# INFORMS Recognizes IBMer with John von Neumann Theory Prize

 John von Neumann (wikipedia)
In the 1940s, John von Neumann developed the computer architecture that established the design, as well as the programming and theories, for how modern computers operate to this day. His work has inspired many others to build and innovate on top of these concepts. He also laid the foundation of game theory, and did research in physics, biology and statistics.
To recognize his contributions, the Institute for Operations Research and the Management Sciences (INFORMS) annually awards a “scholar who has made fundamental, sustained contributions to theory in operations research and the management sciences” with the John von Neumann Theory Prize. This year’s award went to Nimrod Megiddo of IBM Research – Almaden.
INFORMS cites that prize is for “fundamental contributions across a broad range of areas of operations research and management science, most notably in linear programming, combinatorial optimization, and algorithmic game theory.”
Linear Programming, a method to achieve the best outcome in a mathematical model whose requirements are represented by linear relationships, is applied across engineering, business and economics for modeling transportation, energy, telecommunication, manufacturing, agriculture, and military systems. More generally, Operations Researchis a discipline that deals with the application of advanced analytical methods to help make better decisions. It seeks to improve the performance of systems by optimizing their designs and operational decision making.
“[Megiddo’s] work on the probabilistic analysis of the simplex method, alone and with Adler, established some of the most important results in the area, including the best (quadratic) bound for the complexity of a complete parametric pivoting method and an explanation of why this is possible for the lexicographic version but not the standard Lemke perturbation.”
[He] was an early leader in the theory of interior-point methods, and laid the framework for the development of primal-dual methods in his seminal paper on pathways to the solution.”
Megiddo’s work has also played a role in highlighting the question, still unresolved today, of whether there is a strongly polynomial algorithm for linear programming.”
In algorithmic game theory, Megiddo has done ground breaking work that anticipated by two decades the more recent blossoming of the field.”
Megiddo joined IBM in 1984. His research is in optimization theory, machine learning, and game theory, with applications to database systems, storage systems, and most-recently to IBM Watson User Modeling Service and neuromorphic computing.
Megiddo also taught various courses in operations research, statistics, and game theory at Tel Aviv University, Northwestern University’s Kellogg Graduate School of Management, Carnegie Mellon University’s Tepper School of Business, and Stanford University. He holds 58 patents. Megiddo is the editor-in-chief of Discrete Optimization, a former editor-in-chief of Mathematics of Operations Research, and a member of the editorial boards of several other research journals.

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