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Seminar Prof. Mike Malyutov

Moore Annex LT
12/12/2017 (15:00-16:00)

Mike Malyutov

Prof. Mike Malyutov, Northeastern University, USA

Retrospective training slow HMM-SCOT emissions model

The Baum-Welsh recurrent ML estimation of HMM parameters has been successfully applied to speech recognition. Its application to Genome modeling is questionable since assigning independence and equal probabilities to emissions from the same part of genome is a rough approximation. We develop a hybrid slow HMM switching model with SCOT emissions which might be a more realistic model for Genome, analysis of combined authorship of literary works, seismological data or financial time series with piecewise volatility. Our combined online and offline segmentation stage estimates time regions with constant HMM states using homogeneity test for SCOT emissions strings. This is made recurrently in parallel on a cluster

Professor Malioutov applies statistics to a wide variety of areas including : genetic drift in human populations, molecular biology, genetic fingerprints, pooled blood testing for HIV, seismology, forensic pharmacy, quality control of production lines, strong earthquakes and wind speed prediction and weather modification. He has worked on a number of software development projects including work for financial institutions and work in linguistics related to speech recognition and authorship attribution. Twenty one graduate students have received their PhD under Professor Malioutov’s supervision so far. His area of expertise is Experimental design, Information theory, Probability, Statistics



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