admission by application; details at first class.
Terms: Spr
| Units: 3
MS&E 449:
Buy-Side Investing
In-class lectures and guest speakers who work in the Buy-Side to explore the synergies amongst the various players¿ roles, risk appetites, and investment time and return horizons. We aim to see the forest and the different species of trees growing in the forest known as the Buy-Side, so as to develop a perspective as financial engineers for how the ecosystem functions, what risks it digests, how it generates capital at what rate and amount for the Sell-Side, and how impacts in the real economy are reflected - or should be reflected - in the culture and risk models adopted by the Buy-Side participants.
Terms: Win
| Units: 1-2
MS&E 450:
Lessons in Decision Making
Entrepreneurs, senior management consultants, and executives from Fortune 500 companies share real-world stories and insights from their experience in decision making.
Last offered: Spring 2016
| Units: 1
| Repeatable
for credit
MS&E 454:
Decision Analysis Seminar
Current research and related topics presented by doctoral students and invited speakers. May be repeated for credit. Prerequisite: 252.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 463:
Healthcare Systems Design
Students work on projects to analyze and design various aspects of healthcare including hospital patient flow, physician networks, clinical outcomes, reimbursement incentives, and community health. Students work in small teams under the supervision of the course instructor and partners at the Lucille Packard Children's Hospital, the Stanford Hospital, and other regional healthcare providers. Prerequisite: 263.
Terms: Spr
| Units: 3-4
MS&E 472:
Entrepreneurial Thought Leaders' Seminar
Entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. ETL speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks followed by half hour of class interaction. Required web discussion. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 475A:
Entrepreneurial Leadership
This seminar explores a wide range of topics related to entrepreneurial leadership through class discussions, case studies, field trips, and guest speakers. It is part of the DFJ Entrepreneurial Leaders Fellowship, which requires an application during Fall quarter. Details can be found at: http://stvp.stanford.edu/dfj/.
Terms: Win
| Units: 1
MS&E 475B:
Entrepreneurial Leadership
This seminar explores a wide range of topics related to entrepreneurial leadership through class discussions, case studies, field trips, and guest speakers. It is part of the DFJ Entrepreneurial Leaders Fellowship, which requires an application during Fall quarter. Details can be found at: http://stvp.stanford.edu/dfj/.
Terms: Spr
| Units: 1
MS&E 487:
D.ORG: PROTOTYPING ORGANIZATIONAL CHANGE
d.org will send outstanding, proven design thinkers into select organizations to architect "organizational R&D" experiments. Students will work directly with teams in those organizations to implement tools to drive change and measure results. Performance will be measured by the student's effectiveness at driving changes in the organization. Prerequisite: MS&E 489 and permission of instructor.
Last offered: Spring 2017
| Units: 2-4
MS&E 489:
d.Leadership: Design Leadership in Context (ME 368)
d.Leadership is a course that teaches the coaching and leadership skills needed to drive good design process in groups. d.leaders will work on real projects driving design projects within organizations and gain real world skills as they experiment with their leadership style. Take this course if you are inspired by past design classes and want skills to lead design projects beyond Stanford. Preference given to students who have taken other Design Group or d.school classes. Admission by application. See dschool.stanford.edu/classes for more information
Terms: Win
| Units: 4
MS&E 493:
Current issues in Technology and National Security
Probe deeply into both key regional security concerns and advanced technologies that shape today¿s headlines. Shaped by current events, regional focus areas include the Korean peninsula, the South and East China Seas, the Ukraine, and Iran. Technologies explored include missile defense systems, national security space protection and defense, hypersonic vehicles, nuclear stockpile and deterrence concepts, drone and autonomous platforms, and the realities of cyber warfare. Expert speakers followed by an in-depth discussion of both the technologies and their policy implications. Open to undergraduate and graduate students.
Terms: Spr
| Units: 1-3
MS&E 494:
The Energy Seminar (CEE 301, ENERGY 301)
Interdisciplinary exploration of current energy challenges and opportunities, with talks by faculty, visitors, and students. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 802:
TGR Dissertation
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Barley, S. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Chiu, S. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Goel, S. (PI);
Hausman, W. (PI);
Hinds, P. (PI);
Howard, R. (PI);
Johari, R. (PI);
Katila, R. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Saunders, M. (PI);
Shachter, R. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Tse, E. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
