Specializations: Nonlinear dynamics, Bifurcation theory, Linear Algebra, Numerical methods, Differential Equations, Number theory
Research field: Nonlinear Dynamics & Chaos, Bifurcation Theory, Computational Neuroscience, Network of Oscillators, Reservoir Computing, Population Dynamics, Ecological Dynamics
Sishu Shankar Muni currently holds the position of Assistant Professor, School of Digital Sciences, Digital University Kerala. Dr. Muni completed his Integrated M. Sc. in applied Mathematics from the National Institute of Technology Rourkela. During his Bachelor studies, he came across a TED talk by Late Prof. Mandelbrot on fractals. Motivated, he started his journey to self learn dynamical systems. After being awarded a doctoral scholarship, he moved to Massey University, New Zealand to pursue his Ph.D. research in applied mathematics, specifically on dynamical systems. He then decided to work on a challenging conjecture during his postdoc at the Indian Institute of Science Education and Research Kolkata. After the successful postdoc, he joined the School of Digital Sciences, Digital University Kerala as an Assistant Professor. Dr. Muni is motivated to do research on nonlinear dynamical systems which are ubiquitous in nature. These systems exhibit striking complicated dynamics. The research involves the use of both computer simulations and mathematics to understand these complicated behaviors. Recently, Dr. Muni started applying his ideas of dynamical systems to neuroscience, power electronic systems, and mathematical biology.
Dr. Muni is fascinated about dynamical systems. Simple nonlinear dynamical systems can exhibit striking complicated behaviors such as sensitive dependence to initial conditions popularly known as “butterfly effect” and chaos (in mathematical terms). Dr. Muni investigates a deeper understanding of these systems. His research works are classified as follows :
1. Explore different bifurcations in two and three dimensional mappings.
2. Understanding the behavior of network of dynamical systems.
3. Application of various techniques of dynamical systems to neuroscience, biological models, and electronic systems.
4. Application of machine learning techniques to open problems of dynamical systems