Ali Mohammadi Shanghooshabad
Ali's Ph.D. thesis in computer science has now been successfully defended. He was supported by the EPSRC and worked under Prof. Peter Triantafillou's supervision. He began his Ph.D. research by investigating machine learning and statistical techniques on data systems, particularly in big data settings.
Ali works to improve machine learning and statistical techniques for data systems, and vice versa. He has participated in a number of successful projects, including Model Joins, Join Sampling, and Learned Approximate Query Processing. Currently, his primary focus is on discovering a clear mapping between graphical models and the join problem in relational databases, and he is attempting to develop a new efficient and scalable physical join algorithm to obtain the exact join result using graphical models.
Teaching Assistant in modules:
- CS258: Databases (Term 1, 2019)
- CS909: Data Mining (Term 2, 2019)
- CS258: Databases (Term1, 2020)
- CS909 Data Mining (Term 2, 2020)
- CS258: Databases (Term1, 2021)
- Ali Mohammadi Shanghooshabad, and Peter Triantafillou. "Revisiting Join Algorithms with Probabilistic Graphical Models", arXiv preprint arXiv:2206.10435, 2022
- Ali Mohammadi Shanghooshabad, and Peter Triantafillou. “Model Join: Enabling Knowledge Discovery Over Joins of Absent Big Datasets”, arXiv preprint arXiv:2206.10434, 2022
- Ali Mohammadi Shanghooshabad, Meghdad Kurmanji, Qingzhi Ma, Michael Shekelyan, Mehrdad Almasi, and Peter Triantafillou. 2021. PGMJoins: Random Join Sampling with Graphical Models. In Proceedings of the 2021 International Conference on Management of Data. 1610–1622.
- Ali Mohammadi Shanghooshabad. 2021. XLJoins. In Proceedings of the 2021 International Conference on Management of Data. 2902–2904.
- Q. Ma, A.M. Shanghooshabad, M. Kurmanji, M. Almasi, and P. Triantafillou. “Learned Approximate Query Processing: Make it Light, Accurate and Fast”. In Proceedings of CIDR 2021
- Ali Mohammadi Shanghooshabad and Mohammad Saniee Abadeh. "Sifter: an approach for robust fuzzy rule set discovery". Soft Computing, 20(8):3303– 3319, 2016
- Ali Mohammadi, Mohammad Saniee Abadeh, and Hamidreza Keshavarz. "Breast cancer detection using a multi-objective binary krill herd algorithm". In 2014 21th Iranian Conference on Biomedical Engineering (ICBME), pages 128–133. IEEE, 2014.
- Ali Mohammadi Shanghooshabad and Mohammad Saniee Abadeh. "Robust medical data mining using a clustering and swarm-based framework". International Journal of Data Mining and Bioinformatics, 14(1):22–39, 2016.
- Ali Mohammadi Shanghooshabad and Mohammad Saniee Abadeh. "Robust, interpretable and high quality fuzzy rule discovery using krill herd algorithm". Journal of Intelligent & Fuzzy Systems, 30(3):1601–1612, 2016.
- Mohammad Mahdi Motevali, Ali Mohammadi Shanghooshabad, Reza Zohouri Aram, and Hamidreza Keshavarz. "Who: A new evolutionary algorithm bio-inspired by wildebeests with a case study on bank customer segmentation". International Journal of Pattern Recognition and Artificial Intelligence, 33(05):1959017, 2019
- Alireza Hekmatinia, Ali Mohammadi Shanghooshabad, Mohammad Mahdi Motevali, and Mehrdad Almasi. "Tuning parameters via a new rapid, accurate and parameter-less method using meta-learning". International Journal of Data Mining, Modelling and Management, 11(4):366–390, 2019.