Scientists are under increasing pressure to be "interdisciplinary": funders approve, the public likes the sound of the phrase, and there are a number of good examples of the benefits of synergy between different fields. But, as with purely disciplinary science, it important to avoid being superficial, and to be genuinely interdisciplinary takes time, effort and the right choice of problem. I will provide some examples of work at the interface of biology, physics and computation that illustrates the advantages of interdisciplinary science, in particular the importance of bringing new concepts and methodologies to bear on old problems. These examples, mainly from my group, include studies of the properties of chromosomes in cells, models for transport in neurons and agent-based approaches to infectious disease in populations.
Fundamentals of Genetic Algorithm and Applications
Genetic Algorithms (GAs) are randomized search and optimization techniques guided by the principles of evolution and natural genetics. They have been extensively used in solving hard optimization problems in a variety of real-life domains. The essential components of GAs are a strategy for representing or encoding a solution to the problem under consideration, a criterion for evaluating the fitness or goodness of an encoded solution and a set of biologically inspired operations, selection, crossover and mutation, applied on the encoded solutions. The conventional GAs are designed to optimize just a single criterion. However, many real-life problems have multiple conflicting objectives that need to be simultaneously optimized. Such problems are referred to as multiobjective optimization (MOO) problems. In contrast to single objective optimization, which yields a single best solution, in MOO the final solution set contains a number of Pareto-optimal solutions, none of which can be further improved on any one objective without degrading it in another. Since GAs typically adopt a population based search, modeling GAs to solve MOO problems is natural. The present talk will first explain the basic principles of genetic algorithms. This will be followed by a description of how GAs can be applied to clustering into a fixed number of partitions. Its extension to the case of unknown number of clusters will be discussed. The problem of multiobjective optimization will then be discussed in detail, and a GA based approach will be discussed. Finally, an application of genetic algorithm for clustering a data set will be described. Biosketch: Sanghamitra Bandyopadhyay joined the Machine Intelligence Unit of the Indian Statistical Institute as a faculty member in 1999, after completing her PhD from the same Institute.. She is currently the Director of the Institute. Her areas of research interest include computational biology and bioinformatics, soft and evolutionary computation, pattern recognition and data mining. In these areas she has published more than 300 research articles in various journal, conferences and edited volumes. She has published six authored and edited books from publishers like Springer, World Scientific and Wiley. Sanghamitra has worked in various Universities and Institutes world-wide including in USA, Australia, Germany, France, Italy, China, Slovenia and Mexico, and delivered invited lectures in many more countries. She has received several awards and fellowships including the Bhatnagar Prize, Infosys award, TWAS Prize, DBT National Women Bioscientist Award (Young), INAE Silver Jubilee Prize, Young scientist/engineer medals of INSA, INAE and Science Congress, JC Bose Fellowship, Swarnajayanti Fellowship and Humboldt Fellowship. She is a Senior Associate of ICTP and Fellow of INSA, INAE, NASI and IEEE. She is currently a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India.
February 22, 2019
Prof. J. Paul Attfield, Centre for Science at Extreme Conditions and School of Chemistry, University of Edinburgh
Fluid models have been studied for a long time for their control aspects, with a view to understand better, engineering and biological models like channel flow, blood flow, air flow in the lungs etc. Here we focus on a heat conducting fluid model. Local stabilization concerns the decay of the perturbation in the flow near a steady state. The main motivating example is the incompressible Navier-Stokes system. I will discuss this example and the general frame work and indicate some results in this direction.
December 21, 2018
Professor Balasubramanian Sundaram
Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru
Squishy Matter in a Computer - A journey through computational molecular science
The development and availability of large-scale throughput computing and sophisticated algorithms which efficiently represent fundamental laws and forces between atoms have spurred the ability of researchers to represent substances on such platforms with their full atomic-level detail. This capability enables computations to work as a powerful, time-resolved microscope to examine processes in and phenomena exhibited by modern materials which complement and substantively enhance our understanding obtained from experiments. In this talk, I plan to introduce a few of these computational methods and illustrate them with examples from our research group including self assembly in supramolecular polymers, CO2 storage in porous materials, enzyme modelling, and environmentally benign solvents.