Research Member, Microarray Quality Control Phase 2 (MAQC2) Project (2007-2010). In response to the FDA Critical Path Initiative, scientists at the FDA’s National Center for Toxicological Research (NCTR), Jefferson, Arkansas, formally launched the MAQC project. This is a group of over 100 members from industry, academia, and US government working on methods for developing predictive models that use high-dimensional microarray (“DNA chips”) data to classify patients into low- or high-risk with respect to getting a specified kind of cancer.
Research Fellow, U.S. Food and Drug Administration (FDA) / Center for Devices and Radiological Health (CDRH) (2005-2007). Designing and testing statistical learning algorithms to work on real data problems, e.g., medical data for diagnostic purposes.
Internship Research Fellow, FDA/CDRH (2003-2005).
Award of the “GW (George Washington) Co-op Student Employee of the Year” (2005).
Internship at the Food and Drug Administration under the GWU cooperation program (2003-2005).
Four-year Scholarship from home country to pursue the Ph.D. in the US. (2002-2005)
Activities in Scientific Societies
Invited Member:
The National Committee of Experts for collaboration with the International Institute for Applied Systems Analysis (IIASA).
Senior Member:
IEEE.
Member:
IEEE Computer Society
IEEE Engineering in Medicine and Biology Society
American Statistical Association (ASA)
Referee:
IEEE Transactions on Information Forensics & Security.
IEEE Transactions on Medical Imaging.
IEEE Transactions on Knowledge and Data Engineering.
IEEE Systems Journal.
Elsevier: Pattern Recognition.
Elsevier: Computational Statistics and Data Analysis.
Elsevier: Engineering Applications in Artificial Intelligence.
Elsevier: Information Sciences.
Elsevier: Statistics and Probability Letters.
Medical Physics.
Australian & New Zealand Journal of Statistics.
BMC Medical Imaging.
Taylor & Francis, Journal of Image Science.
Lead Guest Editor for The Scientific World Journal.
The Kentucky Science and Engineering Foundation (KSEF).
Information Technology Industry Development Agency (ITIDA).
Yousef, W. A., Traore I., and Briguglio W. “Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring”, Provisional Application Number US 63168686, 2021
Yousef, W. A. “Method and system for computer aided detection for cancer”, Patent allowed, US 62/531,219, 07 11, 2017.
Book Chapters
Wagner, R.F., Yousef, W.A., and Chen W. (2008), “Finite Training of Radiologists and Statistical Learning Machines: Parallel Lessons” in Advances in Medical Physics 2008, A.B. Wolbarst, K.L. Mossman, and W.R. Hendee, Editors, Medical Physics Pub: Madison, WI.
Briguglio, W., Moghaddam, P., Yousef, W. A., Traore, I., & Mamun, M. (2021), “Machine Learning in Precision Medicine to Preserve Privacy via Encryption”. Pattern Recognition Letters, 151, 148-154.
Yousef, W. A. (2019), “Estimating the Standard Error of Cross-Validation-Based Estimators of Classifier Performance”, Pattern Recognition Letters, 146, 115-145.
Yousef, W. A. (2020). “Prudence when assuming normality: an advice for machine learning practitioners”, Pattern Recognition Letters, 138, 44-50.
Mousa, W. A. Y., Lang, W., & Yousef, W. A. (2017). “A pattern recognition approach for modeling the air change rates in naturally ventilated buildings from limited steady-state CFD simulations”, Energy and Buildings, 155, 54-65.
Mousa, W. A. Y., Lang, W., & Yousef, W. A. (2017). “Simulations and quantitative data analytic interpretations of indoor-outdoor temperatures in a high thermal mass structure”. Journal of Building Engineering, 12, 68-76.
Yousef, W. a.; Kundu, S. (2014). “Learning algorithms may perform worse with increasing training set size: Algorithm-data incompatibility”. Computational Statistics & Data Analysis.
Abdel Razek, N. M., Yousef W. A., and W. A. Mustafa (2013). “Microcalcification detection with and without CAD system (LIBCAD): A comparative study.” The Egyptian Journal of Radiology and Nuclear Medicine 44(2): 397-404.
Yousef, W. A. (2013). “Assessing classifiers in terms of the partial area under the ROC curve.” Computational Statistics & Data Analysis 64(0): 51-70.
Chen, W.; Gallas, B.; Yousef, W (2012), “Classifier variability: Accounting for training and testing”. Pattern Recognition 45(7): p. 2661-2671
Chen, W.; Gallas, B.; Yousef, W; et. al. (2012), “Uncertainty estimation with a finite dataset in the assessment of classification models”. Computational Statistics & Data Analysis 56(5): p. 1016-1027
Shi, Leming et. al. (2010), “The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models”. Nature Biotechnology 28(8): 827-838 (advance online publication version).
Yousef, W.A; Kundu, S.; and Wagner, R.F. (2009), “Nonparametric estimation of the threshold at an operating point on the ROC curve”. Computational Statistics & Data Analysis. 53(12): p. 4370-4383
Yousef, W.A., Wagner, R.F., and Loew M.H. (2006) “Assessing Classifiers From Two Independent Data Sets in Terms of The ROC Parameters: a Nonparametric Approach”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28: p.1809-2006.
Yousef, W.A., Wagner, R.F., and Loew, M.H. (2005) “Estimating the Uncertainty in the Estimated Mean Area Under the ROC Curve of a Classifier”. Pattern Recognition Letters, 26(16): p. 2600-2610.
Conferences
Abdelrazek, N.; Yousef, W.; Mustafa, W. (2012), “Microcalcification detection with and without prototype CAD system (LIBCAD): a comparative study”, European Society of Radiology (ECR 2012 / C-1063).
Yousef, W. A. et al. (2010), “On Detecting Abnormalities in Digital Mammography”. in Applied Imagery Pattern Recognition Workshop 2010. Proceedings. 39th; IEEE Computer Society.
Yousef, W.A. and W. Chen. (2009), “Estimating Cross-Validation Variability”. in Proceedings of the 2009 Joint Statistical Meeting, Section on Statistics in Epidemiology.
Chen, Weijie; Wagner, Robert; Yousef, Waleed; Gallas, Brandon (2009). “Comparison of classifier performance estimators: a simulation study”. Proceedings of IEEE Medical Imaging 2009, vol. 7263.
Yousef, W. A. (2008). “Statistical Learning Machines from ATR to DNA Microarrays: Design, Assessment, and Advice for Practitioners”. International Conference on Electrical Engineering. Proceeding 6th; Military Technical College.
Kondratovich, M. and Yousef, W.A. (2005) “Evaluation of Accuracy and “Optimal” Cutoff of Diagnostic Devices in the Same Study”. in Proceedings of the American Statistical Association.
Yousef, W.A., Wagner, R.F., and Loew, M.H. (2004) “Comparison of Non-Parametric Methods for Assessing Classifier Performance in Terms of ROC Parameters”. in Applied Imagery Pattern Recognition Workshop 2004. Proceedings. 33rd; IEEE Computer Society.
Yousef, W. A., Ibrahime, O. M., Madbouly, T. M., Mahmoud, M. A., El-Kassas, A. H., Hassan, A. O., & Albohy, A. R. (2018), “Poem Comprehensive Dataset (PCD)”.
Unpublished Publications (sounds funny): on arxiv and under submission to journals
Yousef, W. A. (2022). “Arabic Poem about Pattern Recognition and Recognizing The Patterns of Arabic Poetry”. OSF preprints, doi: 10.31219/osf.io/cfp3a.
Yousef, W. A., Hisham E. Mohammed, Andrew A. Naguib, Yusuf M. Khalifa, Alaa M. Mamdoh, Eman A. Awad, Nada A. Shawky Shrouk T. AbdElrheem, Sara G. Gaafar (2022), “JSOL: Javascript-based Open-Source Library”, arXiv preprint arXiv:
Yousef, W. A., Abouelkahire, A. A., Marzouk, O. S., Mohamed, S. K., Alaggan, M. N. (2019), “DVP: Data Visualization Platform”, arXiv preprint arXiv:1906.11738
Mustafa, W. A. and Yousef, W. A. (2019), “Nested cavity classifier: performance and remedy”, arXiv preprint arXiv:1906.09669.
Yousef, W. A., Ibrahime, O. M., Madbouly, T. M., & Mahmoud, M. A. (2019), “Learning meters of arabic and english poems with recurrent neural networks: a step forward for language understanding and synthesis”, arXiv preprint arXiv:1905.05700.
Elsayed, A. A., and Yousef, W. A. (2019), “Matlab vs. Opencv: a comparative study of different machine learning algorithms”. arXiv preprint arXiv:1905.01213.
Yousef, W. A. (2019), “A leisurely look at versions and variants of the cross validation estimator”, arXiv preprint arXiv:1907.13413
Yousef, W. A., Ahmed A. Abouelkahire, Deyaaeldeen Almahallawi, Omar S.Marzouk, Sameh K. Mohamed, Waleed A. Mustafa, Omar M. Osama, Ali A. Saleh, Naglaa M. Abdelrazek (2019), “Method and System for Image Analysis to Detect Cancer”, arXiv preprint arXiv:1908.10661
Yousef, W. A. (2019) “Assessment of multiple-biomarker classifiers: fundamental principles and a proposed strategy”. arXiv preprint arXiv:1910.14502
Dissertations
M.Sc. in Statistics, The George Washington University (GWU), 2007, “Nonparametric Estimation of the Threshold at an Operating Point on the ROC Curve” (pdf)
Ph.D. in Computer Engineering, GWU, 2006, “Assessment of Statistical Classification Rules: Implications for Computational Intelligence” (pdf)
M.Sc. in Computer Science, Helwan University, 1999, “Simulation of Prioritized Channel Assignment Models in Cellular Mobile Radio Networks” (pdf)
Technical
ACM/IEEE Curricula Recommendations, provide very important documents that define and offer guidelines for the five sub-disciplines of computing: (1) Computer Science, (2) Computer Engineering, (3) Information Systems, (4) Information Technology, and (5) Software Engineering. You are advised to start with “Computing Curricula 2005: The Overview Report” that sets the definition and scope of each discipline (department).
Statistics Department at Stanford University, where the leading researchers in the US and the world, in the field of statistical learning and pattern recognition, are.
MathWorld, a very useful mathematical resource designed by Wolfram Company, the producer of Mathematica. It is free, authentic, and rich.
Linear Algebra in MIT is the web page of a course taught by Professor Gilbert Strang. It contains the video lectures, homework problems, exams, and other interesting materials. I encourage every student to teach himself Linear Algebra from this web page, the course that is overlooked in many of our faculties.
“How to Read a Paper” is a very nice paper. The advice applies, as well, to reading books.
For Scientific Computing
Mathematica, the power of mathematical analysis in closed forms.
Matlab is one of the most popular scientific computing environments in the world.
OPNET is a comprehensive environment for Network design, analysis and simulation.
Numerical Recipes in C and Fortran is an invaluable source for our students that offers C and Fortran code for solving many numerical problems.
The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License.
For Composing Professional Manuscripts
LaTeX is a high-quality typesetting system. Serious graduate students and researchers in scientific fields are advised to consider LaTeX for composing their manuscripts, articles, and dissertations.
Scientific Word is a very helpful software for writing in LaTeX without worrying about coding.
If you want to interact directly with LaTeX and see the code you have to install one of the LaTeX implementations; MiKTeX is recommended. You need to install an editor, as well, that calls LaTeX for compilation; WinEdt is recommended, and TexStudio is very good too and free.
Whether you are a LaTeX or Microsoft Word user you, still, need software to manage your bibliography; EndNote is a good one among others, and Mendeley is very good and free. You do not need such software only to be as a library for your articles, books and other scientific materials, but also for citing them in your manuscripts.
If you, still, like to use Microsoft Word you need to install MathType, which is the professional version of Microsoft Equation Editor.
