Journal & magazine links
- American Scientist Online
- Computer Communication Review
- Elsevier Computer Science
- Annual Computer Security Applications Conference
- IEEE Computer Society
- IEEE Security & Privacy Magazine
- IEICE Trans Search System
- International Journal of Digital Evidence
- ScienceDirect – Computers & Security
- ACM TISSEC (Transactions on Information & System Security)
Dr. Waleed A. Yousef,
Senior scientist, ISOT LAB;
Adjunct professor, University of Victoria;
Senior member, IEEE.
Email: wyousef at uvic dot ca
- M.Sc. in Statistics, The George Washington University (GWU), 2007.
- Ph.D. in Computer Engineering, GWU, 2006.
- M.Sc. in Computer Science, Helwan University, 1999.
- Professional Certificate in Computer Systems and Applications, American University in Cairo (AUC), 1997.
- B.Sc. in Electrical Engineering, Communications and Electronics, Ain Shams University, 1995.
- Data Science, Data Analysis, Statistical Learning, Machine Learning, and Pattern Recognition.
- Assessment of Classification Rules and ROC Analysis.
- Data Visualization.
- Senior Scientist, ISOT lab., University of Victoria, BC,Canada (2019-present).
- Adjunct professor, University of Victoria, BC, Canada (2021-present).
- Lab Director, Human Computer Interaction Laboratory (HCILAB) (2011-present).
- CEO, Research Director, MESC for Research and Development. (2010-present).
- 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.