101 - 110 of 164 results for: MS&E

This project-based course provides a focused, collaborative environment and leverages the resources of Stanford's Gordian Knot Center for National Security Innovation to empower student teams to address critical defense and national security challenges. Students apply as a team with the national security challenge they aim to address. Teams take a hands-on approach requiring close engagement with government agency end-users. Course time: no scheduled meeting times - to be arranged by teams with instructors. An in-person orientation will be held at the Gordian Knot Center during the first week of the quarter.

This experiential learning course engages students across disciplines in the real-world dynamics of how emerging technologies - AI, semiconductors, biotech, quantum science, space, cyber, and more - are reshaping international competition. Centered on strengthening the strategic positions of the United States and its allies and partners, student teams take on urgent policy challenges with global implications. The course fuses AI tools, stakeholder interviews, and entrepreneurial approaches to help teams move faster and generate solutions that matter. Over 10 weeks, students work in teams to develop and publish a policy report aimed at informing current debates and decisions. In-person attendance is required. Teams must also hold weekly office hours with instructors, scheduled outside regular class sessions. Students enrolled for 5 units will go further by crafting an influence strategy - identifying how to move their work into real policy conversations and decision-making channels.

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.

This experiential learning course explores why entrepreneurial efforts inside governments succeed or fail. For students interested in entrepreneurship, this setting offers one of the most demanding environments for building and scaling new organizations. This topic matters because government innovation shapes public outcomes, national security, and long-term technological competitiveness. Over the last decade, the U.S. government has created more than 100 innovation organizations such as digital service teams and rapid capability offices to advance emerging technologies. Yet while some of these "government startups" achieve meaningful breakthroughs, others stall without widely understood reasons, revealing how little is known about entrepreneurship inside government. Students learn how to overcome organizational inertia, build legitimacy, navigate resource dependencies, establish effective governance, and structure organizations that can adapt and pivot. Together, these principles form a practical framework for improving performance. Each team studies a real government exploratory unit, essentially a startup inside the public sector, and explains its performance using course concepts. Student teams conduct rigorous research grounded in external interviews and produce publicly available reports with recommendations for relevant agencies. The course is intended for graduate and undergraduate students interested in entrepreneurship, national security, public policy, emerging technologies, and innovation. No prior government experience is required. Admission is by application.

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.

Optimization entails seeking decisions that maximize objectives while satisfying constraints, with applications across engineering, business, economics, statistics, data analysis, and everyday life. This course provides an in-depth and rigorous introduction to mathematical optimization, covering how to formulate, analyze, and solve real-world problems using modern optimization theory and software. Topics include finite-dimensional linear optimization problems with continuous and discrete variables, sensitivity and duality, basic elements of convex analysis, first- and second-order optimality conditions for nonlinear optimization problems, and a discussion of important algorithmic and computational aspects related to optimization. Prerequisites: MATH 113, 115, or equivalent.

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.