20042019
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Personal profile

Curriculum vitae

Dr Younsi has strong research interest in heterogeneous parallel programming applied to genomics.  His background is in Machine Learning and software engineering.  His recent research area is software developement for reference free variant calling coloured de Bruijn graphs and developing fast and efficient algorithms for bioinformatics using graphs.  He is investigating the software development of a heterogeneous parallel variants calling software using sequence alignment in multiple-genome graphs for both short and long reads.

Education/Academic qualification

Machine Learning, PhD, University of East Anglia

Fields of Science

  • 113 Computer and information sciences
  • Machine Learning
  • Bioinformatics, Systems biology
  • Parallel and Distributed Systems
  • Heterogeneous Programming in C and C++.

International and National Collaboration Publications and projects within past five years.

Publications 2004 2019

  • 4 Article
  • 2 Conference contribution

Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search

Younsi, R., Tang, J. & Holm, L. U. T., 29 Jan 2019, (Unpublished) In : arXiv.org .

Research output: Contribution to journalArticleGeneral public

File

Ensembles of random sphere cover classifiers

Younsi, R. & Bagnall, A., Jan 2016, In : Pattern Recognition. 49, p. 213-225 13 p.

Research output: Contribution to journalArticleScientificpeer-review

Using 2k+2 bubble searches to find single nucleotide polymorphisms in k-mer graphs

Younsi, R. & MacLean, D., 1 Mar 2015, In : Bioinformatics. 31, 5, p. 642-646 5 p.

Research output: Contribution to journalArticleScientificpeer-review

Open Access

An efficient randomised sphere cover classifier

Younsi, R., 1 Jan 2012, In : International Journal of Data Mining, Modelling and Management (IJDMMM). 4, 2, p. 156 171 p.

Research output: Contribution to journalArticleScientificpeer-review

A Randomized Sphere Cover Classifier

Younsi, R. & Bagnall, A., 2010, INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2010. Fyfe, C., Tino, P., Charles, D., GarciaOsorio, C. & Yin, HJ. (eds.). Springer-Verlag, p. 234-241 8 p. (Lecture Notes in Computer Science; vol. 6283).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review