Welcome to my home page!

I was a PhD student at CSDL of ICS UH Manoa advised by Professor Philip M Johnson. The focus of my dissertation research was on empirical software process discovery. Working on this, I have proposed, designed, and developed the Software Trajectory Analysis framework -- a general approach for recurrent behavior discovery and ranking from systematic measurements of software artifacts. I defended my dissertation in early 2015, and currently looking for a postdoctoral position; my short resume is available here.

Extending my dissertation research on recurrent behaviors discovery, I am increasingly involved in the development of spatio-temporal data mining techniques. Recently, we have proposed novel techniques for time series recurrent pattern (i.e., motif) discovery and for spatio-temporal anomaly (i.e., discord) discovery, both based on the symbolic discretization and grammatical inference. My next research project combines my work on recurrent behaviors discovery and spatio-temporal data ming aiming at activity classification.

Currently, I am working as a bioinformatics software engineer at MIAT INRA on genomics data mining. Previously, I have worked in a number of research projects. In FUNHYMAT I performed bioinformatics analysis of structure and function of microbial communities, in 3SR I was responsible for the identification and annotation of SNPs in sheep genome; at ENSAT, I was involved in the identification of genes involved in the fruit ripening; at INRIA Rennes I have designed and implemented a high-throughput sequence classification pipeline for G protein-coupled receptors (GPRs) based on the finite state machines. I also worked for LANL, DOE-JGI on NGS sequencing data analysis and genome finishing, and for RCUH, Hawaii on the transgenic papaya genome assembly.

Thank you!

senin [at] hawaii.edu

Creative Commons License
© Pavel Senin — 2005 - 2015. Except where otherwise noted, all original material on this page created by Pavel Senin is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported.