Teaching and Research, Research supervision
Ram Ramaswamy was faculty member at JNU and has transferred to Indian Institute of Technology-Delhi
Ram Ramaswamy’s research has, over the years, wandered between disciplines, the underlying theme being dynamics. The origin of chaotic motion and its characterisation have been a major interest, with diverse topics such as strange non-chaotic dynamics, synchronisation, and systems biology being some specific topics he has worked on.
He has published about 200 journal and book articles relating to chemical dynamics, classical and quantum chaos, semi-classical quantisation, disordered systems and statistical physics, molecular dynamics and cluster physics, computational biology and genomics.
Principal Investigator | Supervisor
Field of Expertise
- Nonlinear science
- computational biology
Research interests of relevance to the project
- Nonlinear dynamics, chaotic motion, synchrony and forecasting
- Computational biology and modelling collective behaviour
I have spent several years in the US, UK, and Japan during the course of my studies and sabbaticals.
- The collective dynamics of NF-κB in cellular ensembles: Cluster synchrony, Splay states, and Chimeras
R Donepudi and R Ramaswamy, European Journal of Physics (Special Topics) 2018; 227: 851
- Coupled Lorenz oscillators near the Hopf boundary: Multistability, intermingled basins, and quasi-riddling
T T Wontchui, J Y Effa, H P E Fouda, S R Ujjwal, and R Ramaswamy, Physical Review E 2017; 96: 062203-1–11
- A general mechanism for the 1/f noise
A C Yadav, R Ramaswamy, and D Dhar, Physical Review E 2017; 96: 022215-1–6
- Two–layer modular analysis of gene and protein networks in breast cancer
A Srivastava, S Kumar, and R Ramaswamy, BMC Systems Biology 2014; 8: 81
- Genome wide analysis of mobile genetic element insertion sites K Rawal and R Ramaswamy, Nucleic Acids Research 2011; 39: 6864–6878
Projects within the Big Data Project/ Supervised students within the Big Data Project/Anticipated projects
We are working on two projects. One is to use techniques such as reservoir computing to examine chaotic time series and examine forecasting. The second is to infer equations of motion from time series observations.
Department of Chemistry
IIT Delhi, Hauz Khas
New Delhi 110016, India