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Indian Statistical Institute is an academic institute of national importance as recognised by a 1959 act of the Indian parliament. It grew out of the Statistical Laboratory set up by Prasanta Chandra Mahalanobis in Presidency College, Kolkata. Established in 1931, this public university of India is one of the oldest and most prestigious institutions focused on statistics, and its early reputation led it to being adopted as a model for the first US institute of Statistics set up at the Research Triangle, North Carolina by Gertrude Mary Cox.Mahalanobis, the founder of ISI, was deeply influenced by wisdom and guidance of Rabindranath Tagore and Brajendranath Seal. Under his leadership, the institute initiated and promoted the interaction of Statistics with natural and social science to advance the role of Statistics as a key technology by explicating the twin aspects – its general applicability and its dependence on other disciplines for its own development. The institute is now considered as one of the foremost centres in the world for training and research in Statistics and related science.ISI has its headquarters in Baranagar, a suburb of Kolkata, West Bengal. It has four subsidiary centres focused in academics at Delhi, Bangalore, Chennai and Tezpur, and a branch at Giridih. In addition, the Institute has a network of units of Statistical Quality Control and Operations Research at Vadodara, Coimbatore, Hyderabad, Mumbai and Pune engaged in guiding the industries, within and outside India, in developing the most up–to–date quality management systems and solving critical problems of quality, reliability and productivity.Primary activities of ISI are research and training of Statistics, development of theoretical Statistics and its applications in various natural and social science. Originally affiliated with the University of Calcutta, the institute was declared an institute of national importance in 1959, through an act of Indian parliament, Indian Statistical Institute act, 1959. ISI functions under the Ministry of Statistics and Programme Implementation of the Government of India.Key areas of expertise of ISI are Statistics, Mathematics, Computer science, quantitative Economics, Operations Research and Information Science and it is one of the few research oriented Indian schools offering courses at both the undergraduate and graduate level. The current Director of ISI is Professor Bimal Kumar Roy and the current Dean of Studies is Professor Pradipta Bandyopadhyay. Wikipedia.


Majumder P.P.,Indian Statistical Institute
Current Biology | Year: 2010

South Asia - comprising India, Pakistan, countries in the sub-Himalayan region and Myanmar - was one of the first geographical regions to have been peopled by modern humans. This region has served as a major route of dispersal to other geographical regions, including southeast Asia. The Indian society comprises tribal, ranked caste, and other populations that are largely endogamous. As a result of evolutionary antiquity and endogamy, populations of India show high genetic differentiation and extensive structuring. Linguistic differences of populations provide the best explanation of genetic differences observed in this region of the world. Within India, consistent with social history, extant populations inhabiting northern regions show closer affinities with Indo-European speaking populations of central Asia that those inhabiting southern regions. Extant southern Indian populations may have been derived from early colonizers arriving from Africa along the southern exit route. The higher-ranked caste populations, who were the torch-bearers of Hindu rituals, show closer affinities with central Asian, Indo-European speaking, populations. © 2010 Elsevier Ltd. All rights reserved.


Sarkar P.,Indian Statistical Institute
IEEE Transactions on Information Theory | Year: 2010

A general result is proved for constructions which use a pseudo-random function (PRF) with a small domain to build a PRF with a large domain. This result is used to analyse a new block-cipher based parallelizable PRF, called iPMAC which improves upon the well-known PMAC algorithm. New authenticated encryption schemes are described and then combined with iPMAC to obtain new schemes for authenticated encryption with associated data. Improvements over well known schemes such as the offset codebook (OCB) mode include avoiding a design-stage discrete logarithm computation, a small speed-up and a smaller size decryption algorithm. © 2006 IEEE.


Maji P.,Indian Statistical Institute
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

One of the major tasks with gene expression data is to find groups of coregulated genes whose collective expression is strongly associated with sample categories. In this regard, a new clustering algorithm, termed as fuzzyrough supervised attribute clustering (FRSAC), is proposed to find such groups of genes. The proposed algorithm is based on the theory of fuzzyrough sets, which directly incorporates the information of sample categories into the gene clustering process. A new quantitative measure is introduced based on fuzzyrough sets that incorporates the information of sample categories to measure the similarity among genes. The proposed algorithm is based on measuring the similarity between genes using the new quantitative measure, whereby redundancy among the genes is removed. The clusters are refined incrementally based on sample categories. The effectiveness of the proposed FRSAC algorithm, along with a comparison with existing supervised and unsupervised gene selection and clustering algorithms, is demonstrated on six cancer and two arthritis data sets based on the class separability index and predictive accuracy of the naive Bayes' classifier, the K-nearest neighbor rule, and the support vector machine. © 2010 IEEE.


Maji P.,Indian Statistical Institute
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Microarray technology is one of the important biotechnological means that allows to record the expression levels of thousands of genes simultaneously within a number of different samples. An important application of microarray gene expression data in functional genomics is to classify samples according to their gene expression profiles. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of coregulated genes whose collective expression is strongly associated with the sample categories or response variables. In this regard, a new supervised attribute clustering algorithm is proposed to find such groups of genes. It directly incorporates the information of sample categories into the attribute clustering process. A new quantitative measure, based on mutual information, is introduced that incorporates the information of sample categories to measure the similarity between attributes. The proposed supervised attribute clustering algorithm is based on measuring the similarity between attributes using the new quantitative measure, whereby redundancy among the attributes is removed. The clusters are then refined incrementally based on sample categories. The performance of the proposed algorithm is compared with that of existing supervised and unsupervised gene clustering and gene selection algorithms based on the class separability index and the predictive accuracy of naive bayes classifier, K-nearest neighbor rule, and support vector machine on three cancer and two arthritis microarray data sets. The biological significance of the generated clusters is interpreted using the gene ontology. An important finding is that the proposed supervised attribute clustering algorithm is shown to be effective for identifying biologically significant gene clusters with excellent predictive capability. © 2011 IEEE.


Choudhury S.,Indian Statistical Institute | Mazumdar A.,Lancaster University
Nuclear Physics B | Year: 2014

In this paper we provide an accurate bound on primordial gravitational waves, i.e. tensor-to-scalar ratio (r) for a general class of single-field models of inflation where inflation occurs always below the Planck scale, and the field displacement during inflation remains sub-Planckian. If inflation has to make connection with the real particle physics framework then it must be explained within an effective field theory description where it can be trustable below the UV cut-off of the scale of gravity. We provide an analytical estimation and estimate the largest possible r, i.e. r ≤ 0.12, for the field displacement less than the Planck cut-off. © 2014 The Authors.

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