MS&E 10SC:
Artificial Intelligence and Deliberative Democracy
Deliberative democracy is a political theory that holds that democracy should be based on informed, respectful, and inclusive public deliberation. In this SoCo course, we explore the relationship between artificial intelligence (AI) and deliberative democracy, and examine how AI can be used to support and enhance the democratic process through deliberative democracy. This course will focus on the use of AI in the Stanford Online Deliberation Platform (a collaboration between the Crowdsourced Democracy Team and Deliberative Democracy Lab, both at Stanford), the ethics of AI and democracy, and the potential for AI to support deliberation and participation. The course will also explore the challenges and limitations of using AI in a democratic context and the need for effective regulation and governance of AI.
Terms: Sum
| Units: 2
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 75:
Redefining Creativity: Designing Human Connections in an AI World
With the recent developments in generative AI, the value in human creativity is increasingly a focus. Course draws from lessons from creativity in the arts to teach engineering students methods for creativity derived from musicians and artists. For our engineering students to learn creativity as a skill that is distinguishable and differentiated from generative artificial intelligence, this course explores, for instance the anatomy of a Hollywood pop song and the process behind the creation of globally impactful art. Students learn how to transfer these skills into the creation of engaging entrepreneurial solutions, for effective storytelling, and in developing their unique personal and professional stories. Students learn skills to unlock creative power which they will apply in the course as a design vehicle for a wide range of applications in engineering, self-expression, technological exploration, and the development of solutions that are centered around human connection and emotional engagement with the user. Sessions are practical, drawing tools and lessons from interdisciplinary individuals with wide-ranging careers. No artistic or entrepreneurial experience necessary.
Terms: Sum
| Units: 3
MS&E 79SI:
Values and Principles in the Workplace: PEAK Fellows
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.
Terms: Aut
| Units: 1
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 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. Cardinal Course certified by the 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: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR
MS&E 111DS:
Introduction to Optimization: Data Science (MS&E 211DS)
Formulation and computational analysis of linear, discrete, and other optimization problems. Strong emphasis on data science and machine learning applications, as well as applications in matching and pricing in online markets. Prerequisite: CME 100 or MATH 51.
Terms: Win
| Units: 3-4
MS&E 111X:
Introduction to Optimization (Accelerated) (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: Spr
| Units: 3-4
| UG Reqs: WAY-AQR
MS&E 112:
Graph and Combinatorial Optimization (MS&E 212)
Optimization problems dealing with graph structure. Topics: introduction to graph theory; combinatorial optimization problems on networks including network flows, matching, and assignment problems; NP-completeness and approximation algorithms; applications in the study of social networks, market design, and bioinformatics. Prerequisites: basic concepts in linear algebra, probability theory, CS 106A or X.
Terms: Win
| Units: 3
MS&E 120:
Introduction to Probability
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.
Terms: Aut
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
MS&E 120ACE:
Introduction to Probability, ACE
Students attend MS&E 120 lectures with additional recitation sessions; two to four hours per week. Enrollment by permission only. Prerequisite: students should submit application for enrollment at: https://engineering.stanford.edu/students/programs/engineering-diversity-programs/additional-calculus-engineers before study list deadline. It is recommended students enroll in the regular section of MS&E 120 prior to submitting application. Corequisite: MS&E 120.
Terms: Aut
| Units: 1
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 or equivalent.
Terms: Win
| 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: Spr
| 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 134:
Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, POLISCI 154, PUBLPOL 155, SOC 127)
Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. This course is the required gateway course for the new major in Data Science & Social Systems. Preference given to Data Science & Social Systems B.A. majors and prospective majors. Course material and presentation will be at an introductory level. Enrollment and participation in one discussion section is required. Sign up for the discussion section will occur on Canvas at the start of the quarter. Prerequisites: CS106A (required), DATASCI 112 (recommended as pre or corequisite). Limited enrollment. Please complete the interest form here: https://forms.gle/8ui9RPgzxjGxJ9k29. A permission code will be given to admitted students to register for the class.
Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
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: Win
| 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.
Terms: Spr, Sum
| Units: 3
MS&E 141:
Economic Analysis (MS&E 241)
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: 111 or 211, and ECON 50.
Terms: Win
| Units: 3-4
MS&E 145:
Introduction to Finance and Investment
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, expected shortfall; designing optimal security portfolios; the capital asset pricing model. Group projects involving financial market data. No prior knowledge of finance required. Prerequisite: MS&E 120 or equivalent.
Terms: Win
| Units: 4
MS&E 146:
Corporate Financial Management (MS&E 249)
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. Limited enrollment. Recommended: 145, 245A, or equivalent.
Terms: Win
| Units: 3-4
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 repeated 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. Prerequisites: high school algebra and basic spreadsheet skills.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
MS&E 178:
Entrepreneurship: Principles & Perspectives
This course uses the speakers from the Entrepreneurial Thought Leader seminar (MS&E472) to seed discussions around core topics in entrepreneurship. Students are exposed to a variety of guest speakers and lecturers. Topics change each quarter based on the speakers but cover foundational concepts: e.g. resilience, discovery, leadership, strategy, negotiations. Reflection and experiential exercises are used to augment learning. Enrollment limited to 60 students. See note for course application.
Terms: Aut, Win, Spr
| Units: 2
| Repeatable
for credit
MS&E 180:
Organizations: Theory and Management
For undergraduates only. Classical and contemporary organization theory; the behavior of individuals, groups, and organizations. Limited enrollment; preference to declared MS&E majors and seniors from other departments.
Terms: Aut, Spr, Sum
| Units: 3-4
MS&E 184:
Flash Teams: Theory and Practice
Today's teams work in a world where experts are available everywhere all the time, where remote work has become a norm, and where data can be in-the-loop to guide team decisions. In this world, teams can become adaptive, augmented, and on-demand. This class equips students to understand and use this emerging form of collaboration - flash teams - by laying out the theory and practice involved in creating them. Already industries are being transformed by this new approach to teaming, and new opportunities, challenges, and responsibilities are arising. This class uses a practice-based workshop approach to help students develop the tools and understanding they need.
Terms: Aut
| 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. 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. Limited Enrollment; preference to MS&E juniors and seniors, and seniors in other majors.
Terms: Win
| Units: 4
MS&E 193:
Technology and National Security (INTLPOL 256)
Explores the relation between technology, war, and national security policy with reference to current events. Course focuses on current U.S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges, including the recent Russia-Ukraine conflict. Topics include: 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: Aut
| Units: 3-4
| UG Reqs: WAY-SI
MS&E 206:
Incentives in Computer Science (CS 269I)
Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, reputation systems, social choice, and social network analysis. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. Prerequisites: CS106B/X and CS161, or permission from the instructor.
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: https://forms.gle/bFtMtwJMyaCJRhkf8 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: https://forms.gle/bFtMtwJMyaCJRhkf8 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: https://forms.gle/bFtMtwJMyaCJRhkf8 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: https://forms.gle/bFtMtwJMyaCJRhkf8 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: https://forms.gle/bFtMtwJMyaCJRhkf8 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: Aut
| Units: 3-4
MS&E 211DS:
Introduction to Optimization: Data Science (MS&E 111DS)
Formulation and computational analysis of linear, discrete, and other optimization problems. Strong emphasis on data science and machine learning applications, as well as applications in matching and pricing in online markets. Prerequisite: CME 100 or MATH 51.
Terms: Win
| Units: 3-4
MS&E 211X:
Introduction to Optimization (Accelerated) (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: Spr
| Units: 3-4
MS&E 212:
Graph and Combinatorial Optimization (MS&E 112)
Optimization problems dealing with graph structure. Topics: introduction to graph theory; combinatorial optimization problems on networks including network flows, matching, and assignment problems; NP-completeness and approximation algorithms; applications in the study of social networks, market design, and bioinformatics. Prerequisites: basic concepts in linear algebra, probability theory, CS 106A or X.
Terms: Win
| Units: 3
MS&E 214:
Advanced Applied Optimization
This class will illustrate applications of optimization principles such as linear and non-linear programming, decision making under uncertainty, and dynamic programming in several important real-world scenarios, including Machine Learning, Market Design, Logistics and Revenue Management, Centralized and Decentralized Finance, Recommendation Systems, and Participatory Budgeting. The focus will be on applying the techniques, and in addition to the modeling, there will also be several hands-on assignments that will require you to deal with large and complex data sets. Prerequisites: Linear programming at the level of MS&E 111; proficiency in some programming language (preferably python).
Terms: Spr
| Units: 3
MS&E 218:
Applied Data Science (CME 218)
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to important techniques in applied data science as well as to the soft skills necessary for success in applied data science, such as ethics, unintended consequences and team building. Enrollment by application only. Graduate students only. The course application closes Sept 25, 2023. Application and more information: https://forms.gle/gzGXkJmGMVYuJabK7
Terms: Aut
| Units: 3
| Repeatable
2 times
(up to 6 units total)
MS&E 220:
Probabilistic Analysis
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.
Terms: Aut
| Units: 3-4
MS&E 221:
Stochastic Modeling
Focus is on time-dependent random phenomena. Topics: discrete time Markov chains, Markov jump processes, queueing theory, and applications. Emphasis on model-building, computation, and related calibration and statistical issues. Prerequisite: 220 or equivalent, or consent of instructor.
Terms: Spr
| 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 224:
Resilience and Reliable Network Design
Planning for large-scale infrastructure networks is with the objective of improving reliability and resilience. Over the last decades, a number of disasters have resulted in substantial losses of human, significant damage to property, and massive service interruptions for a number large infrastructure systems. The concepts reliability and resilience are now frequently used to characterize how well these infrastructure systems, their operators, and their users are prepared and capable to recover from disruptive events. In order to analyze a network, attention must be paid to three important aspects: First, the election of a failure model that is complex enough to capture the interaction between components but, at the same time, simple enough to calibrate with the available information. Second, study the performance of the network. This means that given a failure model, we need to develop methodologies to compute the performance of the network. Finally, the comparison of different network designs and to choose, according to a budget constraint, those which have a better performance. Natural disasters, such as earthquake and wildfires, can cause large blackouts and pose a challenge that a network should be able to overcome. Research into natural disaster impact on electric power systems is can help us understand the causes of the blackouts, explore ways to prepare and harden the grid, and increase the resilience of the power grid under such events. Discussion of how network design should address these challenges. Lectures, with regular weekly assignments and a study group for a final project. The target students include graduate and undergraduate students in MS&E, and other students on campus, including, for example, Civil and Environmental Engineering, ICME, and interested students at the Doerr School, among others. Prerequisite: probability such as 120, 220, or CEE 203. Recommended 121 or 221.
Terms: Win
| Units: 3
MS&E 226:
Fundamentals of Data Science: Prediction, Inference, Causality
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 228:
Applied Causal Inference with Machine Learning and AI (CS 288)
Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies. Prerequisites: basic knowledge of probability and statistics. Recommended: 226 or equivalent.
Terms: Win
| Units: 3
MS&E 231:
Social Algorithms
Learning algorithms play increasingly central roles within modern complex social systems. In this course, we examine the design and behavior of algorithms in such contexts, including search algorithms, content recommendation systems, social recommendation algorithms, feed ranking algorithms, content moderation algorithms, and more. The course has a split focus on the technical design of such algorithms, as well the literature on theoretical and empirical evaluations in the presence of network effects, strategic behavior, and algorithmic confounding. Prerequisites: training in applied statistics at the level of MS&E 125 or above, including experience coding in Python.
Terms: Aut
| Units: 3
MS&E 232:
Introduction to Game Theory
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.
Terms: Win
| Units: 3
MS&E 232H:
Introduction to Game Theory (Accelerated)
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.
Terms: Win
| Units: 3
MS&E 233:
Game Theory, Data Science and AI
The course will explore applied topics at the intersection of game theory, data science and artificial intelligence. The first part of the course will focus on computational approaches to solving complex games, with applications in developing successful algorithmic agents and explore recent successes in the games of Go, Stratego, Poker and Diplomacy. The lectures will provide the foundations of the methods that underlie these computational game theory methods (rooted in the theory of learning in games) and the assignments will explore implementation of simple variants. The second part of the course will explore the interplay between data science and mechanism design. We will overview topics such as optimizing auctions and mechanisms from data and explore applications in optimizing online auction markets. We will also overview methodologies for learning structural parameters in games and econometrics in games and how these can be used to analyze data that stem from strategic interactions, such as auction data. The third part of the course will explore topics that relate to deploying machine learning and data science pipelines in the presence of strategic behavior. Topics will include A/B testing in markets, with applications to A/B testing on digital platforms such as Uber, Amazon and other matching platforms.
Terms: Spr
| Units: 3
MS&E 236:
Machine Learning for Discrete Optimization (CS 225)
Machine learning has become a powerful tool for discrete optimization. This is because, in practice, we often have ample data about the application domain?data that can be used to optimize algorithmic performance, ranging from runtime to solution quality. This course covers how machine learning can be used within the discrete optimization pipeline from many perspectives, including how to design novel combinatorial algorithms with machine-learned modules and configure existing algorithms? parameters to optimize performance. Topics will include both applied machinery (such as graph neural networks, reinforcement learning, transformers, and LLMs) as well as theoretical tools for providing provable guarantees.
Terms: Spr
| Units: 3
MS&E 237A:
Bandit Learning: Behaviors and Applications (EE 277)
The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).
Terms: Aut
| Units: 3
MS&E 237B:
Reinforcement Learning: Behaviors and Applications (EE 370)
This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.
Terms: Win
| Units: 3
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.
Terms: Spr, Sum
| Units: 3
MS&E 241:
Economic Analysis (MS&E 141)
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: 111 or 211, and 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.
Terms: Spr
| Units: 3
MS&E 244:
Statistical Arbitrage
Practical introduction to statistical arbitrage, which typically refers to trading strategies that are bottom up, market neutral, with trading driven by statistical or econometric models. Models may focus on tendency of short term returns to revert, leads/lags among correlated instruments, volume momentum, or behavioral effects. A classic statistical arbitrage program is relatively high frequency over a large universe of stocks and is driven algorithmically. This course discusses a taxonomy of market participants and what motivates trading, data: different types, how to obtain data, timestamps, errors and dirty data, methods of exploring relationships between instruments, forecasting, portfolio construction across a large number of instruments, trading: the execution of portfolio changes in real markets, risks inherent in statistical arbitrage, nonstationarity of relationships due to changes in market regulations, fluctuations in market volatility and other factors, frictions such as costs of trading and constraints and how strategies scale, analysis of strategies. Prepares students with valuable skills for engaging in quantitative trading in a hedge fund or investment bank trading desk, understanding how to evaluate quantitative strategies from the point of view of an investor or asset allocator, including performance evaluation, risk analysis, and strategy capacity analysis. Occasional hands-on data projects supporting weekly topics. Weekly lectures and a final data-driven project. The objective of the final project is to build, test and analyze some kind of statistical arbitrage strategy. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Python or similar computational/statistical package.
Terms: Spr
| 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: Spr
| 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 248:
Blockchain and Crypto Currencies
Blockchain is one of the most significant technologies to impact law and business in many years. Blockchain is also one of the most interdisciplinary areas, bringing together new questions, and opportunities at the intersection of technology, business and law. This course is designed to employ this interdisciplinary nature, provide an overview of the technology behind blockchain, and explore current and potential real-world applications in technology, business and law. This is a lecture, discussion, and project-oriented class. Each lecture will focus on one of the topics, including a survey of the state-of-the-art in the area and in-depth discussion of the topic. Each week, students are expected to complete reading assignments before class and participate actively in class discussion.
Terms: Spr
| Units: 3
MS&E 249:
Corporate Financial Management (MS&E 146)
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. Limited enrollment. Recommended: 145, 245A, or equivalent.
Terms: Win
| Units: 3-4
MS&E 250A:
Engineering Risk Analysis
Techniques of analysis of risk management decisions in engineering and other systems involving preferences and trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis in the public or private sector (fault trees, event trees, systems dynamics); Bayesian updating and learning (elementary notions of quantum computing for complex cases); value of tests, economic analysis of failure consequences (human safety and long-term economic discounting); case studies such as space systems, nuclear power plants, medical systems and cyber security. Pre-requisites: probability, 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. Prerequisites: engineering risk analysis, decision analysis, or consent of instructor.
Terms: Spr
| Units: 3
| Repeatable
4 times
(up to 12 units total)
MS&E 252:
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. 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. How to assess the quality of decisions, the role of the decision analysis cycle, framing decisions, the decision hierarchy, biases in assessment, and uncertainty about probability. Sensitivity analysis, joint information, options, flexibility, assessing and using risk attitude, and decisions involving health and safety. Principles are applied to decisions in business, technology, law, and medicine. nPrerequisite: 220 or equivalent.
Terms: Win
| Units: 3-4
MS&E 254:
The Ethical Analyst
We raise awareness of ethically sensitive situations and provide principles and tools for forming coherent ethical judgments regarding individual, government, or organizational actions. Students learn ethical theories and tools from which they create their own personal ethical codes and test them against established ethical principles, class discussion, homework, class presentations, and situations from work and life. The course addresses personal life, human action and relations in society, technology, medicine, coercion, harming, stealing, imposition of risk, deception, and other ethical issues.
Terms: Spr, Sum
| Units: 3
MS&E 254A:
The Ethical Analyst
We raise awareness of ethically sensitive situations and provide principles and tools for forming coherent ethical judgments regarding individual, government, or organizational actions. Students learn ethical theories and tools from which they create their own personal ethical codes and test them against established ethical principles, class discussion, homework, class presentations, and situations from work and life. The course addresses personal life, human action and relations in society, technology, medicine, coercion, harming, stealing, imposition of risk, deception, and other ethical issues. Limited enrollment.
Terms: Spr
| Units: 1
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: Win
| Units: 3
MS&E 262:
Topics in Service and Supply Chain Management
This course will focus on topics in management of supply chains and services. The course will first discuss individual trade-offs and decisions faced by business such warehousing, transportation, revenue, and network design with emphasis on how to accommodate uncertainty. Next, it will explore decisions involved in supply chains and their impact on supply chain resiliency and performance. Finally, the course will discuss operational decisions faced by marketplaces such as controlling choice and managing revenue. The course will combine analytics to address trade-offs and discussions of practical cases. There will be some overlap with MS&E 260. There is no requirement to take MS&E 260.
Terms: Spr
| Units: 3
MS&E 263:
Healthcare Operations Management
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.
Terms: Win
| Units: 3
MS&E 264:
Healthcare Engineering
The healthcare industry, accounting for over 17% of the US GDP, stands at the forefront of rapid growth and innovation, offering vast opportunities and challenges for engineers. This course is specifically designed for graduate students and advanced undergraduate students in healthcare engineering and healthcare management, focusing on the pivotal role of data and management engineers in revolutionizing healthcare systems through the integration of advanced mathematical, economic, and managerial principles. The course covers innovative methods for designing experiments, modeling healthcare systems, leveraging big data amidst uncertainty, and specifically, delve into advanced techniques for anomaly detection in healthcare settings, identifying outliers that may indicate critical health trends or emergent crises. Through exploring these methodologies with applications from recent research to illustrate each concept, this course is structured to foster a collaborative learning environment, encouraging participants to contribute to the advancement of personalized medicine, evidence-based practices, and informed healthcare policymaking.
Terms: Spr
| Units: 3
MS&E 265:
Introduction to Product Management
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.
Terms: Win
| Units: 3
MS&E 271:
Global Entrepreneurial Marketing
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.
Terms: Aut
| Units: 3-4
MS&E 272:
Entrepreneurship without Borders
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.
Terms: Spr
| Units: 3-4
MS&E 273:
Venture Creation for the Real Economy (CEE 246)
CEE 246 is a unique course geared toward developing entrepreneurial businesses (both start-ups and internal ventures). This team, project-based class teaches students how to exploit emerging materials science, engineering and IT technologies to radically apply innovation to the real economy e.g., new products and services that produce real economic value for society as well as for the entrepreneurs. Areas of focus include: Sustainable Buildings and Infrastructure, Digital Cities and Communities, Clean Energy, Transportation and Logistics, Advanced Manufacturing, Digital Health Care, Web3.0, and Education. With one-on-one support from seasoned industry mentors and influential guest speakers, the course guides students through the three key elements of new venture creation: identifying opportunities, developing business plans, and determining funding sources. The class culminates with business presentations to industry experts, VCs and other investors. The goal is to equip students with the knowledge and network to create impactful business ideas, many of which have been launched from this class. To apply for this limited enrollment course, students must submit an application. Please visit the course website for additional information: https://cee.stanford.edu/venture-creation
Terms: Win
| Units: 3-4
MS&E 274:
Dynamic Entrepreneurial Strategy
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: Spr
| Units: 3
MS&E 275:
Secret Foundations of Scalable Startups
Explore the foundational, strategic, and experiential knowledge that entrepreneurs wish they had before building their company. Topics can be broken down into two core themes - how to build a scalable startup and how to be the founder of such a company. In discussion with venture capitalists, students learn how to build a company's foundation to position it for large scale growth. Then, in meeting and talking with expert founders, students understand the human impact of leading fast-paced, high-growth organizations. Primarily for graduate students. Limited enrollment.
Terms: Win
| Units: 3
MS&E 276:
Entrepreneurial Management and Finance
For graduate students only. Emphasis on managing high-growth, early-stage ventures, especially those with technology-intensive products and services. Students work in teams to develop skills and approaches necessary to becoming effective entrepreneurial leaders and managers. Key topics involve ethical decision-making when assessing risks, 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, navigating the trade-offs between control versus growth objectives, and handling adversity and failure. Limited enrollment with admission by an application for all matriculated students (full-time, part-time, and remote) due March 15th: https://forms.gle/Yfq1qbDpAUHC77Nu8. Admission results will be provided prior to start of quarter. Pre-requisite or Co-requisite: a college-level financial accounting course (e.g. MS&E 240) or equivalent.
Terms: Spr
| Units: 3
MS&E 277A:
Entrepreneurial Leadership
This Winter and Spring course sequence is part of the STVP Accel Leadership Program and explores how to lead entrepreneurial ventures including establishing startup strategy, forming organizational culture and effective team structures, securing resources, and building operating models that scale. Teams formulate a case study with a current startup CEO/senior executive that tackles a real-world business problem for their high-growth venture, and present the case on the challenge and the potential paths to resolution. The selection process for the Accel Leadership Program runs during the Autumn fall quarter each year; applications are available at https://stvp.stanford.edu/students.
Terms: Win
| Units: 2-3
MS&E 277B:
Entrepreneurial Leadership
This Winter and Spring course sequence is part of the STVP Accel Leadership Program and explores how to lead entrepreneurial ventures including establishing startup strategy, forming organizational culture and effective team structures, securing resources, and building operating models that scale. Teams formulate a case study with a current startup CEO/senior executive that tackles a real-world business problem for their high-growth venture, and present the case on the challenge and the potential paths to resolution. The selection process for the Accel Leadership Program runs during the Autumn fall quarter each year; applications are available at https://stvp.stanford.edu/students.
Terms: Spr
| Units: 2-3
MS&E 278:
Patent Law and Strategy for Innovators and Entrepreneurs (ENGR 208)
This course teaches the essentials for a startup founder to build a valuable patent portfolio and avoid a patent infringement lawsuit. Jeffrey Schox and Diana Lin are partners at Schox Patent Group, which is the law firm that wrote the patents for Coinbase, Cruise, Duo, Joby, Twilio and 500+ other startups that have collectively raised over $10B in venture capital. This course, which was previously called ME 208, is appropriate for students with any engineering background. For those students who are interested in a career in Patent Law, please note that this course is a prerequisite for ME238 Patent Prosecution. There are no prerequisites for this course, but the student must be at the senior or graduate level.
Terms: Aut
| Units: 2-3
MS&E 279:
Disruptive Innovations in New Globalization Era
The pandemic and geopolitics present a new inflection point that all industries and countries need to manage properly in order to survive the crisis and create new opportunities for growth. The globalization structure that we have taken for granted in the past fifty years is gone and a new globalization structure is slowly emerging. Instead of global supply chains and global markets, we may have strong regional supply chains and regional markets and weak connections between regions. It is not clear what the final structure will be, but one thing for sure is that the dynamic formation of the new globalization structure will be shaped by how companies and countries respond and manage the new inflection point through disruptive innovations. In this new globalization era, we need to re-think innovation factoring the unquantifiable pandemic and geopolitical risk into product development and business expansion decisions. For emerging technology businesses like clean energy, one needs to develop a resilient supply chain structure that would provide a proper balance between cost and risk exposure to unexpected disruption due to pandemic and geopolitics. For an established industry, like semiconductor, there will be new risk exposure in the current supply chain structure. New supply chain structures will emerge as companies respond to the disruptions caused by pandemics and geopolitics. We discuss the possible changes in the supply chain structure and how companies in the related industries should establish proper risk management policies and procedures to increase the chance of successfully managing the inflection point and creating new opportunities for their growth. To support developing a resilient supply chain, we identify new 0-1 innovation opportunities and discuss the important role that government can play in this new changing era that would shape the structure of new globalization and spur new national economic growth. We pick the following specific industries to focus our discussions: semiconductor, clean energy, mobile communication, robotics and AI.
Terms: Spr
| Units: 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. Limited enrollment.
Terms: Aut
| Units: 3-4
MS&E 284:
Managing Data Science Organizations for Innovation and Impact
Most organizations are drawn to data science by the tantalizing prospects of competitive advantage and disruptive capabilities. Yet many organizations are finding that their data science teams are not providing the expected business impact, and some are beginning to question the ROI of these teams altogether. This course works to bridge the gap between the technical training that data scientists spend years mastering and the role they must play in their companies to successfully drive business impact. Drawing on inside accounts, case studies, and academic research, this course identifies the key capabilities that data science teams and their business partners must develop to successfully drive business impact. We explore how impactful data science teams have made a fundamental shift toward business understanding and impact accountability, even while ensuring that their statistics are pristine. This course lays out a practical "how to" guide for designing and enabling impact-driven data science teams, including templates and exercises for applying these practical insights in any organizations. Limited enrollment.
Terms: Win
| Units: 3
MS&E 292:
Health Policy Modeling (HRP 293)
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 296:
Technology, Innovation and Great Power Competition (INTLPOL 340)
This course explores how new technologies pose challenges and create opportunities for the United States to compete more effectively with rivals in the international system with a focus on strategic competition with the People's Republic of China. In this experiential policy class, you will address a priority national security challenge employing the "Lean" problem solving methodology to validate the problem and propose a detailed technology informed solution tested against actual experts and stakeholders in the technology and national security ecosystem. 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.
Terms: Aut
| Units: 4
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.
Terms: Spr
| Units: 3-5
MS&E 298:
Detecting Discrimination with Data (CSRE 298)
What does it mean for a decision-making process to be discriminatory? How do we quantify inequality? What steps can be taken to mitigate potential bias? This hands-on course explores legal and statistical conceptions of discrimination using examples from public policy, healthcare, economics, technology, and education. Each session will consist of an interactive lecture, a live coding session where we implement techniques from the lecture, and a research paper discussion. The course also features occasional guest speakers from industry and academia. Prerequisites: An introductory statistics course (e.g., 120, 125, 226, or CS 109) and an introductory programming course (e.g., CS 106A). Graduate students may enroll for 1 unit.
Terms: Aut
| Units: 1-2
MS&E 301:
Dissertation Research
Prerequisite: doctoral candidacy.
Terms: Aut, Win, Spr, Sum
| Units: 1-10
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Hinds, P. (PI);
Johari, R. (PI);
Karunakaran, A. (PI);
Katila, R. (PI);
Lo, I. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Shachter, R. (PI);
Sheares, A. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Syrgkanis, V. (PI);
Tse, E. (PI);
Udell, M. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Vitercik, E. (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:
Optimization Algorithms (CME 334, CS 369O)
Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure. Prerequisite: linear algebra, multivariable calculus, probability, and proofs.
Terms: Win
| Units: 3
MS&E 314:
Optimization in Data Science and Machine Learning
Optimization in Data Science and Machine Learning
Terms: Win
| Units: 3
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.
Terms: Spr
| Units: 3
MS&E 323:
Stochastic Simulation
Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.
Terms: Win
| Units: 3
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 328:
Foundations of Causal Machine Learning
Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics may include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and double/debiased machine learning, theoretical foundations of high-dimensional linear regression, theoretical foundations of non-linear regression models, such as random forests and neural networks, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation, adaptive experimentation and inference.
Terms: Aut
| Units: 3
MS&E 334:
Topics in Social Data
This Ph.D. course will study advanced topics in causal inference, with a focus on nuances of experimental design and policy evaluation, particularly in settings with interference. We will emphasize discussion of a range of experimental designs, as well as applications in networks and marketplaces. The course will be taught in a seminar format, with an emphasis on in-depth discussion of recent research papers at the frontiers of this area. The class is restricted to Ph.D. students; exceptions require instructor approval.
Terms: Spr
| Units: 3
MS&E 335:
Queueing and Scheduling in Processing Networks
Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.
Terms: Aut
| Units: 3
MS&E 336:
Computational Social Choice (CS 366)
An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Win
| Units: 3
MS&E 337:
Large Networks and Graph Limits
Random graph theory, Erdos-Renyi, and other network models, the algebra of graph homomorphisms, limits for dense and sparse graphs, and applications in algorithm design, graph representation learning, and others.
Terms: Aut
| Units: 3
MS&E 338:
Aligning Superintelligence
Within a couple of decades, or less, it is plausible that humans will create an AI that is much smarter than humans in practically all domains of human activity. We refer to such an AI as a superintelligence. The alignment problem is how to make sure that such a superintelligence acts according to its creator's intent. This course is intended for a technical audience interested in thinking about this problem. Prerequisites: one graduate-level machine learning course and one course that studies agents (e.g., AI, RL, decision analysis, economics).
Terms: Spr
| Units: 3
| Repeatable
4 times
(up to 12 units total)
MS&E 346:
Foundations of Reinforcement Learning with Applications in Finance (CME 241)
This course is taught in 3 modules - (1) Markov Processes and Planning Algorithms, including Approximate Dynamic Programming (3 weeks), (2) Financial Trading problems cast as Stochastic Control, from the fields of Portfolio Management, Derivatives Pricing/Hedging, Order-Book Trading (2 weeks), and (3) Reinforcement Learning Algorithms, including Monte-Carlo, Temporal-Difference, Batch RL, Policy Gradient (4 weeks). The final week will cover practical aspects of RL in the industry, including an industry guest speaker. The course emphasizes the theory of RL, modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of programming exercises. No pre-requisite coursework expected, but a foundation in undergraduate Probability, basic familiarity with Finance, and Python programming skills are required.
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 365:
Topics in Market Design (ECON 287)
Market design is a field that links the rules of the of the marketplace to understand frictions, externalities and more generally economic outcomes. The course provides theoretical foundations on assignment and matching mechanisms as well as mechanism design. Emphasis on theories at the intersection of economics, CS and operations as well as applications that arise in labor markets, organ allocation, platforms. Exposes students to timely market design challenges. Guest lectures and a research project. The class offers an opportunity to begin a research project. Students read and critique papers and write and present a final paper.
Terms: Win
| 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: 1-3
| Repeatable
for credit
MS&E 385:
Doctoral Seminar in Race and Ethnicity
What is the difference between race and ethnicity? In what ways can we theorize the difference (if it exists)? How does modern racism work? And how does immigration change a nation's racial landscape? This graduate course surveys classic and contemporary writings on race and ethnicity mainly within the sociological tradition. We begin with Weber and some non-canonized classics, including the works of W.E.B. DuBois and Franz Fannon to understand how the study of race and ethnicity emerged in Social Science as a contrast to the biological determinist scholarship of the time. We pay attention to the way that social scientists emphasized the role of culture, structure, and status. From there we proceed to examine the more contemporary arguments, including uncovering the various mechanisms that undergird the (re)production or transformation of racial and ethnic boundaries. We spend time examining the literature on inequality and questions about the significance of race and racism. In addition, we assess how assimilation and racialization developed over time. We then spend time thinking about how to consider race and ethnicity in research designs. Finally, we end with looking towards the future, including how technology is changing modern conceptualizations of race and the potential of policy to mitigate the effects of systemic racism.
Terms: Win
| Units: 1-3
MS&E 386:
Doctoral Research Seminar on Technology & Organizations (SOC 360)
Doctoral Research Seminar on Technology & Organizations
Terms: Spr
| Units: 1-3
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.
Terms: Aut
| Units: 1-3
MS&E 390:
Doctoral Research Seminar in Health Systems Modeling (HRP 390)
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 394:
Advanced Methods in Modeling for Climate and Energy Policy
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.
Terms: Spr
| Units: 3
MS&E 408:
Directed Reading and Research
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
Terms: Aut, Win, Spr, Sum
| Units: 1-10
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Byers, T. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Hinds, P. (PI);
Johari, R. (PI);
Karunakaran, A. (PI);
Katila, R. (PI);
Lo, I. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Shachter, R. (PI);
Sheares, A. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Syrgkanis, V. (PI);
Tse, E. (PI);
Udell, M. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
MS&E 447:
Blockchain Technologies & Entrepreneurship
This course offers a concise, in-depth exploration of entrepreneurship in decentralized computing, focusing on the rapid advance of decentralized blockchain technology since Bitcoin's release in 2009. We'll examine relevant technological advancements and their market opportunities in finance, AI, social media, gaming, and open computing. Discussions will differentiate lasting innovations from transient trends, helping students sort real advances from headline-grabbing volatility, speculation, and fraud. The course features guest speakers from top blockchain startups and venture capital firms, fostering actionable real-world insights. Key topics include blockchain foundations, emerging trends in scalable infrastructure, AI, verifiable computation, Decentralized Finance (DeFi), Real World Assets (RWA), decentralized governance (e.g. DAOs), and Decentralized Physical Infrastructure (DePIN). The course will equip students with foundational knowledge for potential entrepreneurial ventures based on distributed computing.
Terms: Spr
| Units: 1
| Repeatable
12 times
(up to 12 units total)
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
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 (PEDS 463)
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.
Terms: Spr
| Units: 3-4
MS&E 472:
Entrepreneurial Thought Leaders' Seminar
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.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 478:
Ases Breakthrough
Eight-week long program designed to help audacious builders and aspiring VCs from across educational backgrounds (undergraduates, masters, PhD) break into the entrepreneurial world. We help students identify, evaluate, and capitalize on venture opportunities. Strong emphasis on project-based work, relationship-building, and international presence in venture capital, with the cohort split into teams, each assigned to a VC mentor to help with hands-on projects involving identifying entrepreneurial talent, world-building, due diligence, and more. Each VC mentor will have a concentration in an industry of their choice. This will help guide our students to focus on a series of two-week projects within a particular domain. It will be taught this Spring.
Terms: Spr
| Units: 1
| Repeatable
5 times
(up to 5 units total)
MS&E 489:
Leadership Lab (DESIGN 368, ME 368)
The Leadership Lab (previously known as d.Leadership) is a one-of-a-kind hands-on leadership course. This course bridges leadership research and principles with real-world application, offering a unique opportunity to grasp not only the theory but also the practical application of leadership. Real Application: Embrace a dynamic learning environment where theory meets practice. You will apply a wide range of leadership capabilities and skills within real, live teams and environments - all with instruction along the way. Experiment with your Leadership Style: We believe your leadership style is something you must prototype and iterate throughout your life. This course creates a safe environment where you can practice new leadership techniques without worrying about your reputation or next performance review in a real work environment. As you practice new techniques, you will undoubtedly experience highs and lows and most importantly refine your own leadership point of view. Key Topic Areas: Leveraging Failure and Learning to Pivot; Leading with Influence in the Absence of Authority; Framing Projects with Purpose in Order to Drive Momentum; and Subtracting Friction in Organizational Change. By the end of this course, you will have enhanced and transformed your leadership capabilities, found your natural strengths, enhanced them, and explored new horizons. Join us and experience a leadership journey that is both inspiring and hands-on. Preference to graduate students and students who have previously taken MS&E 280 or equivalent (not a prerequisite). Reach out to the teaching team with questions. Admission by Application https://forms.gle/B4sFZxjTaN4fFvRQ9 due 5pm on March 22, 2024.
Terms: Spr
| Units: 3-4
MS&E 494:
The Stanford Energy Seminar (CEE 301, ENERGY 301)
Interdisciplinary exploration of current energy challenges and opportunities in the context of development, equity and sustainability objectives. Talks are presented by faculty, visitors, and students and include relevant technology, policy, and systems perspectives. More information about the seminar can be found on the website https://energyseminar.stanford.edu/May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
MS&E 802:
TGR Dissertation
For doctoral students in Terminal Graduate Residency (TGR) status only.
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Ashlagi, I. (PI);
Bambos, N. (PI);
Blanchet, J. (PI);
Brandeau, M. (PI);
Eesley, C. (PI);
Eisenhardt, K. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Hinds, P. (PI);
Johari, R. (PI);
Karunakaran, A. (PI);
Katila, R. (PI);
Lo, I. (PI);
Pate-Cornell, E. (PI);
Pelger, M. (PI);
Saberi, A. (PI);
Shachter, R. (PI);
Sheares, A. (PI);
Sidford, A. (PI);
Sutton, R. (PI);
Sweeney, J. (PI);
Syrgkanis, V. (PI);
Tse, E. (PI);
Udell, M. (PI);
Ugander, J. (PI);
Valentine, M. (PI);
Van Roy, B. (PI);
Vitercik, E. (PI);
Weyant, J. (PI);
Ye, Y. (PI)
