For contributions to the field of algorithms—including foundational new methods in optimization, approximation algorithms, and algorithmic game theory—Éva Tardos, Jacob Gould Schurman Professor of Computer Science at Cornell, and winner of the IEEE’s John von Neumann Medal, will be honored “for outstanding achievements in computer-related science and technology” at The IEEE Vision, Innovation, and Challenges Summit (IEEE VIC Summit) held on May 17, 2019 at the Marriott Marquis San Diego Marina in San Diego, California.

As the conference organizers describe the event: “The IEEE VIC Summit brings together leading innovators, visionaries, and disruptors in technology to discuss, explore, and uncover what is imminent, what is possible—and what these emerging technologies mean for our future. This is a unique opportunity to connect with, learn from, and build partnerships with some of the technology ‘Giants’ in the world.”

Moreover, “the VIC Summit culminates with an evening’s festivities that will include the celebration of the contributions of some of the greatest minds of our time who have made a lasting impact on society for the benefit of humanity.”

You can watch the 2019 IEEE Honors Ceremony Gala via live stream on May 17 at 7 pm PT at this link. The gala will celebrate the pinnacle achievements of this year’s IEEE Award recipients, whose accomplishments have led to myriad scientific breakthroughs.

For video and photos of previous honors ceremonies, visit this link.

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2019 IEEE John von Neumann Medal (sponsored by IBM Corporation)

  • Éva Tardos, a member of the U.S. National Academy of Sciences and National Academy of Engineering, has reshaped and renewed the foundations of algorithm design with long-term vision, creativity, and technical strength that is benefitting the Internet through improved resource allocation, network formation, routing and congestion control, and electronic commerce. During a career spanning over thirty years, Tardos is most known for her work on network-flow algorithms, approximation algorithms, and quantifying the efficiency of selfish routing through the lens of algorithmic game theory. Her solo work on strongly polynomial algorithms was a breakthrough,having resolved a major open problem in the field; in particular, she showed that the minimum-cost flow problem (one of the basic problems in network flow that models the efficient transport of goods through a network) could be solved in strongly polynomial time, with a running time depending only on the number of nodes and edges of the network, not on the magnitudes of its capacities or costs. She then played a pivotal role in establishing the modern use of linear programming in algorithm design to advance the field of approximation algorithms. She has developed approximation algorithms for fundamental problems in a wide range of application areas, including facility location, routing, clustering, classification, and social network analysis. Tardos’ work has been one of the pillars of algorithmic game theory, a burgeoning field that brings theoretical computer science and economics together to develop algorithmic foundations for our highly connected digital world. Algorithmic game theory is concerned with algorithms designed in the presence of self-interested agents, governed by incentives and economic constraints. Her pioneering work with Tim Roughgarden demonstrated how game-theoretic ideas could quantify the performance gaps between centrally managed network traffic and the flow of traffic directed by self-interested agents. This innovation provided the tools by which computer scientists can analyze the behavior of rational entities in computerized systems and has sparked an enormous amount of further research.