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Google Research
Other Google Resources
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Dr. Feldman graduated from Dartmouth College (BS, 97) and MIT (Ph.D., 03). He was an NSF postdoc at Columbia University before joining as a Research Scientist at Google, NY. His research is mainly in Algorithms, Coding Theory, and other areas of Theoretical Computer Science; in the past few years, he has been working on algorithms and systems for sponsored search advertising at Google.
A Truthful Mechanism for Offline Ad Slot Scheduling, Jon Feldman, S. Muthukrishnan, Evdokia Nikolova, Martin Pal, Symposium on Algorithmic Game Theory, 2008.
Algorithmic Methods for Sponsored Search Advertising, Jon Feldman, S. Muthukrishnan, Performance Modeling and Engineering (Proc. SIGMETRICS 2008 Tutorial Sessions), pp. 91-124.
On Distributing Symmetric Streaming Computations, Jon Feldman, S. Muthukrishnan, Anastasios Sidiropoulos, Cliff Stein, Zoya Svitkina, Proc. 19th Annual Symposium on Discrete Algorithms (SODA), 2008.
Online Ad Slotting with Cancellations, Florin Constantin, Jon Feldman, S. Muthukrishnan, Martin Pal, Fourth Workshop on Ad Auctions, 2008 (to appear).
Position Auctions with Bidder-Specific Minimum Prices, Eyal Even-Dar, Jon Feldman, Yishay Mansour, S. Muthukrishnan, Fourth Workshop on Ad Auctions, 2008 (to appear).
Sponsored Search Auctions for Markovian Users, Gagan Aggarwal, Jon Feldman, Martin Pal, S. Muthukrishnan, Fourth Workshop on Ad Auctions, 2008 (to appear).
Budget Optimization in Search-Based Advertising Auctions, Jon Feldman, S. Muthukrishnan, Martin Pál, Cliff Stein, Proc. ACM Conference on Electronic Commerce, 2007.
Bidding to the Top: VCG and Equilibria of Position-Based Auctions, Gagan Aggarwal, Jon Feldman, S. Muthukrishnan, Proceedings of the Fourth Workshop on Approximation and Online Algorithms (WAOA), 2006.
Growth Codes: Maximizing Sensor Network Data Persistence, Abhinav Kamra, Vishal Misra, Jon Feldman, Dan Rubenstein, Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications, pp. 255-266.
PAC Learning Mixtures of Gaussians with No Separation Assumption, Jon Feldman, Ryan O'Donnell, Rocco A. Servedio, Proc. 19th Annual Conference on Learning Theory (COLT), 2006.
Using Many Machines to Handle an Enormous Error-Correcting Code, Jon Feldman, Proc. IEEE Information Theory Workshop (ITW), 2006.
Data persistence in sensor networks: towards optimal encoding for data recovery in partial network failures, Abhinav Kamra, Jon Feldman, Vishal Misra, Dan Rubenstein, SIGMETRICS Performance Evaluation Review, vol. 33 (2005), pp. 24-26.
LP decoding achieves capacity, Jon Feldman, Clifford Stein, SODA, 2005, pp. 460-469.
Learning mixtures of product distributions over discrete domains, Jon Feldman, Ryan O'Donnell, Rocco A. Servedio, FOCS, 2005, pp. 501-510.
Using linear programming to Decode Binary linear codes, Jon Feldman, Martin J. Wainwright, David R. Karger, IEEE Transactions on Information Theory, vol. 51 (2005), pp. 954-972.
Decoding turbo-like codes via linear programming, Jon Feldman, David R. Karger, J. Comput. Syst. Sci., vol. 68 (2004), pp. 733-752.
Decoding Turbo-Like Codes via Linear Programming, Jon Feldman, David R. Karger, FOCS, 2002, pp. 251-260.
A 3/2-Approximation Algorithm for Augmenting the Edge-Connectivity of a Graph from 1 to 2 Using a Subset of a Given Edge Set, Guy Even, Jon Feldman, Guy Kortsarz, Zeev Nutov, RANDOM-APPROX, 2001, pp. 90-101.
Computing an Optimal Orientation of a Balanced Decomposition Tree for Linear Arrangement Problems, Reuven Bar-Yehuda, Guy Even, Jon Feldman, Joseph Naor, J. Graph Algorithms Appl., vol. 5 (2001).
Parallel processor scheduling with delay constraints, Daniel W. Engels, Jon Feldman, David R. Karger, Matthias Ruhl, SODA, 2001, pp. 577-585.
The Directed Steiner Network Problem is Tractable for a Constant Number of Terminals, Jon Feldman, Matthias Ruhl, FOCS, 1999, pp. 299-308.
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