Mokhtar Z. Alaya
Scholar

Mokhtar Z. Alaya

Google Scholar ID: bmbmbusAAAAJ
LMAC Laboratory - University of Technology of Compiègne
Statistical LearningMachine/Deep LearningOptimal Transport
Citations & Impact
All-time
Citations
1,683
 
H-index
8
 
i10-index
8
 
Publications
20
 
Co-authors
0
 
Resume (English only)
Academic Achievements
  • - Paper: Application of Convolution Neural Network for Unfolding Simulated Neutron Spectra of an Activation Spectrometer (Submitted)
  • - Paper: Unmixing Mean Embeddings for Domain Adaptation with Target Label Proportion (Submitted)
  • - Paper: Bounds in Wasserstein Distance for Locally Stationary Functional Time Series (Submitted)
  • - Paper: PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection (EUSIPCO 2025)
  • - Paper: Sparsified-Learning for Heavy-Tailed Locally Stationary Processes (Preprint)
  • - Paper: Adversarial Semi-Supervised Domain Adaptation for Semantic Segmentation: A New Role for Labeled Target Samples (Computer Vision and Image Understanding)
Research Experience
  • - 2020 - Present: Maître de Conférences, LMAC, Department of Computer Science, UTC
  • - 2018 - 2020: Postdoctoral Researcher, LITIS Laboratory, University Rouen Normandy, involved in OATMIL Project
  • - 2017 - 2018: Postdoctoral Researcher, Modal'X Laboratory, University Paris Nanterre, working with Olga Klopp on collective matrix completion project
  • - 2017 - 2016: Temporary Teaching Researcher Assistant, Modal'X Laboratory, University Paris Nanterre
Education
  • - Ph.D. in Statistical Machine Learning, University Pierre and Marie Curie, 2016, Advisors: Stéphane Gaïffas and Agathe Guilloux
  • - Master of Sciences in Statistics, University Pierre and Marie Curie, 2012
  • - Master of Sciences in Probabilities and Random Modelling, University Pierre and Marie Curie, 2011
  • - Magisterium of Mathematics, University Gabes Tunisia, 2010
Background
  • - Research Interests: Statistics and Machine Learning
  • - Specialization: Supervised learning in high-dimensional settings, Bridging optimal transport and machine learning
  • - Brief Introduction: Since September 2020, Assistant Professor of Statistics and Machine Learning at the Université de Technologie de Compiègne (UTC), Department of Computer Science, Laboratory of Applied Mathematics (LMAC).
Co-authors
0 total
Co-authors: 0 (list not available)