Dr. Abdulhakim Ali Qahtan
Research Focus at QCRI
Abdulhakim Qahtan is currently a Post Doctoral Researcher at Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Qatar. Mr. Qahtan was awarded his PhD degree from the Machine Intelligence & Knowledge Engineering (MINE) Lab at King Abdullah University of Science and Technology (KAUST) in 2016. He completed his B.S. and M.S. in Computer Science at Cairo University, Egypt (2000) and King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia (2008), respectively. He worked as a teaching assistant at Taiz University, Yemen (2001 – 2003) and a lecturer in KFUPM, Saudi Arabia (2008 – 2010).
His research interests include:
- Large-scale data discovery
- Data cleaning
Post Doctoral Researcher
Data Analytics Group, QCRI
KAUST, Saudi Arabia
Texea A&M University, USA
Professional Associations and Awards
PhD Computer Science - Dissertation Title: Efficient Estimation of Dynamic Density Functions with Applications in Streaming Data.
King Abdullah University of Science and Technology (KAUST), Thuwal, KSA. August 2010 - June 2016
M.S. Computer Science - Thesis Title: Implementation and Experimental Performance Evaluation of a Hybrid Interrupt-Handling Scheme.
King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, KSA. September 2003-June 2008
Bachelor in Computer Science (with honors)
Cairo University, Cairo, Egypt. September 1996-June 2000.
- Fernandez, R., Deng, D., Mansour, E., Qahtan, A., Tao, W., Abedjan, Z., Elmagarmid, A., Ilyas, I. F., Madden, S., Ouzzani, M., Stonebraker, M., Tang, N.
“A Demo of the Data Civilizer System,”, the 2017 ACM SIGMOD Conference (SIGMOD'17), May 14-19, Chicago, IL USA.
- Qahtan, A., Wang, S. and Zhang, X.
“KDE-Track: An Efficient Dynamic Density Estimator for Data Streams,” the IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 29, No. 3, 2017, pp. 642-655.
- Alharbi, B., Qahtan, A., and Zhang, X.
“Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces,” accepted by: the 30-th AAAI Conference on Artificial Intelligence (AAAI’16), February 12-17, 2016, Phoenix, Arizona, USA.
- Qahtan, A., Alharbi, B., Wang, S. and Zhang, X.
“A PCA-Based Change Detection Framework for Multidimensional Data Streams,” In the 21-st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’15), August 10-13, 2015, Sydney, Australia, pp. 935-944.
- Qahtan, A., Zhang, X. and Wang, S.
”Efficient Estimation of Dynamic Density Functions with an Application to Outlier Detection,” In the 21-st ACM International Conference on Information and Knowledge Management (CIKM’12), October 29 - November 02, 2012, Maui, HI, USA, pp. 2159-2163.