SPEAKER: Ravi Kannan from Microsoft Research India

ABSTRACT:

Modeling data as high-dimensional (feature) vectors is a staple in Computer Science, its use in ranking web pages reminding us again of its effectiveness. Algorithms from Linear Algebra (LA) provide a crucial toolkit. But, for modern problems with massive data, these algorithms may take too long. Random sampling to reduce the size suggests itself. I will give a from-first-principles description of the LA connection, then discuss sampling techniques developed over the last decade for vectors, matrices and graphs. Besides saving time, sampling leads to sparsification and compression of data.

Bio: Ravindran (Ravi) Kannan is Principal Researcher in the Algorithms Research
Group at Microsoft Research Bangalore. Previously he was a professor at CMU,
MIT, and Yale, where he was the William Lanman Professor of Computer Science.
His research areas span Algorithms, Optimization and Probability. He is widely
known for introducing several groundbreaking techniques in theoretical computer
science, notably in the algorithmic geometry of numbers, sampling and volume
computation in high dimension, and algorithmic linear algebra. He received the
Knuth Prize in 2011, and the Fulkerson Prize in 1992. He is a distinguished
alumnus of IIT Bombay.

There will be a reception at 4:00 p.m. in the Atrium of the Klaus Building.

For more information: https://www.math.gatech.edu/news/aco-distinguished-lecture