## Speaker:

## Organisers:

## Time:

## Venue:

**Abstract:** Ultra Large-scale Linear Programming refers to class of problems where number of linear inequality constraints grows exponentially w.r.t. the number of variables.

Dr. Narendra Karmarkar

Tuesday, 21 January 2020, 16:00 to 17:00

**Abstract:** Ultra Large-scale Linear Programming refers to class of problems where number of linear inequality constraints grows exponentially w.r.t. the number of variables.

Pranabendu Misra

Tuesday, 21 January 2020, 14:30 to 15:30

**Abstract:** Matroids are combinatorial objects that generalize the notion of linear independence. They have several applications in design and analysis of algorithms.

Speaker:

Prerona Chatterjee, TIFR

Friday, 17 January 2020, 16:00 to 17:30

Abstract: An Algebraic Branching Program (ABP) is a layered graph where each edge is labeled by an affine linear form and the first and the last layer have one vertex each, called the “start” and the “end” vertex respectively.

Speaker:

Abhishek Singh, TIFR

Wednesday, 15 January 2020, 11:30 to 13:00

**Abstract:** Mathematical proofs when written in conventional ways often contain imprecise definitions, unstated background assumptions, and inferential gaps in reasoning.

R.K. Shyamasundar

Tuesday, 14 January 2020, 14:30 to 15:30

Abstract: Smart Contracts handle and transfer assets of considerable value. Thus, it is crucial that their implementation be secure against attacks which aim at stealing or tampering the assets.

Prof. Janusz Konrad

Monday, 13 January 2020, 16:00 to 17:00

**Abstract: **Smart rooms, that respond to occupant behavior, will likely become a common occurrence in our lifetimes.

Parikshit Gopalan

Monday, 13 January 2020, 14:30 to 15:30

**Abstract: **Anomaly detection is a ubiquitous problem in machine learning. Here one is given a large population of points, we may not have much knowledge about their structure a priori.

Karthikeyan Shanmugam

Friday, 10 January 2020, 14:30 to 15:30

Abstract: Directed Causal Graphs (DAGs) capture causal relationships amongst a set of variables and they specify how interventional distributions relate to observational ones.

Anindya De

Monday, 6 January 2020, 10:30 to 11:30

**Abstract:** Consider the following basic problem in sparse linear regression -- an algorithm gets labeled samples of the form (x, <w.x> + \eps) where w is an unknown n-imensional vector, x is drawn from a background distributi

Monday, 6 January 2020, 09:00 to Thursday, 9 January 2020, 17:00

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