Welcome to my home page!

I have graduated from Collaborative Software Development Laboratory of UH Manoa ICS where I was advised by Professor Philip M Johnson. The focus of my dissertation research was on empirical software process discovery. Working on this subject, 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 an academic or industrial 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 (i.e., motif) and anomalous (i.e., discord) patterns discovery, which are based on the symbolic discretization and grammatical inference. My next research project combines the experience in recurrent behaviors discovery and spatio-temporal data ming aiming at the unsupervised patterns discovery and activity classification.

Currently, I am working as a bioinformatics software engineer at MIAT INRA on genomic "dark matter" 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 of disease-related SNPs in the sheep genome; at ENSAT, I was involved in the identification of transcripts and pathways involved in the fruit ripening; at INRIA Rennes I worked on the olfactory proteins identification for insect mating disruption. 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

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© 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.