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Students complete core courses in accounting, computer science, economics, ethics, organizational theory, mathematical modeling, optimization, probability, and statistics. 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. To personalize their exploration, students select additional courses from different areas of the department, with greater emphasis in one of them. Application to queueing theory, storage theory, reliability, and finance. 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. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Key results on single queues and queueing networks. Dynamic routing and scheduling in processing networks. We will review recent research that aims to both understand and design such markets. Prerequisites: Mathematical maturity; 300-level background in optimization and probability; prior exposure to game theory. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents. Research and teaching activities are complemented by an outreach program that encourages the transfer of ideas to the environment of Silicon Valley and beyond. The basic limit theorems of probability theory and their application to maximum likelihood estimation. Markov chains and processes, random walks, basic ergodic theory and its application to parameter estimation. Current stochastic models, motivated by a wide range of applications in engineering, business, and science, as well as the design and analysis of associated computational methods for performance analysis and control of such stochastic systems. In this course, we analyze recent court decisions, discuss methods from machine learning and game theory, and examine the often subtle relationship between law, public policy, and statistics. This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Emphasis on mathematical modeling and methodology, with a view towards preparing Ph. Projects involving the practical application of optimization under uncertainty to financial planning. Close associations with other engineering departments and with industry enrich the programs by providing opportunities to apply MS&E methods to important problems and by motivating new theoretical developments from practical experience. Students with a background in statistics, computer science, law, and/or public policy are encouraged to participate. The class will include a theoretical project and a paper presentation. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. MS&E’s programs also provide a basis for contributing to other areas such as biotechnology, defense policy, environmental policy, information systems, and telecommunications. Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the Hamilton-Jacobi-Bellman equation, and statistical inference of SDEs. Enrollment is limited, and project teams will be selected during the first week of class. Prerequisites: CS 261 or equivalent; understanding of basic game theory. Students are expected to be able: MS&E offers programs leading to the degrees of Master of Science and Doctor of Philosophy. Applicants for admission as graduate students in MS&E must submit the results of the verbal, quantitative, and analytical parts of the Graduate Record Examination. 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 deadline for application to the doctoral program is December 5, 2017, and the deadline for application to the master's program is January 16, 2018. 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.
See also the department's undergraduate Learning Outcomes for additional learning objectives. Is Silicon Valley-style entrepreneurship possible in other places? Open to graduate students interested in technology driven start-ups. How entrepreneurial strategy focuses on creating structural change or responding to change induced externally. Themes include controversial and disruptive insights, competitive analysis, network effects, organizational design, and capital deployment.
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. 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.
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. 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. In this class, student teams will take actual foreign policy challenges and learn how to apply lean startup principles, ("mission model canvas," "customer development," and "agile engineering¿) to discover and validate agency and user needs and to continually build iterative prototypes to test whether they understood the problem and solution. Team applications required at the end of shopping period. 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. 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. Relaxations of harder optimization problems and recent convex conic linear programs. Modern applications in communication, game theory, auction, and economics.
The program builds on the foundational courses for engineering, including calculus, mathematical modeling, probability, statistics, engineering fundamentals, and physics or chemistry. Practical introduction to financial risk analytics. The focus is on data-driven modeling, computation, and statistical estimation of credit and market risks. How does an entrepreneur act differently when creating a company in a less-developed institutional environment? Provides the experience of an early-stage entrepreneur seeking initial investment, including: team building, opportunity assessment, customer development, go-to-market strategy, and IP. 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. Case studies, expert guests, and experiential learning projects will be used.
Students interested in a minor should see the Minor tab in this section. See the “Mathematical and Computational Science” section of this bulletin. Case studies based on real data will be emphasized throughout the course. Students, individually or in groups, choose, define, formulate, and resolve a real risk management problem, preferably from a local firm or institution. Scope of the project is adapted to the number of students involved. Learn through forming teams, a mentor-guided startup project focused on developing students' startups in international markets, case studies, research on the international aspects of the entrepreneurial process, and networking with top entrepreneurs and venture capitalists who work across borders. Teaching team includes serial entrepreneurs and venture capitalists. 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.
This is precisely what the department educates its students to do. Students are encouraged to plan their academic programs as early as possible, ideally in the freshman or sophomore year. For graduate students only, with a preference for engineering and science majors.