Department of Computer Science
 Rutgers University

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Md P. Mahmud

Doctoral Student

Mailing address:
Department of Computer Science
Rutgers University
110 Frelinghuysen Road
Piscataway, NJ, 08854, USA

Visiting address:
Department of Computer Science
Rutgers University
Hill 270
110 Frelinghuysen Road
Piscataway, NJ, 08854, USA

Phone: +1-732-445-2001 ext.9502
Fax:
E-mail: pavelm@cs.rutgers.edu

Md Pavel Mahmud received his B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology. Currently he is a PhD student in Rutgers University.

He is working on computing reduced representations of massive next generation sequencing data sets and finding ways to utilize the reduced representations for downstream analysis. He has previously worked on speeding up Bayesian analysis of Hidden Markov Models and their applications on detecting copy number variations. He has also developed fast sampling techniques for Hidden Markov Models with discrete valued observations.

Upcoming/Recent presentations

April 16, 2012. Indel-sensitive Read Mapping with 2 and 3-gram Frequencies and Cache Oblivious kd-Trees. Invited Talk at DIMACS, Rutgers

Sept. 6, 2011. Speeding Up Bayesian HMM by the Four Russians Method. Contributed Talk at Max-Planck-Institute für Informatics, Saarbrücken, Germany

Recent publications

M. Mahmud Reduced representations for efficient analysis of genomic data; from microarray to high throughput sequencing. Ph.D. Thesis, Oct 2014.

M. Mahmud and A. Schliep TreQ-CG: Clustering Accelerates High-Throughput Sequencing Read Mapping. arxiv 2014.

M. Mahmud, J. Wiedenhoeft and A. Schliep Indel-tolerant Read Mapping with Trinucleotide Frequencies using Cache-Oblivious kd-Trees. Bioinformatics 2012, 28:18, i325–i332.

M. Mahmud and A. Schliep Speeding Up Bayesian HMM by the Four Russians Method. In Algorithms in Bioinformatics, Springer Berlin / Heidelberg, 6833, 188–200, 2011.

M. Mahmud and A. Schliep Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations. BMC Bioinformatics 2011, 12:1, 428.

Project lead

HTSMethods: Analysis of high-throughput sequencing data.

BayesianHMM: Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations.

Software lead

TreqCG: Clustering Accelerates High-Throughput Sequencing Read Mapping.

TreQ: Indel-tolerant read mapping.