Results for MS&E

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Prerequisite: doctoral candidacy.

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.

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.

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.

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).

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.

Doctoral Research Seminar on Technology & Organizations

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.

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.

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

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.

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

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.

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.

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.

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.

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.