معرفی
Jesse Read is a Professor at École Polytechnique (Institut Polytechnique de Paris) affiliated with the ORAILIX team within the Data Analytics and Machine Learning pôle of the Computer Science Laboratory (LIX). His work focuses on multi-label and probabilistic inference, explainable and robust methods, and learning from data streams and sequential data with applications in medicine, energy, and transportation. He obtained his PhD from the University of Waikato (2010) and held postdoc positions at INFRES Télécom Paris, Aalto University, and Universidad Carlos III de Madrid before becoming Assistant Professor (2017) and Professor (2019) at École Polytechnique.
Research Trends
- His recent publications emphasize multi-label learning (Classifier Chains, Regressor Chains) and data stream analysis with applications in degradation modeling and medical diagnostics.
- Key methodological contributions include probabilistic inference in chained models, scalable ensemble learning, and Monte Carlo optimization for sequential tasks.
- Application domains span healthcare (ECG analysis, insomnia detection), energy systems (wind turbine control), and transportation (route prediction).
Scientific Awards
- Test of Time Award at ECML-PKDD 2019 for foundational work on Classifier Chains
- Habilitation à Diriger des Recherches (HDR) in Computer Science (2017)
Advising & Grants
Read has supervised PhD theses from Ekaterina Antonenko (Multi-Target Learning) and Olivier Pallanca (Paradoxical Insomnia Analysis). He secured an ANR grant for Dynamic Graph Signal Processing with Dr. Johannes Lutzeyer and Dr. Luca Martino.
