Faculty List by Research Area

Professor Ron Howard defined the field of decision analysis in 1964, and continues to lead research at Stanford. Although many people make personal decisions instinctively and haphazardly, a more serious approach essential for management is to analyze the problem as a three-legged structure of preferences, alternatives, and available information in the context of a frame.

The mission of the Engineering Risk Research Group led by department Professor M. Elisabeth Pate-Cornell, is the analysis, mathematical modeling, and management of the safety of engineering systems using probabilistic methods and systems analysis. Recently, the members of the ERRG have extended the application of these methods to the domains of medical operations and devices, national security, and the strategy of firms in the financial industry with the objective of identifying the most cost effective risk reduction measures within these complex systems. Technical, organizational, and strategic solutions are considered and decision analysis is often used to make the final choice among a spectrum of risk mitigation options.

Through systematic analysis and application of sophisticated mathematical tools, engineers make vital contributions to understanding commodities, credit, currencies, derivatives, equities, pricing, profits, resources and other core economic and financial concepts. Research and teaching in the department cover topics ranging from investments, economic growth, natural resources and energy, entrepreneurship and microeconomics.

The design and operation of networks requires technical concepts such as information theory, algorithms and optimization, but also depends on economic, social and even political factors. One example is the concept of net neutrality, in which infrastructure providers seeking to recoup their investments by charing differently based on bandwidth consumption, come into conflict with users who oppose making economic distinctions related to information content. The department has deep expertise in all facets of these and other problems of technology implementation and usage in the real world.

An optimization problem involves finding values of the variables that minimize or maximize an objective function while satisfying constraints. For example, how might a meat packer running a supply chain minimize fuel use given the constraint of needing to get a ton of hamburger to 50 customer warehouses by 6 a.m. each day.

The Systems Optimization Laboratory conducts basic and applied research on large-scale mathematical programming, including algorithmic development, model formulation, and software production. The field has a strong intersection with scientific computing and the director, Professor Walter Murray, is also a director of the Institute for Computational and Mathematical Engineering.

Research spans the study of technical work; technology's effects on individuals and teams; the formation and growth of entrepreneurial firms; and strategy and innovation in technology-based firms. Some projects examine the role of technology in the work of engineers, including the interplay between workplace technologies and engineering knowledge, on-the-job learning, problem-solving and coordination. Other research investigates the dynamics of globally distributed work teams; the implications of contracting and outsourcing; human-robot interaction in the workplace; and evidence-based management. At the firm-level, research examines how entrepreneurial firms gain financing, build alliance networks, and grow. Other investigations center on established firms, including creating successful R&D collaborations across businesses, effectively competing against other firms, and entering new markets. Researchers also study strategies that enable established organizations to discover, develop and commercialize technologies. The department is home to a premier teaching programs in these areas including Stanford Technology Ventures Program and the Work Technology and Organizations, WTO is a world-class center dedicated to understanding how work is changing and how to design more effective organizations and technologies.

In many complex systems, randomness plays an integral part in the outcome of an event. The likelihood of a system to produce an event is based on the initial state and range of parameters. Results are predicted and described by a number of mathematical methods in this academic field, including: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems, discrete and continuous time parameter Markov chains, queuing theory, inventory theory, and simulation.

Production and Operations Management involves the development, manufacturing, and distribution of products and the provision of services in the modern global economy. Problems studied involve scheduling, inventory control, supply chain coordination and contracting, product development, operations strategy, and "green" or environmentally friendly sustainable systems. Underlying methodologies used include mathematical modeling (both deterministic and stochastic), game theory, economic analysis and simulation.

Research and teaching in this area focus on the design and analysis of public policies and corporate strategies, especially those with technology-based issues. It features grounding in microeconomics and modeling approaches. Courses with a policy focus include such topics such as national security, energy and environment, and health care and courses with a strategy focus in topics such as entrepreneurship, innovation, and product development.