Samose Seminar Series: To Be or Knot to Be
Date & Time: Friday, June 9th at 4pm (Tea/Coffee at 3.45pm)
Venue: Simons Centre, Ground Floor
Speaker: Dr. Prathamesh Turaga, IMSc Chennai Abstract:
Knot theory is a branch of mathematical research in which one studies the mathematical formulation of the intuitive notion
Samose Seminar Series : Symmetries and Dynamics
Date & Time: Friday, May 5th at 4pm (Tea/Coffee at 3:45pm)
Venue: Simons Centre, Ground Floor.
Speaker: Amit Vutha, ICTS and NCBS
Abstract:
Simply put, a symmetry of a dynamical system is any transformation that
takes solutions to solutions. Although symmetries constrain dynamics, the
symmetries of a dynamical system can be used in a systematic way to
analyze, predict, and understand general properties of these systems.
After reviewing definitions and major results, I will explore applications
Date and time: Wed, 19 April, 4pm (Refreshments at 3:45pm)
Speaker: Siddhartha Gadgil, IISc Mathematics
Title: Automating Mathematics? (Part 2)
Abstract: (This is a continuation of the speaker's talk on 7 April.)
I will discuss various stuff related to the goal of having computers
play a central role in the discovery of mathematical proofs, including
some of my own efforts.
Samose Seminar Series: Automating Mathematics
Friday, 7 April, 4pm (Refreshments at 3:45pm)
Simons Center-GF, NCBS
Speaker: Siddhartha Gadgil, IISc Math
Abstract:
I will discuss various stuff related to the goal of having computers play
a central role in the discovery of mathematical proofs, including some of
my own efforts.
Speaker: Gaurav Mendiratta
Title: Foliation in Dynamical Systems: Two models of competition
Abstract:
Foliation in dynamical systems is a global property where the solution set
of trajectories span a subspace, parametrized by one or more parameters.
We start with the example of a specific model where tumor cells and normal
cells compete to survive. In the case of mathematical models of a disease
such as cancer, immunity is an important feature of the competitive
We aim to understand some properties that characterize Gaussian distributions and make them ubiquitous in all fields of science, starting from Maxwell's Law in statistical mechanics. We will explain some key yet elementary heuristic ideas of the proofs, keeping the mathematics as simple as possible. Prerequisite: calculus.
Abstract: We start with univariate time series data and describe a popular representation of the data using autoregressive (AR) processes. Properties of AR processes will be considered in some detail. Next we generalize to multivariate time series analysis and their representation through vector autoregressive (VAR) processes. Finally we describe Granger causality that quantifies causal relations within a multivariate time series data.
