Md P. Mahmud

Alumn

Ph.D. in Computer Science, Rutgers, May 2014.
Now: Senior software engineer, Google, Inc.

Md Pavel Mahmud received his B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology and his PhD in computer science from 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.

Publications

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

Mahmud et al.. TreQ-CG: Clustering Accelerates High-Throughput Sequencing Read Mapping. Technical report, 2014.

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

Mahmud et al.. Fast MCMC Sampling for Hidden Markov Models to Determine Copy Number Variations. BMC Bioinformatics 2011, 12:1, 428.

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

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 Workshop on Algorithms in Bioinformatics (WABI), Saarbrücken, Germany

Project or Software Lead

HTSMethods: Analysis of high-throughput sequencing data.

TreqCG: Clustering Accelerates High-Throughput Sequencing Read Mapping.

TreQ: Indel-tolerant read mapping.