Dr. Halima Bensmail

Halima Bensmail - Copy.jpg
Senior Scientist
Computational Science and Engineering
+(974) 4454 0195

Connect with me

Leadership is the most important factor that attracted me to QCRI. Need for creativity is the second factor. Innovation is a very important factor at QCRI. At QCRI we are being challenged continuously, we have been asked to think big. We are supposed to do research with applications that are of great impact, nationally and internationally.

Research Focus at QCRI

Halima is working broadly on statistical machine learning, applied it to medical areas for research that is referred to as: machine learning in Bioinformatics.

Most importantly, Halima is focusing on:

  • Use of machine learning for genetic mapping,
  • Bayesian approach to Quantification of metabolites related to diseases such as diabetes.
  • Covariance matrix robustness in graphical models and networks
  • Topology of causal network.
  • Nonlinear statistical analysis in NeuroImages.

Previous Experience

Halima Bensmail is an Associate Professor of Mathematics at the University of Tennessee and an adjunct faculty member at ORNL. She received her PhD in Mathematics and statistics from the University of Jussieu and INRIA in December 1995. She holds a BS degree in Mathematics from University of Science, Rabat and a MS degree of Mathematics and statistics from the University of Jussieu, Paris, France. She spent one year at the University of Washington, one year at the Fred Hutchinson cancer research center and two years at the Data Theory center, at the University of Leiden, the Netherlands. She advised five PhD students and over 20 Master students in different field of mathematical and computational sciences.

Professional Experience

  • University of Virginia Medical School, Biostatistics and Bioinformatics, Associate Professor, 2006
  • University of Tennessee, Statistics, Operations and management Sciences Department, Associate Professor, 2005
  • Oak Ridge National Laboratory, Adjunct faculty member, 2003
  • University of Tennessee, Statistics, Operations and management Sciences Department, Assistant Professor, 2000-2004
  • Data Theory Center, University of Social and Behavioral Sciences, Leiden, Netherlands, Scientist, 1997-2000
  • Fred Hutchinson Cancer Research Center Seattle, USA, Post-doc, 1996 -1997
  • University of Washington, Statistics Department, Seattle, USA, Research Associate, 1995-1996

Professional Associations and Awards

Awards:
  • Qatar Annual Research Forum Award for best research subject in computing, Doha 2011.
  • Nominated and finalist for best researcher and teaching junior faculty member, Knoxville, 2005.
  • Best junior researcher award by IFCS (International Federation for Classification society), Rome, 1997.
Membership

Education

  • PhD: Mathematics and Statistics, Paris 6 and INRIA (Paris - Rocquencourt), 1995.
  • MS: Mathematical Modeling, Paris 7, Paris, France, 1991.
  • BS: Mathematics, University of Sciences, Rabat, 1989.

Selected Research

  • Jing-Yang and Bensmail, H.  2013:  Cross-Domain Sparse Coding.  CIKM 2013.
  • Jing-Yang and Bensmail, H. 2012: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification. BMC Bioinformatics.
  • Liu, Z and Bensmail, H. 2012: Phenotype Prediction and Feature Detection with Linear Programming and Metagenomics Count data. Plos One.
  • Liu, Z and Bensmail, H. 2012: Efficient Feature Selection and Multiclass Classification with Integrated Instance and Model based Learning.Journal of Evolutionary Biology.
  • Bensmail, H., Anjum, S., Bouhali, O. and Elanbari, M. 2012: RAFNI: Robust Analysis of Functional NeuroImages with nonnormal-stable error, in LNCS: Lecture Notes in computer science, Vol. 7663, subseries: Theoretical computer Science and General Issues.
  • Kwon, Y, Bozdogan, H. and Bensmail, H. 2009: Performance of Model Selection Criteria in Bayesian Threshold VAR (TVAR) Models. Econometric Reviews, Vol. 28 (1-3), pp. 83-101.
  • Bensmail H, Golek J, Moody MM, Semmes JO, Haoudi A. 2005: Bayesian Fast-Fourier Transform Based Clustering Method for Proteomics Data. Bioinformatics, 21(10), pp. 2210-24.
  • Bensmail, H. Celeux, G. Raftery, A., Robert, C. 1997. Inference in Model-Based Cluster Analysis. Journal of Computing and Statistics, 1, N10, pp.1-10.
  • Bensmail, H. & Celeux, G. 1996. Regularized Discriminant Analysis. Journal of the American Statistical Association (JASA), Vol. 91, No 436, pp. 1743—1748

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In the News

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Using Big Data To Understand Migrations

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Upcoming Events

2014

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KDD2014: 20th ACM Conference on Knowledge Discovery and Data Mining

Download ICS File 24/08/2014 - 27/08/2014, New York City, USA

KDD 2014, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

The theme of this year's SIGKDD conference is 'Data Science for Social Good.'

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VLDB2014: 40th International Conference on Very Large Data Bases

Download ICS File 01/09/2014 - 05/09/2014, Hangzhou, China

VLDB is a premier annual international forum for data management and database researchers, vendors, practitioners, application developers, and users.

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31st IASP World Conference

Download ICS File 19/10/2014 - 22/10/2014, Qatar National Convention Centre, Doha, Qatar

The annual conference of the International Association of Science Parks and Areas of Innovation (IASP) is hosted by Qatar Science & Technology Park.

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Press Releases

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To Make Your Mark in Computer Science Visit the QCRI Summer Internship Programme Open House

03/04/2014

Qatar Computing Research Institute to host student information session showcasing unique summer internship opportunities

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QCRI Organises Talk by the Inventor of the World Wide Web

17/03/2014

Doha, Qatar, 17 March 2014:  Just a quarter of a century since the birth of the World Wide Web, Qatar Computing Research Institute (QCRI) has invited its inventor, Sir Tim Berners-Lee, to deliver a ...

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World’s Top Machine Learning and Data Analytics Experts Come to Qatar

02/03/2014

Joint Boeing and QCRI research symposium to highlight new approaches to valuable data extraction

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