Souhaib Ben Taieb is an Associate Professor of Machine Learning at the University of Mons (UMONS) in Belgium, where he leads the Big Data and Machine Learning Lab . His research spans machine learning, artificial intelligence, time series forecasting, uncertainty quantification, temporal point processes, and anomaly detection.

Previously, he served as a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Australia. Souhaib holds a B.Sc and M.Sc in Computer Science from the Free University of Brussels (ULB) and earned his Ph.D. specializing in machine learning from ULB under the supervision of Prof. Gianluca Bontempi (ULB) and Prof. Rob Hyndman (Monash University), supported by a Doctoral research fellowship from the Belgian National Fund for Scientific Research. He worked as a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST under the supervision of Prof. Marc G. Genton . Souhaib received the Solvay Award for his Ph.D. thesis, and he has successfully obtained various research grants both within academia and in collaboration with industry. He is an Associate Editor of the International Journal on Forecasting and serves as a reviewer for esteemed machine learning conferences such as NeurIPS, ICML, ICLR, AISTATS, and KDD.

Recent publications

  • Xiaochun Meng, James W. Taylor, Souhaib Ben Taieb, Siran Li (2023) Scores for Multivariate Distributions and Level Sets. Operations Research (To appear). Abstract Arxiv
  • Souhaib Ben Taieb, Tanguy Bosser (2023) On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. Transactions on Machine Learning Research(To appear). Abstract Arxiv
  • Victor Dheur, Souhaib Ben Taieb (2023) A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 2023.. Abstract Arxiv
  • Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski (2023) HierarchicalForecast - A Reference Framework for Hierarchical Forecasting in Python. Abstract Arxiv
  • Souhaib Ben Taieb (2022) Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. Proceedings of the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, Valencia, Spain. PMLR, Volume 151.. Abstract  pdf