العربية
Dr Saeid Hosseini
Biography
Dr Saeid Hosseini (Data Scientist) is an Assistant professor at Sohar University, Faculty of Computing and Information Technology (FCIT). He completed his Ph.D. studies at the University of Queensland, Australia, under Prof. Xiaofang Zhou and Prof. Shazia Sadiq’s supervision and acquired the Australian Postgraduate Award (APA) in 2015. Dr Hosseini also completed an MSc degree at the Queensland University of Technology in information technology in coursework mode, focusing on various programming skills, software testing approaches, and the security perspective of data science. For his Ph.D., he developed a novel machine-learning approach to infer spatial information from microblog content, finding individuals’ cities and suburbs for advertising purposes, benefitting the marketing services. He proposed the temporal subset property with multi-domain applications, including the multi-aspect temporal influence in recommendation systems. During his studies, he worked as a solution architect and lead programmer in various Australian software companies in Melbourne and Brisbane for over ten years. He completed multiple solutions, including online shopping and newsletter websites, mainly based on Microsoft’s leading technologies. After finishing his Ph.D., he obtained a post-doc position in Singapore at the Singapore University of Technology and Design (SUTD). He conducted a few research work in the iTrust research center for cyber security.
The methods were concentrated on a mixture of machine learning and graph mining prototypes, benefitting preventative maintenance and triggering procedures. Dr Hosseini also completed a concurrent post-doc in Iran University of Science and Technology (among the Top 5 Universities in Iran). While supervising more than ten postgraduate students at IUST, he taught various courses at the computer faculty, including Data Science, Database Fundamentals, Research Methods, Enterprise Software Architecture, Web Application Programming, and Agile Software Development. After joining Sohar University, He has been teaching numerous courses, including Database Management Systems, Enterprise Resource Planning Design and implementation, Big data analytics, Distributed database design, Electronic environments, Wireless sensors & actuator networks, and computer animation. Moreover, he has published dozens of research pieces in well-known journals and conferences, including the Web Conference, IEEE International Conference on Mobile Data Management, IEEE International Conference on Data Engineering, Database Systems for Advanced Applications, IEEE Transactions on Knowledge and Data Engineering, Neural Computing and Applications, Knowledge-Based Systems, World Wide Web Journal, Data Mining and Knowledge Discovery, and Computers in biology and medicine. Dr Hosseini has been an active reviewer in various venues such as IEEE Transactions on Knowledge and Data Engineering, Neural Computing and Applications, Knowledge-Based Systems, World Wide Web Journal, The IEEE International Conference on Data Mining (ICDM), The supercomputing, and Journal of Network and Computer Applications. He has also been a CIKM, DASFAA, and ICMLA program committee member. Besides teaching and research, Dr Hosseini triggered the FCIT programming club for the industry sector to help students develop real-world applications where he has presented more than 60 workshops focusing on trending technologies, including ASP.NET Core, MS SQL Server, AngularJS, and End-to-End machine learning solutions using Keras and TensorFlow. His research interests include spatiotemporal databases, dynamical processes, data and graph mining, big data analytics, recommendation systems, Knowledge Graph inference, reasoning, sentiment & emotion analysis, summarization, and time-aware embedding approaches.
To conclude, Dr Hosseini aims to fulfill his academic role as an energetic lecturer, motivated research scientist and reviewer, supportive research supervisor, research-oriented program coordinator, and student programming club organizer, where observing his postgraduate students publishing in highly regarded venues such as IEEE ICDE (CORE A* and CCF A), IEEE TKDE (A* journal) and his undergraduate students developing real-world internet solutions, fully practical for various businesses, such as khasamsohar using trending technologies.
Qualification
- 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
Teaching Interest
- Data Science (Analytics and Deep learning)
- Database (Including Advanced DB, and Distributed DB)
- Web Application Development
- Research Methods
- Big Data Analytics
- Enterprise Resource Planning (ERP, SAP, Odoo)
- Wireless Sensors and Actuator Networks
- Enterprise Software Architecture
Research Interest
- Spatio-temporal databases
- Dynamical processes
- Recommendation systems
- Social network analytics
- Data Science
Publications
- 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.