Assistant Professor
SaHosseini@su.edu.om Phone: +968 26850100 Extn: 311 Location: Rustaq building, Office no: 002 |  |  |
- Ph.D. In Information Technology, University of Queensland, Australia, 2017
- MSc. In Information Technology, Queensland University of Technology, Australia, 2012
- BSc. In Software Engineering, Azad University, Tehran, Iran, 2000
- Data Science (Machine learning engineering including deep learning)
- Database (DBMS, OLAP, Ware-housing)
- Research Methods
- Enterprise Software Architecture
- The Web
- Web Application Development
- Security
- Spatio-temporal databases,
- Dynamical processes,
- Recommendation systems, and
- Social network analytics
- Hosseini, S., Najafipour, S., Cheung, N., Yin, H., Kangavari, M.R., & Zhou, X. (2020) TEAGS: time-aware text embedding approach to generate subgraphs. Data Mining Knowledge Discovery, 1136–1174.
- Najafipour, S., Hosseini, S., Hua, W., Kangavari, M. R., & Zhou, X. (2020). SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding. IEEE Transactions on Knowledge and Data Engineering.
- Ashrafi-Payaman, N., Kangavari, M. R., Hosseini, S., & Fander, A. M. (2020). GS4: Graph stream summarization based on both the structure and semantics. The Journal of Supercomputing, 1-21.
- Hosseini, S., Yin, H., Zhou, X., Sadiq, S., Kangavari, M. R., & Cheung, N. M. (2019). Leveraging multi-aspect time-related influence in location recommendation. World Wide Web, 22(3), 1001-1028.
- Hosseini, S., Yin, H., Zhang, M., Elovici, Y., & Zhou, X. (2018, June). Mining subgraphs from propagation networks through temporal dynamic analysis. In 2018 19th IEEE International Conference on Mobile Data Management (MDM) (pp. 66-75). IEEE.
- Hosseini, S., Yin, H., Cheung, N. M., Leng, K. P., Elovici, Y., & Zhou, X. (2018). Exploiting reshaping subgraphs from bilateral propagation graphs. In International Conference on Database Systems for Advanced Applications (pp. 342-351). Springer, Cham.
- Khalatbari, L., Kangavari, M. R., Hosseini, S., Yin, H., & Cheung, N. M. (2019). MCP: A multi-component learning machine to predict protein secondary structure. Computers in biology and medicine, 110, 144-155.
