Editorial Team

Editor-in-Chief: Dr. Samantha Chen

  • Affiliation: Department of Computer Science, University of Cambridge, UK
  • Research Interests: Deep learning, computer vision, interpretable machine learning

Associate Editors:

  1. Prof. Javier Rodriguez

    • Affiliation: Department of Electrical Engineering, Stanford University, USA
    • Research Interests: Reinforcement learning, robotics, autonomous systems

  2. Dr. Mei Ling

    • Affiliation: School of Information Technology, Tsinghua University, China
    • Research Interests: Natural language processing, machine translation, sentiment analysis

  3. Prof. Giovanni Rossi

    • Affiliation: Department of Statistics, University of Milan, Italy
    • Research Interests: Bayesian machine learning, probabilistic graphical models, statistical learning theory

Editorial Board Members:

  1. Prof. Sarah Johnson

    • Affiliation: School of Computer Science, Carnegie Mellon University, USA
    • Research Interests: Fairness and bias in machine learning, ethics of AI, social implications of technology

  2. Dr. Ahmed Khan

    • Affiliation: AI Research Lab, Google Research, USA
    • Research Interests: Large-scale machine learning, distributed systems, scalability of deep learning models

  3. Prof. Hiroshi Yamamoto

    • Affiliation: Institute of Industrial Science, University of Tokyo, Japan
    • Research Interests: Machine learning for manufacturing, supply chain optimization, industrial applications of AI

  4. Dr. Elena Petrova

    • Affiliation: Department of Computer Engineering, Moscow Institute of Physics and Technology, Russia
    • Research Interests: Federated learning, privacy-preserving machine learning, secure multi-party computation

The editorial team of ITML is committed to maintaining the highest standards of academic excellence and fostering the dissemination of innovative research in the field of machine learning. They oversee the peer review process, provide guidance on journal policies, and contribute to the strategic direction of ITML, ensuring its relevance and impact in the global research community.