Draft:Halima Bensmail
Computational biologist and AI researcher
From Wikipedia, the free encyclopedia
Halima Bensmail is a computational biologist and artificial intelligence researcher specializing in machine learning applications in biomedical sciences. She is a Principal Scientist at the Qatar Computing Research Institute (QCRI) at Hamad Bin Khalifa University (HBKU), a Professor in the College of Science and Engineering at HBKU, and a visiting full professor at Texas A&M University at Qatar.[1]
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Université Paris Cité
Pierre and Marie Curie University
Halima Bensmail | |
|---|---|
 Halima Bensmail | |
| Alma mater | University of Sciences Rabat Université Paris Cité Pierre and Marie Curie University |
| Scientific career | |
| Fields | Machine learning, Artificial intelligence, Biomedical science, Precision medicine |
| Institutions | Hamad Bin Khalifa University, Qatar Computing Research Institute |
Her research focuses on the development of statistical and machine learning methods for analyzing biological data, particularly genomics and multi-omics data integration to understand complex diseases such as cancer.[2]
Career
Bensmail worked as a postdoctoral researcher in statistics at the University of Washington from 1994 to 1996. She then served as a Research Associate in Biostatistics at the Fred Hutchinson Cancer Research Center in the United States from 1996 to 1997.
From 1997 to 2000, she worked as a Scientist in Data Theory at Leiden University in the Netherlands. In 2000 she joined the University of Tennessee as an Assistant Professor, a position she held until 2005, after which she was promoted to Associate Professor (2005–2006).
She later served as an Associate Professor of Public Health at Eastern Virginia Medical School. Since 2011, she has been a Principal Scientist at the Qatar Computing Research Institute at Hamad Bin Khalifa University. In 2016 she also became a Professor in the College of Science and Engineering at HBKU.
Bensmail has served as a reviewer for major research funding agencies including the National Institutes of Health (NIH) and the National Science Foundation (NSF). She is also a board member of the Artificial Intelligence and Quantum Technology Foundation in Davos and an Administrative Committee (AdCom) representative for the IEEE Engineering in Medicine and Biology Society (EMBS) in the Middle East and North Africa region.
Research
As a computational biologist, Bensmail develops statistical and machine learning approaches for precision analysis of biological data. Her work focuses on genomics, transcriptomics, and multi-omics data integration to investigate molecular mechanisms underlying complex diseases.
Her research also involves network analysis and clustering algorithms designed to identify biomarkers from large biological datasets. These approaches contribute to the discovery of disease-related biosignatures that can improve diagnostic methods and support advances in precision medicine.
Awards and honors
- 2023 – Research Award for Women in AI, Middle East Enterprise AI & Analytics Summit
- 2016 – Best Contribution in Information Complexity, International Conference on Information Complexity and Statistical Modeling in High Dimensions (IC-SMHD-2016)
- 2013 – Best Research Team, National Priorities Research Program (NPRP) Outcome Award, Qatar National Research Fund
- 2011 – Best Researcher in Computing, Annual Research Forum, Qatar Foundation
- 2000 – ONR Science and Technology Postdoctoral Fellowship, Office of Naval Research
- 1996 – Best Research Award in Statistics for Classification, International Federation of Classification Societies (IFCS)
- 1994 – Graduate Fellowship, French National Institute for Research in Digital Science and Technology
Selected publications
- Villiers, W.; Mifsud, B.; Lavender, P.; Kelly, A.; Bensmail, H.; Elbasir, A.; Dillon, A.; Osborne, C. (2023). "Multi-omics and deep learning reveal context-specific gene regulatory activities of PML-RARA in acute promyelocytic leukemia." Nature Communications. 14(1): 724.
- Patel, C. N.; Mall, R.; Bensmail, H. (2023). "AI-driven drug repurposing and binding pose meta-dynamics identifies novel targets for monkeypox virus." Journal of Infection and Public Health. 16(5): 799–807.
- Chen, Z.; Zhao, P.; Li, F.; Wang, Y.; Smith, A. I.; Webb, G. I.; Akutsu, T.; Baggag, A.; Bensmail, H.; Song, J. (2020). "DeepPRoMIse: A deep-learning framework for predicting RNA post-transcriptional modification sites." Briefings in Bioinformatics. 21(5): 1676–1696.
- Elbasir, A.; Moovarkumudalvan, B.; Kunji, K.; Kolatkar, P.; Mall, R.; Bensmail, H. (2020). "A deep learning framework for sequence-based protein crystallization prediction." Bioinformatics. 35(13): 2216–2225.
- Mall, R.; Cerulo, L.; Garofano, L.; Frattini, V.; Kunji, K.; Bensmail, H.; Sabedot, T. S. (2020). "RGBM: Regularized gradient boosting machines for identification of transcriptional regulators of discrete glioma subtypes." Nucleic Acids Research. 46(7): e39.