The department’s engineering research strength is integrated with its educational program at the undergraduate, master’s, and doctoral levels: graduates of the program are trained as engineers and future leaders in technology, policy, and industry. How to organize the decision conversation, the role of the decision analysis cycle and the model sequence, assessing the quality of decisions, framing decisions, the decision hierarchy, strategy tables for alternative development, creating spare and effective decision diagrams, biases in assessment, knowledge maps, uncertainty about probability. Research and teaching activities are complemented by an outreach program that encourages the transfer of ideas to the environment of Silicon Valley and beyond. Successive approximation, policy improvement, and linear programming methods. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. Prerequisites: MATH 113, 115; Markov chains; linear programming. Decision Analysis II: Professional Decision Analysis. Sensitivity analysis, approximations, value of revelation, joint information, options, flexibility, bidding, assessing and using corporate risk attitude, risk sharing and scaling, and decisions involving health and safety. Applicants admitted to the doctoral program, who have indicated on their application that they would like to be considered for financial aid, are automatically considered for these assistantships and fellowships. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Combinatorial and mathematical programming techniques to derive approximation algorithms for NP-hard optimization problems. New and returning master's students may apply for course assistantships each quarter, but priority is given to MS&E doctoral students. prepares engineers for a lifelong career addressing the critical technical and managerial needs of private and public organizations. Topics: Basic Algebraic Graph Theory, Matroids and Minimum Spanning Trees, Submodularity and Maximum Flow, NP-Hardness, Approximation Algorithms, Randomized Algorithms, The Probabilistic Method, and Spectral Sparsification using Effective Resistances. We traditionally think of algorithms as running on data available in a single location, typically main memory. The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Prossible topics include: greedy algorithms for vertex/set cover; rounding LP relaxations of integer programs; primal-dual algorithms; semidefinite relaxations. The major prepares students for a variety of career paths, including investment banking, management consulting, facilities and process management, or for graduate school in industrial engineering, operations research, business, economics, law, medicine, or public policy . Teaches creativity tools using workshops, case studies, field trips, expert guests, and team design challenges. For those students who are interested in a career in Patent Law, please note that this course is a prerequisite for ME238 Patent Prosecution. See also the department's undergraduate Learning Outcomes for additional learning objectives. For graduate students only, with a preference for engineering and science majors.
Through the core, students in the program are exposed to the breadth of faculty interests, and are in a good position to choose an area during the junior year. What is entrepreneurial leadership in a venture that spans country borders? Explore the foundational and strategic elements needed for startups to be designed for "venture scale" at inception. Information about loan programs and need-based aid for U. citizens and permanent residents can be obtained from the Financial Aid Office. The program emphasizes developing analytic abilities, making better decisions, developing and executing strategies while also leading people who innovate. Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Topics will be illustrated with applications from Distributed Computing, Machine Learning, and large-scale Optimization. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Iterative methods for linear equations and least squares. Recommended: MS&E 310, 311, 312, 314, or 315; CME 108, 200, 302, 304, 334, or 335. Unlike an MBA, our master’s program addresses the technical as well as the behavioral challenges of running organizations and complex systems. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research. This practice-based experiential lab course is geared toward MS&E masters students. Design and application of formal analytical methods in climate policy development. Design and application of formal analytical methods for policy and technology assessments of energy efficiency and renewable energy options. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. Algorithms include the interior-point, barrier function, and cutting plane methods. Prerequisites: CS 261 is highly recommended, although not required. Paradigms such as map-reduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. Other graduates make careers tackling the problems faced by local, national, and international governments by developing new healthcare systems, new energy systems and a more sustainable environment. The personal, team-based and organizational skills needed to become a transformative leader. Themes include: personal transformation; the inside-out effect, positive intelligence, group transformation; cross-functional teams; re-engineering; rapid - non-profit and for profit - organizational transformation; and social transformation. Guest speakers from industry will present real-world challenges related to class concepts. The application of mathematical, statistical, economic, and systems models to problems in health policy. Topics include the 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. Startups operate with continual speed and urgency 24/7. Hacking for Diplomacy: Tackling Foreign Policy Challenges with the Lean Launchpad. At a time of significant global uncertainty, diplomats are grappling with transnational and cross-cutting challenges that defy easy solution including: the continued pursuit of weapons of mass destruction by states and non-state groups, the outbreak of internal conflict across the Middle East and in parts of Africa, the most significant flow of refugees since World War II, and a changing climate that is beginning to have impacts on both developed and developing countries. Topics: efficacy and ethics; use rights for property; contracts and torts; spontaneous order and free markets; crime and punishment based on restitution; guardian-ward theory for dealing with incompetents; the effects of state action-hypothesis of reverse results; applications to help the needy, armed intervention, victimless crimes, and environmental protection; transition strategies to a voluntary society. Fundamental Concepts in Management Science and Engineering. Each course session will be devoted to a specific MS&E Ph D research area. Variants of the simplex method and the state of art interior-point algorithms. Elements of convex analysis, first- and second-order optimality conditions, sensitivity and duality. Topic chosen in first class; different topics for individuals or groups possible. The major problems of the day demand an ability to integrate the technical, social and economic ways of thinking. degree in Management Science and Engineering (MS&E) is outlined in the School of Engineering section of this bulletin; more information is contained in the School of Engineering’s . Limited enrollment; preference to graduate students. Students will complete a quarter-long project designing and managing an actual online organization. Areas include: disease screening, prevention, and treatment; assessment of new technologies; bioterrorism response; and drug control policies. 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. While the traditional tools of statecraft remain relevant, policymakers are looking to harness the power of new technologies to rethink how the U. government approaches and responds to these and other long-standing challenges. Advanced students will make presentations designed for first-year doctoral students regardless of area. Sensitivity analyses, economic interpretations, and primal-dual methods. Algorithms for unconstrained optimization, and linearly and nonlinearly constrained problems.