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Paul Valiant is an Associate Professor in the Department of Computer Science at Purdue University. His research focuses on algorithmic research, particularly on sublinear algorithms and big data related statistics. His work aims to illuminate the 'unseen' portions of probability distributions, specifically how to interpret the behavior of customers visiting a website over a given period. He explores how to leverage limited and costly data to extract valuable insights, particularly in scenarios where traditional algorithms might falter. Valiant’s research in theory of computing encompasses algorithms and quantum computing, aiming to achieve unusually high benchmarks such as 'instance optimal' algorithms that perform competitively against custom-designed algorithms for specific instances. His complementary work also includes developing matching lower bounds, which are crucial for understanding the limitations of existing algorithms and guiding future research directions in this area.
GRE is not required.