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Meghdad Kurmanji


Meghdad is a third year PhD student at the Department of Computer Science at the University of Warwick. He is working with Prof. Peter Triantafillou on Machine Learning and Database Systems.

Research Interests

Meghdad is interested in machine learning for database components and has been involved in related projects such as approximate query processing and cardinality estimation. Currently, he is trying to find the best solutions and approaches to perform update operations in learned data systems.

Teaching Assistance:

  • CS258: Databases (Term1, 2020)
  • CS909 Data Mining (Term 2, 2021)
  • CS258: Databases (Term1, 2021)
  • CS909 Data Mining (Term2, 2022)
  • CS258 Databases (Term1, 2022)


  • Kurmanji, M. and Triantafillou, P., 2022. Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data. arXiv preprint arXiv:2210.05508.
  • Shanghooshabad, A.M., Kurmanji, M., Ma, Q., Shekelyan, M., Almasi, M. and Triantafillou, P., 2021, June. PGMJoins: Random Join Sampling with Graphical Models. In Proceedings of the 2021 International Conference on Management of Data (pp. 1610-1622).
  • Ma, Q., Shanghooshabad, A.M., Almasi, M., Kurmanji, M. and Triantafillou, P., 2021. Learned Approximate Query Processing: Make it Light, Accurate and Fast. In CIDR.
  • Kurmanji, M., Ghaderi, F. (2020). 'Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study', Journal of AI and Data Mining, 8(2), pp. 177-188. doi: 10.22044/jadm.2019.7903.1929
  • Kurmanji, M. and Ghaderi, F., 2019, April. A comparison of 2D and 3D convolutional neural networks for hand gesture recognition from RGB-D data. In 2019 27th Iranian Conference on Electrical Engineering (ICEE) (pp. 2022-2027). IEEE.