Results for MS&E

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E 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.

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.

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

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.

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.

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.

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).

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.

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.

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.

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

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.

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.

Prerequisite: doctoral candidacy.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

For doctoral students in Terminal Graduate Residency (TGR) status only.