## Organisers:

## Time:

## Venue:

Propositional proof complexity is the study of the resources that are needed to prove formulas in propositional logic.

Speaker:

Marc Vinyals, TIFR

Friday, 17 November 2017, 17:15 to 18:15

Propositional proof complexity is the study of the resources that are needed to prove formulas in propositional logic.

Speaker:

Varun Narayanan, TIFR

Friday, 10 November 2017, 17:15 to 18:15

We would discuss the lower bound on the joint entropy of pairwise independent random variables (X_1, ... , X_n) by Lazlo Babai (2013).

Speaker:

Sumedh Vinod Tirodkar, TIFR

Friday, 27 October 2017, 17:15 to 18:15

We consider the maximum matching problem in the semi-streaming model formalized by Feigenbaum et al.

Vidya Sagar Sharma

Friday, 20 October 2017, 17:15 to 18:15

Storing sets is a common problem in Computer Science.

Speaker:

Nikhil S Mande, TIFR

Friday, 13 October 2017, 17:15 to 18:15

We use exponential sums to analyze the Fourier spectrum of functions of the type MOD_m^A (with output in {-1, 1}), for any constant m, and a general accepting set A. We will then see how this yields lower bounds on the number of monomials require

Speaker:

Anamay Tengse, TIFR

Friday, 6 October 2017, 17:15 to 18:15

The polynomial identity testing task is to determine whether a given circuit computes the zero polynomial.

Ankit K.

Friday, 29 September 2017, 16:00 to 17:00

Logical relations are proof techniques that can be used to prove properties about languages like normalization, type safety, program equivalence and are closed under elimination.

Speaker:

Tulasi mohan Molli, TIFR

Friday, 15 September 2017, 17:15 to 18:15

A boolean circuit is a natural model of computation for Boolean functions. Size and Depth of a circuit are two measures of complexity of the circuit.

Speaker:

Gunjan Kumar, TIFR

Friday, 8 September 2017, 17:15 to 18:15

We initiate the study of property testing of submodularity on the boolean hypercube. Submodular functions come up in a variety of applications in combinatorial optimization.

Speaker:

Gowtham Raghunath Kurri, TIFR

Friday, 1 September 2017, 17:15 to 18:15

Suppose we want to transmit messages across a noisy channel to a receiver. The maximum rate of transmission such that the receiver may recover the original message without errors (i.e., zero error) is called zero error capacity of the channel.