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| Eva Tardos | |
|---|---|
| Name | Eva Tardos |
| Birth date | 1966 |
| Birth place | Budapest, Hungary |
| Fields | Computer Science, Algorithms, Theoretical Computer Science, Game Theory |
| Workplaces | Cornell University, Microsoft Research, Bell Labs |
| Alma mater | Eötvös Loránd University, University of Szeged, Massachusetts Institute of Technology |
| Doctoral advisor | Michael Sipser |
| Known for | Approximation algorithms, Network algorithms, Algorithmic game theory |
| Awards | Nevalinna Prize, Gödel Prize, Knuth Prize, National Academy of Sciences, Association for Computing Machinery Fellow |
Eva Tardos is a Hungarian-born computer scientist noted for foundational work in approximation algorithms, algorithmic game theory, and network flow. She is a professor at Cornell University and has held positions at Microsoft Research and Bell Labs, contributing to theory that impacted practice at Google, Amazon, Facebook, and IBM. Her career bridges interactions with researchers at MIT, Stanford University, Princeton University, Harvard University, and international centers such as University of Cambridge, ETH Zurich, and École Polytechnique Fédérale de Lausanne.
Born in Budapest to a family with ties to Hungary, she studied mathematics and computer science at Eötvös Loránd University and University of Szeged before moving to the United States for graduate study at the Massachusetts Institute of Technology, where she completed her Ph.D. under Michael Sipser. Her formative years connected her to research groups at Rutgers University, Bell Labs, and collaborations with scholars from University of California, Berkeley, Columbia University, Cornell University, and University of Illinois Urbana-Champaign.
Tardos joined the faculty of Cornell University and developed courses and programs that intersected with faculty from Department of Computer Science, Cornell University, School of Engineering and Applied Sciences, Harvard University, and visiting scholars from Princeton University. She held visiting researcher roles at Microsoft Research and maintained collaborations with academics at Stanford University, UC Berkeley, Carnegie Mellon University, Tel Aviv University, and Technion – Israel Institute of Technology. Her administrative work connected with units such as National Science Foundation-funded centers, partnerships with Simons Foundation, and initiatives supported by European Research Council and Defense Advanced Research Projects Agency.
Her work on approximation algorithms built on techniques linked to the Primal-dual method, Linear programming, and Semidefinite programming, intersecting with research by Vazirani, Vijay, Arora, Sanjeev, Karloff, Howard, Young, Neal E., Goemans, Michel X., and Williamson, David P.. Tardos produced influential results in network flow and matching theory related to problems studied at Stanford University and University of Waterloo. In algorithmic game theory, she investigated Nash equilibrium, price of anarchy, and mechanism design with colleagues such as Tim Roughgarden, Éva Tardos collaborators, Noam Nisan, Avi Wigderson, Jon Kleinberg, and Robert Kleinberg. Her analyses influenced applications at Google, Amazon, LinkedIn, Uber Technologies, and Airbnb. Cross-disciplinary impact occurred in projects with researchers from Economics departments at MIT, University of Chicago, and London School of Economics.
Her accolades include the Nevalinna Prize and the Gödel Prize shared for collaborative work, recognition as a Fellow of the Association for Computing Machinery and election to the National Academy of Sciences and National Academy of Engineering. She received honors also offered to scholars associated with ACM and IEEE, alongside awards similar to those earned by recipients of the Turing Award, Knuth Prize, MacArthur Fellowship, and fellowships from Simons Foundation and Guggenheim Foundation.
At Cornell University she taught courses drawing students from departments such as Computer Science, Operations Research and Information Engineering, and joint programs with Sloan School of Management and Johnson Graduate School of Management. Her graduate students and postdocs went on to faculty positions at institutions including Stanford University, Harvard University, Princeton University, University of California, Berkeley, University of Toronto, University of British Columbia, ETH Zurich, École Polytechnique Fédérale de Lausanne, University of Oxford, and industry research labs at Microsoft Research, Google Research, and IBM Research.
Tardos served on program committees for flagship conferences such as STOC, FOCS, SODA, ICALP, and editorial boards for journals like Journal of the ACM, SIAM Journal on Computing, and Algorithmica. She participated in advisory roles for agencies including the National Science Foundation, panels at the Simons Foundation, and collaborations with the European Research Council. Her leadership extended to organizing workshops with groups from Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and industry-academic consortia with Google, Microsoft, and Facebook.
- "Approximation algorithms" (coauthored research articles with peers such as David P. Williamson, Michel X. Goemans, Vijay Vazirani, and Arora, Sanjeev) in Journal of the ACM and conference proceedings of STOC and FOCS. - Papers on algorithmic game theory and price of anarchy collaborating with Tim Roughgarden, Jon Kleinberg, and Noam Nisan presented at SODA and ACM EC. - Work on network flow and combinatorial optimization appearing in SIAM Journal on Computing and Mathematics of Operations Research.
Category:Living people Category:Computer scientists Category:Theoretical computer scientists Category:Cornell University faculty