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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
Instructors: ; Weyant, J. (PI)

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 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 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 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 130: Information Networks and Services

Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer-to-peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisites: 111, 120, and CS 106A.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci

MS&E 135: Networks

This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".
Terms: Win | 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)
Instructors: ; Borland, L. (PI)

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 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 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 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 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 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
Instructors: ; Barrera Martinez, J. (PI)

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 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
Instructors: ; Lo, I. (PI); Murthy, A. (TA)

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 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 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 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 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 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 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 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 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 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 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
Instructors: ; Byers, T. (PI)

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 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
Instructors: ; Udell, M. (PI)

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
Instructors: ; Ye, Y. (PI)

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
Instructors: ; Glynn, P. (PI)

MS&E 336: Computational Social Choice (CS 366)

An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Win | Units: 3
Instructors: ; Goel, A. (PI)

MS&E 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
Instructors: ; Rao, A. (PI); Zanotti, G. (TA)

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
Instructors: ; Ashlagi, I. (PI)

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
Instructors: ; Katila, R. (PI)

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
Instructors: ; Sheares, A. (PI)

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
Instructors: ; Brandeau, M. (PI)

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

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
Instructors: ; Cahan, B. (PI); Somda, F. (TA)

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
Instructors: ; Shachter, R. (PI)

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 494: The Stanford Energy Seminar (CEE 301, ENERGY 301)

Interdisciplinary exploration of current energy challenges and opportunities, with talks by faculty, visitors, and students. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit
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