Management Science and Engineering

MS&E 20. Discrete Probability Concepts And Models. 4 Units.

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.

MS&E 33N. How We Decide: Social Choice in the Age of Algorithms. 4 Units.

The digital revolution arrived with the promise of improving human life, including through its ability to transform the way in which we make social decisions. But one of the most common critiques today is that unstructured interactions in social media and online platforms have actually set us back by spreading fake news, amplifying polarization, and failing to aggregate our diverse views and opinions into collective choices that move our society forward. nnHow should social decisions be made in the age of algorithms? We will approach this question through the lens of social choice theory, and connect this theory from economics and political science to the potential design of algorithms that aggregate our diverse preferences and information. We will review various systems of preference and information aggregation in small groups as well as large societies, including voting systems, bargaining protocols, and methods of deliberation. We will also describe decision making problems that arise in modern applications, such as distributed systems like blockchains and Wikipedia, as well as applications of topical interest such as the assignment of children to schools, the design of congressional districts, and the direct involvement of communities in participatory budgeting. nnA key objective of the class will be to get students to think about how social choice theory can be applied to real-life problems through the design of algorithms. There are no prerequisites, but students should come prepared to use high school level mathematics and deductive reasoning.
Same as: POLISCI 33N

MS&E 52. Introduction to Decision Making. 3-4 Units.

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.

MS&E 73SI. ASES Entrepreneurship Bootcamp. 1 Unit.

Practicum designed to introduce freshmen and sophomores to design thinking and entrepreneurship. Students learn how to conduct user interviews, identifynmarket opportunities, find product-market fit, and develop pitch decks. Concludes with a fast-paced problem-solving session and a 'demo day'-style pitch event, in which students pitch their projects to Silicon Valley venture capitalists (VCs), entrepreneurs, and investors. Students interact with highly experienced speakers, gain mentorship from upperclassmen and VCs, and develop a worldwide entrepreneurship network, both in and out of Silicon Valley. No background knowledge or experience is necessary, only willingness to work hard, get your hands dirty, and learn. Limited enrollment. Application required: https://tinyurl.com/bootcamp2021.

MS&E 79SI. Values and Principles in the Workplace: PEAK Fellows. 1 Unit.

Extension of the PEAK Fellows program. Serves as an opportunity for students to explore what it means to create and work for principled, entrepreneurial businesses. Through readings and peer-led discussions, students will definentheir personal set of values and principles to serve as a guide in shaping future teams and workplaces. Prerequisite: admission to PEAK Fellows Program. See https://stvp.stanford.edu/peak-fellows.

MS&E 92. Introduction to Health Policy Modeling. 3 Units.

The application of mathematical models to problems in health policy. Estimating the benefits, harms, costs, and uncertainties of a health policy or intervention. Understanding concepts of cost-effectiveness analysis. Developing decision models that capture the tradeoffs between policy alternatives. Examples include disease screening, prevention, and treatment, combating the opioid epidemic, and protecting the blood supply. As a course project, students will develop a simple decision model to evaluate a current health policy problem.

MS&E 92Q. International Environmental Policy. 3 Units.

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.

MS&E 108. Senior Project. 5 Units.

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.

MS&E 111. Introduction to Optimization. 3-4 Units.

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.
Same as: ENGR 62, MS&E 211

MS&E 111X. Introduction to Optimization (Accelerated). 3-4 Units.

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.
Same as: ENGR 62X, MS&E 211X

MS&E 112. Mathematical Programming and Combinatorial Optimization. 3 Units.

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: basic concepts in linear algebra, probability theory, CS 106A or X.
Same as: MS&E 212

MS&E 120. Introduction to Probability. 4 Units.

Probability is the foundation behind many important disciplines including statistics, machine learning, risk analysis, stochastic modeling and optimization. This course provides an in-depth undergraduate-level introduction to fundamental ideas and tools of probability. Topics include: the foundations (sample spaces, random variables, probability distributions, conditioning, independence, expectation, variance), a systematic study of the most important univariate and multivariate distributions (Normal, Multivariate Normal, Binomial, Poisson, etc...), as well as a peek at some limit theorems (basic law of large numbers and central limit theorem) and, time permitting, some elementary markov chain theory. Prerequisite: CME 100 or MATH 51.

MS&E 121. Introduction to Stochastic Modeling. 4 Units.

Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite: 120, 125, or equivalents.

MS&E 125. Introduction to Applied Statistics. 4 Units.

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.

MS&E 130. Information Networks and Services. 3 Units.

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.

MS&E 135. Networks. 3 Units.

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".

MS&E 140. Accounting for Managers and Entrepreneurs. 3-4 Units.

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.
Same as: MS&E 240

MS&E 145. Introduction to Finance and Investment. 4 Units.

Introduction to modern quantitative finance and investments. The course focuses on the basic principles underlying financial decision making which are applicable to all forms of investment: stocks, bonds, real estate, corporate finance, etc., and how they are applied in practice. Topics: 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, value at risk, conditional value at risk; designing optimal security portfolios; the capital asset pricing model, arbitrage pricing theory. Group projects involving financial market data. No prior knowledge of finance required. Prerequisite: basic preparation in probability, statistics, and optimization.

MS&E 146. Corporate Financial Management. 3-4 Units.

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, or equivalent.
Same as: MS&E 249

MS&E 148. Ethics of Finance. 2 Units.

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.

MS&E 149. Hedge Fund Management. 1-2 Unit.

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.

MS&E 152. Introduction to Decision Analysis. 3-4 Units.

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. Prerequisites: high school algebra and basic spreadsheet skills.

MS&E 175. Innovation, Creativity, and Change. 3-4 Units.

Problem solving in organizations; creativity and innovation skills; thinking tools; creative organizations, teams, individuals, and communities. Limited enrollment.

MS&E 178. The Spirit of Entrepreneurship. 2 Units.

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.

MS&E 180. Organizations: Theory and Management. 4 Units.

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.

MS&E 182A. Leading Organizational Change. 4 Units.

This course blends lecture, case discussions, readings about pertinent research, and hands-on projects to learn about what leaders and senior teams can do to bring about broad-based change in complex organizations. Topics include the role of the CEO and the senior team, organizational growth and scaling, organizational culture, organizational design, and innovation. The course focuses in particular on the causes and cures for dysfunctional organizational friction, and the related question what organizations ought to make difficult or impossible to do. Limited enrollment. For juniors and seniors, with preference given to students who have taken MS&E 180.

MS&E 182B. Leading Organizational Change II. 4 Units.

Project-based course for students who wish to explore focused intellectual topics or applied questions pertinent to leading organizational change. Work is done in groups of three to four students that are formed prior to the start of class. Prerequisite: At least one student in each group shall have completed MS&E 182A.

MS&E 184. Future of Work: Issues in Organizational Learning and Design. 4 Units.

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.

MS&E 185. Global Work. 4 Units.

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.

MS&E 188. Organizing for Good. 4 Units.

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. Focus is on the role of organizations in society, the ways that organizations can "do good," the challenges organizations face in attempting to "do good", limitations to current ways of organizing, alternative ways to organize and lead organizations that are "good," and the role and responsibilities of individuals in organizations. Students will reflect on and refine their own values and purpose to identify ways in which they can ¿do good.¿ This course has been designated as a Cardinal Course by the Haas Center for Public Service.

MS&E 193. Technology and National Security: Past, Present, and Future. 3-4 Units.

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.
Same as: INTLPOL 256, MS&E 293

MS&E 201. Dynamic Systems. 3 Units.

Dynamic System: 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 course covers the notions of equilibrium, stability, growth and limit cycle of dynamic systems and discussed in terms of examples in product market penetration, business competition, ecology and spread of epidemics. The course gives an 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. The course concludes with optimal control theory and differential games. Optimal economic growth model and optimal dynamic pricing are used to illustrate how the optimal control theory is applied to economic modeling analysis and business application. A platform competition model is used to illustrate how different games can be used to do dynamic competitive analysis. Required a project in dynamic system modeling. Pre-requisite: calculus and linear algebra.

MS&E 202. Topics in Management Science and Engineering. 1 Unit.

Topics in Management Science and Engineering. Restricted to MS&E MS students.

MS&E 208A. Practical Training. 1 Unit.

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: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 208B. Practical Training. 1 Unit.

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: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 208C. Practical Training. 1 Unit.

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: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 208D. Practical Training. 1 Unit.

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: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 208E. Part-Time Practical Training. 1 Unit.

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: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 211. Introduction to Optimization. 3-4 Units.

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.
Same as: ENGR 62, MS&E 111

MS&E 211X. Introduction to Optimization (Accelerated). 3-4 Units.

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.
Same as: ENGR 62X, MS&E 111X

MS&E 212. Mathematical Programming and Combinatorial Optimization. 3 Units.

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: basic concepts in linear algebra, probability theory, CS 106A or X.
Same as: MS&E 112

MS&E 213. Introduction to Optimization Theory. 3 Units.

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.
Same as: CS 269O

MS&E 220. Probabilistic Analysis. 3-4 Units.

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, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: multivariable calculus and some linear algebra.

MS&E 221. Stochastic Modeling. 3 Units.

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.

MS&E 223. Simulation. 3 Units.

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.

MS&E 226. Fundamentals of Data Science: Prediction, Inference, Causality. 3 Units.

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.

MS&E 230. Incentives and Algorithms. 3 Units.

Many policies and algorithms interact with many self-interested agents. Provides students methodology and vocabulary to analyze and design such problems. Provides foundations of basic economics and game theoretic concepts and will apply them to a variety of real world applications. Topics: equilibrium analysis, auction design, matching markets, social choice, externalities and network design. Applications: matching marketplaces (NRMP, Upwork, college admissions), dynamic pricing in ride-sharing, advertising mechanisms, reputation systems, platform design, kidney exchange and organ allocations, food banks. Relation to MS&E 232: while 232 provides an extensive introduction to game theory, this course focuses on designing the "rules of the game" to achieve good economic outcomes and will cover only a few basic topics from MS&E 232, including more on application and algorithmic design. Prerequisites: basic mathematical maturity at the level of MATH 51, and probability at the level of MS&E 120, 220 or EE 178.

MS&E 231. Introduction to Computational Social Science. 3 Units.

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.
Same as: SOC 278

MS&E 232. Introduction to Game Theory. 3 Units.

Examines foundations of strategic environments with a focus on game theoretic analysis. Provides a solid background to game theory as well as topics in behavioral game theory and the design of marketplaces. Introduction to analytic tools to model and analyze strategic interactions as well as engineer the incentives and rules in marketplaces to obtain desired outcomes. Technical material includes non-cooperative and cooperative games, behavioral game theory, equilibrium analysis, repeated games, social choice, mechanism and auction design, and matching markets. Exposure to a wide range of applications. Lectures, presentations, and discussion. Prerequisites: basic mathematical maturity at the level of MATH 51, and probability at the level of MS&E 120 or EE 178.

MS&E 232H. Introduction to Game Theory. 3 Units.

Game theory uses mathematical models to study strategic interactions and situations of conflict and cooperation between rational decision-makers. This course provides an accelerated introduction to tools, models and computation in non-cooperative and cooperative game theory. Technical material includes normal and extensive form games, zero-sum games, Nash equilibrium and other solution concepts, repeated games, games with incomplete information, auctions and mechanism design, the core, and Shapley value. Exploration of applications of this material through playing stylized in-class and class-wide games and analyzing real-life applications. Prerequisites: mathematical maturity at the level of MATH51, and probability at the level of MS&E 120, or equivalent.
Same as: Accelerated

MS&E 234. Data Privacy and Ethics. 3 Units.

This course engages with 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 raises both practical and theoretical considerations. As part of the module on experimentation, students are required to complete the Stanford IRB training for social and behavioral research. The course assumes a strong technical familiarity with the practice of machine learning and and data science. Recommended: 221, 226, CS 161, or equivalents.

MS&E 235. Network Structure and Epidemics. 3 Units.

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. Recommended: 212.
Same as: MS&E 337

MS&E 240. Accounting for Managers and Entrepreneurs. 3-4 Units.

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.
Same as: MS&E 140

MS&E 241. Economic Analysis. 3-4 Units.

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.

MS&E 243. Energy and Environmental Policy Analysis. 3 Units.

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.

MS&E 245A. Investment Science. 3-4 Units.

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.

MS&E 245B. Advanced Investment Science. 3 Units.

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.

MS&E 246. Financial Risk Analytics. 3 Units.

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.

MS&E 249. Corporate Financial Management. 3-4 Units.

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, or equivalent.
Same as: MS&E 146

MS&E 250A. Engineering Risk Analysis. 3 Units.

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.

MS&E 250B. Project Course in Engineering Risk Analysis. 3 Units.

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.

MS&E 251. Introduction to Stochastic Control with Applications. 3 Units.

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.
Same as: EE 266

MS&E 252. Decision Analysis I: Foundations of Decision Analysis. 3-4 Units.

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. Prerequisite: 220 or equivalent.

MS&E 254. The Ethical Analyst. 1-3 Unit.

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.

MS&E 256. Technology Assessment and Regulation of Medical Devices. 3 Units.

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.
Same as: BIOE 256

MS&E 256A. Technology Assessment and Regulation of Medical Devices. 1 Unit.

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.

MS&E 260. Introduction to Operations Management. 3 Units.

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.

MS&E 263. Healthcare Operations Management. 3 Units.

US health care spending is approximately 18% of GDP, growing rapidly, and driven in large part by prices and waste rather than quality and access. New approaches for improving health care delivery are urgently needed. This class focuses on the use of analytical tools to support efficient health care delivery. Topics include case studies on capacity planning, resource allocation, and scheduling. Methods include queueing, optimization, and simulation. Prerequisites: basic knowledge of Excel, probability, and optimization. For students in the Schools of Medicine, Business, and Law the course includes a variant of the curriculum with less emphasis on the technical material.
Same as: PEDS 263

MS&E 265. Introduction to Product Management. 3 Units.

Product Managers define a product's functional requirements and lead cross functional teams responsible for development, launch, and ongoing improvement. 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 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.

MS&E 267. Service Operations and the Design of Marketplaces. 3 Units.

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.

MS&E 270. Strategy in Technology-Based Companies. 3-4 Units.

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.

MS&E 271. Global Entrepreneurial Marketing. 3-4 Units.

Introduces core marketing concepts to bring a new product or service to market and build for its success. Geared to both entrepreneurs and intrapreneurs alike who have a passion for innovation. Course themes include: Identifying markets and opportunities, defining the offering and customer experience, creating demand, generating revenue, and measuring success. The team-based final focuses on developing a go-to-market strategy based on concepts from the course. Learn about managing self, building culture and teams, strategically think about your contribution as entrepreneur or intrapreuneur to an organization, community or society at large. Highly experiential and project based. Limited enrollment.

MS&E 272. Entrepreneurship without Borders. 3-4 Units.

How and why does access to entrepreneurial opportunities vary by geographic borders, racial/gender borders, or other barriers created by where or who you are? What kinds of inequalities are created by limited access to capital or education and what role does entrepreneurship play in upward mobility in societies globally? What are the unique issues involved in creating a successful startup in Europe, Latin America, Africa, 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 unequal access to wealth creation and innovation via entrepreneurship, while also networking with top entrepreneurs and venture capitalists who work across borders.

MS&E 273. Venture Creation for the Real Economy. 3-4 Units.

A project-based course where teams of 4 prepare their entrepreneurial venture for fundraising and launch. Students acquire the experience of an early-stage entrepreneur as they progress through stages of team building, opportunity assessment, product-market fit analysis, business model architecture, go-to market strategy, product planning, financial modelling, and fundraising planning. The course structure includes weekly workshops, guest presentations from seasoned entrepreneurs, weekly meetings with the teaching team, and one-on-one support from a dedicated industry mentor. The experience culminates in three pitches to panels of VCs and other industry experts. By the end of the class, successful students will be equipped with the knowledge and network to create impactful business ideas, many of which have been launched from this class. Open to all Stanford students. No prerequisites. For more information, visit the course website: https://web.stanford.edu/class/msande273. Enrolment by application: https://web.stanford.edu/class/msande273/apply.
Same as: CEE 246

MS&E 274. Dynamic Entrepreneurial Strategy. 3 Units.

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.

MS&E 275. Intelligent Growth in Startups. 3 Units.

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.

MS&E 276. Entrepreneurial Management and Finance. 3 Units.

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.

MS&E 277A. Entrepreneurial Leadership. 3 Units.

This course sequence is part of the Accel Leadership Program which accepts 24 technically-minded students from across Stanford. The program focuses on how to lead entrepreneurial ventures, with a focus on startup strategy, organizational structure, securing resources, operating models, and how to build an effective team. There will be skill-building workshops as well as living case studies with startup founders. Each student will be on a team that will tackle a real world business problem for a high growth venture and present their findings to the class. The selection process will run during Fall quarter 2020, and applications will be available at https://stvp.stanford.edu/students.

MS&E 277B. Entrepreneurial Leadership. 3 Units.

This course sequence is part of the Accel Leadership Program which accepts 24 technically-minded students from across Stanford. The program focuses on how to lead entrepreneurial ventures, with a focus on startup strategy, organizational structure, securing resources, operating models, and how to build an effective team. There will be skill-building workshops as well as living case studies with startup founders. Each student will be on a team that will tackle a real world business problem for a high growth venture and present their findings to the class. The selection process will run during Fall quarter 2020, and applications will be available at https://stvp.stanford.edu/students.

MS&E 278. Patent Law and Strategy for Innovators and Entrepreneurs. 2-3 Units.

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 300 startups that have collectively raised over $3B 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.
Same as: ME 208

MS&E 280. Organizational Behavior: Evidence in Action. 3-4 Units.

Organization theory; concepts and functions of management; behavior of the individual, work group, and organization. Emphasis is on cases and related discussion. Enrollment limited.

MS&E 284. Designing Modern Work Organizations. 3 Units.

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.

MS&E 292. Health Policy Modeling. 3 Units.

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.

MS&E 293. Technology and National Security: Past, Present, and Future. 3-4 Units.

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.
Same as: INTLPOL 256, MS&E 193

MS&E 296. Technology, Innovation and Modern War: Keeping America's Edge in an Era of Great Power Competition. 4 Units.

This course explores how technology advances in areas like Cyber, Space, AI, Machine Learning, and Autonomy will create new types of military systems that will be deployed in modern conflicts, and the new operational concepts, organization and strategies that will emerge from these technologies. The course develops an appreciation that innovation in military systems throughout history has followed a repeatable pattern: technology innovation > new weapons > experimentation with new weapons/operational concepts > pushback from incumbents > first use of new operational concepts. Students will apply course concepts and learning to identify opportunities for the U.S. to maintain its technological edge and compete more effectively in this era of great power rivalry. The course builds on concepts presented in MS&E 193/293: Technology and National Security and provides a strong foundation for MS&E 297: Hacking for Defense.
Same as: INTLPOL 340

MS&E 297. "Hacking for Defense": Solving National Security issues with the Lean Launchpad. 3-4 Units.

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.

MS&E 301. Dissertation Research. 1-10 Unit.

Prerequisite: doctoral candidacy.

MS&E 302. Fundamental Concepts in Management Science and Engineering. 1 Unit.

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.

MS&E 310. Linear Programming. 3 Units.

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.

MS&E 311. Optimization. 3 Units.

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.
Same as: CME 307

MS&E 313. Almost Linear Time Graph Algorithms. 3 Units.

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.
Same as: CS 269G

MS&E 315. Advanced Optimization Theory. 3 Units.

Advanced optimization methods, algorithmic techniques, and proof strategies for obtaining provably efficient methods for minimizing high dimensional continuous functions. Focus on solving broad canonical optimization and obtaining start-of-the-art running times for both general oracle-based optimization problems as well as structured problems. Topics vary year to year based on interest. Possible topics include (but are not limited to) critical point computation of non-convex functions, linear system solving, eigenvector computation, finite sum optimization, linear system solving, principle component analysis, interior point methods, linear programming, semi-definite programming, and cutting-plane methods. Prerequisite: MS&E 213 or equivalent.

MS&E 316. Discrete Mathematics and Algorithms. 3 Units.

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.
Same as: CME 305

MS&E 319. Approximation Algorithms. 3 Units.

The theory of matching with its roots in the work of mathematical giants like Euler and Kirchhoff has played a central and catalytic role in combinatorial optimization for decades. More recently, the growth of online marketplaces for allocating advertisements, rides, or other goods and services has led to new interest and progress in this area. The course starts with classic results characterizing matchings in bipartite and general graphs and explores connections with algebraic graph theory, permanent, Pfaffian and counting and sampling matchings. Those results are complemented with models and algorithms developed for modern applications in market design, online advertising, and ride sharing. May be repeated for credit. Prerequisite: 212, CS 261, or equivalent.

MS&E 321. Stochastic Systems. 3 Units.

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).

MS&E 322. Stochastic Calculus and Control. 3 Units.

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.

MS&E 323. Stochastic Simulation. 3 Units.

Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.

MS&E 324. Stochastic Methods in Engineering. 3 Units.

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.
Same as: CME 308, MATH 228

MS&E 325. Optimal Transport in Operations Research, Statistics, and Economics. 3 Units.

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.

MS&E 326. Advanced Topics in Game Theory with Engineering Applications. 3 Units.

Advanced Topics in Game Theory with Engineering Applications.

MS&E 327. Topics in Causal Inference. 3 Units.

This course introduces the fundamental ideas and methods in causal inference, and surveys a broad range of problems and applications. Emphasis will be on framing causal problems and identifying causal effects in both randomized experiments and observational studies. Topics will include: the potential outcomes framework; randomization-based inference and covariate adjustment; matching, and IPW; instrumental variables, regression discontinuity and synthetic ncontrols. Examples and applications will be taken from the fields of education, political science, economics, public health and digital marketing.
Same as: STATS 209A

MS&E 330. Law, Order, & Algorithms. 3 Units.

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission is by consent of instructor and is limited to 20 students. To enroll in the class, please complete the course application by March 20, available at: https://5harad.com/mse330/. Grading is based on response papers, class participation, and a final project. Prerequisite: CS 106A or equivalent knowledge of coding.
Same as: CS 209, CSRE 230, SOC 279

MS&E 332. Security and Risk in Computer Networks. 3 Units.

Risk management of large scale computing and networking systems with respect to security, data integrity, performance collapse, and service disruption. Qualitative and analytical basis for assessment, modeling, control, and mitigation of network risks. Stochastic risk models. Contact process. Random fields on networks. Virus and worm propagation dynamics and containment. Denial of service attacks. Intruder detection technologies. Distributed network attacks and countermeasures. Disaster recovery networks. Network protection services and resource placement. Autonomic self-defending networks. Economics of risk management. Emphasis is on analytics and quantitative methods.

MS&E 334. Topics in Social Data. 3 Units.

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, rank aggregation, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.

MS&E 335. Queueing and Scheduling in Processing Networks. 3 Units.

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.

MS&E 336. Computational Social Choice. 3 Units.

An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Same as: CS 366

MS&E 337. Network Structure and Epidemics. 3 Units.

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. Recommended: 212.
Same as: MS&E 235

MS&E 338. Reinforcement Learning: Frontiers. 3 Units.

This class covers subjects of contemporary research contributing to the design of reinforcement learning agents that can operate effectively across a broad range of environments. Topics include exploration, generalization, credit assignment, and state and temporal abstraction. An important component of the class is a research project aimed at understanding a focused issue in reinforcement learning. Can be repeated for credit. Prerequisites: 226, CS 234, or EE 277, and experience with mathematical proofs.

MS&E 346. Reinforcement Learning for Stochastic Control Problems in Finance. 3 Units.

This course will explore a few problems in Mathematical Finance through the lens of Stochastic Control, such as Portfolio Management, Derivatives Pricing/Hedging and Order Execution. For each of these problems, we formulate a suitable Markov Decision Process (MDP), develop Dynamic Programming (DP) solutions, and explore Reinforcement Learning (RL) algorithms. The course emphasizes the theory of DP/RL as well as modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of coding exercises of the methods. No pre-requisite coursework expected, but a foundation in undergraduate Probability, basic familiarity with Finance, and Python coding skills are required. Dynamic Programming or Reinforcement Learning background not required.
Same as: CME 241

MS&E 347. Credit Risk: Modeling and Management. 3 Units.

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.

MS&E 348. Optimization of Uncertainty and Applications in Finance. 3 Units.

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.

MS&E 349. Financial Statistics. 3 Units.

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.

MS&E 350. Fundamental and Current Topics in Engineering Risk Analysis. 3 Units.

Limited to doctoral students and advanced master 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.

MS&E 351. Dynamic Programming and Stochastic Control. 3 Units.

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.

MS&E 352. Decision Analysis II: Professional Decision Analysis. 3-4 Units.

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.

MS&E 353. Decision Analysis III: Frontiers of Decision Analysis. 3 Units.

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.

MS&E 355. Influence Diagrams and Probabilistics Networks. 3 Units.

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.

MS&E 365. Mechanism and Market Design. 3 Units.

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.
Same as: ECON 287

MS&E 366. Market Design and Resource Allocation in Non-Profit Settings. 3 Units.

Survey of recent research on market design and resource allocation with a focus on under-explored domains in non-profit settings. Will start with classic results in allocation, matching and social choice, and discuss them in the context of relevant objectives such as social welfare and equity. Will then draw on techniques from operations research and economics to explore the design of resource allocation platforms in emerging applications including housing, humanitarian logistics, volunteer coordination, food allocation, conservation and sustainability, and informal markets in the developing world. Prerequisite: consent of instructor; background material will be covered throughout the course as necessary. May be repeated for credit.

MS&E 370. Current Topics in Strategy, Innovation and Entrepreneurship. 1 Unit.

This course will cover focused exploration of contemporary readings and classics as relevant in strategy, innovation and entrepreneurship such as platforms, ecosystems, institutional logics, and strategic "games" in nascent markets. The course will include both content and methods discussions, including theory-building from multiple cases. PhD students only. Prerequisite: Consent of instructor.

MS&E 371. Innovation and Strategic Change. 1-3 Unit.

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.

MS&E 372. Entrepreneurship Doctoral Research Seminar. 1-3 Unit.

Classic and current research on entrepreneurship. In this class, we will focus on questions of how entrepreneurship may exacerbate or alleviate inequalities in society across race/ethnicity, gender and class. How do institutional environments shape who engages in entrepreneurship and how successful they become? We will read literature from economics, sociology and strategy/management that has theoretically and empirically examined the phenomenon of entrepreneurship. Limited enrollment, restricted to PhD students. Prerequisites: SOC 363 or equivalent, and permission of instructor.

MS&E 376. Strategy Doctoral Research Seminar. 1-3 Unit.

Classic and current research on business and corporate strategy. Limited enrollment, restricted to PhD students. Course may be repeated for credit.

MS&E 379. Social Data Analysis. 3 Units.

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.

MS&E 380. Doctoral Research Seminar in Organizations. 3 Units.

Limited to Ph.D. students. Topics from current published literature and working papers. Content varies. Prerequisite: consent of instructor.

MS&E 384. Groups and Teams. 3 Units.

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.

MS&E 387. Design of Field Research Methods. 3 Units.

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.

MS&E 388. Contemporary Themes in Work and Organization Studies. 3 Units.

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.

MS&E 389. Seminar on Organizational Theory. 5 Units.

The social science literature on organizations assessed through consideration of the major theoretical traditions and lines of research predominant in the field. For PhD students only.
Same as: EDUC 375A, SOC 363A

MS&E 390. Doctoral Research Seminar in Health Systems Modeling. 1-3 Unit.

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.

MS&E 391. Doctoral Research Seminar in Energy-Environmental Systems Modeling and Analysis. 1-3 Unit.

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.

MS&E 394. Advanced Methods in Modeling for Climate and Energy Policy. 3 Units.

Design and application of computational models and techniques for assessing climate and energy policy, and for predicting the impacts of climate change. Topics include 1) best practices in research design, model design and selection; 2) types of models available, taxonomy, core concepts, and limitations; 3) interpreting and presenting model results; and 4) advanced topics and recent literature, e.g. representing uncertainty, technological change, distributional change, and cross-sectoral climate impacts. Prerequisites: MS&E 241, MS&E 211, or equivalents.

MS&E 408. Directed Reading and Research. 1-15 Unit.

Directed reading and research on a subject of mutual interest to student and faculty member. Available to undergraduate, master, and doctoral students. Student must clarify deliverables, units, and grading basis with faculty member before applicable deadlines. Prerequisite: consent of instructor.

MS&E 441. Policy and Economics Research Roundtable. 1 Unit.

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.
Same as: PERR

MS&E 448. Big Financial Data and Algorithmic Trading. 3 Units.

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, 245A, 245B, or equivalents, some exposure to statistics and programming. Enrollment limited. Admission by application; details at first class.

MS&E 449. Buy-Side Investing. 1-2 Unit.

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.

MS&E 454. Decision Analysis Seminar. 1 Unit.

Current research and related topics presented by doctoral students and invited speakers. May be repeated for credit. Prerequisite: 252.

MS&E 463. Healthcare Systems Design. 3-4 Units.

Students work on projects to analyze and design various aspects of healthcare delivery including hospital patient flow, clinical risk prediction, 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 and a mandatory meeting during the preceding Winter quarter to choose projects.
Same as: PEDS 463

MS&E 472. Entrepreneurial Thought Leaders' Seminar. 1 Unit.

Learn about entrepreneurship, innovation, culture, startups and strategy from a diverse lineup of accomplished leaders and entrepreneurs in venture capital, technology, education, philanthropy and more. Open to all Stanford students. Required weekly assignment. May be repeated for credit.

MS&E 489. d.Leadership: Leading Disruptive Innovation. 3-4 Units.

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.
Same as: ME 368

MS&E 494. The Energy Seminar. 1 Unit.

Interdisciplinary exploration of current energy challenges and opportunities, with talks by faculty, visitors, and students. May be repeated for credit.
Same as: CEE 301, ENERGY 301

MS&E 495. Sustainable Energy Interdisciplinary Graduate Seminar. 1 Unit.

Graduate students will present their ongoing research to an audience of faculty and graduate students with a diversity of disciplinary perspectives regarding sustainable energy.
Same as: CEE 372, ENERGY 309

MS&E 802. TGR Dissertation. 0 Units.

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