When: Tuesday, March 28, 10:00 a.m.
Where: 3305 Newell-Simon Hall
NIR AILON, Princeton University
Abstract:
The information revolution has made massive amounts of data available from a myriad of sources such as biology, finance, e-commerce, advertising and the web. Handling these high-dimensional, often noisy and inconsistent datasets has become an important focus of algorithmic research.
(1) Rank Aggregation deals with combining inconsistent rankings suggested by different voters. This old problem from social choice theory has recently attracted the attention of computer scientists in surprising new contexts such as search engines and information retrieval systems. I will present new, improved algorithms, and discuss related results and follow-up work on cluster aggregation, hierarchical clustering and phylogeny.
(2) Sublinear algorithms compute functions by considering only a small sample of the input. This is especially important when the input is massive. I will talk about new results in online data reconstruction and nearest-neighbor searching in this context.
Time permitting, I will briefly touch upon other topics I am interested in such as self-improving algorithms, computational geometry and complexity.
BIO: Nir Alon is a Ph.D. student in the Department of Computer Science at Princeton University, expecting to graduate this summer. He is working under the supervision of Prof. Bernard Chazelle in the theoretical computer science group. He holds a Bachelor’s degree in Computer Science and a Master’s degree in Mathematics, both from Tel-Aviv University, Israel.
