Editorial Team

The editorial team of the International Transactions on Data Science (ITDS) comprises distinguished experts and scholars from diverse backgrounds in academia, industry, and research institutions. The team is dedicated to upholding the journal's standards of excellence, integrity, and relevance in advancing the field of data science. Here's an overview of the editorial team:

Editor-in-Chief: Dr. Samantha Lee

  • Affiliation: Department of Computer Science, Stanford University
  • Expertise: Machine Learning, Big Data Analytics, Data Privacy

Associate Editors:

  1. Dr. Javier Rodriguez

    • Affiliation: Amazon Research
    • Expertise: Natural Language Processing, Recommender Systems

  2. Dr. Mei Chen

    • Affiliation: IBM Research
    • Expertise: Healthcare Analytics, Predictive Modeling

  3. Dr. Rajesh Gupta

    • Affiliation: University of California, San Diego
    • Expertise: Data Mining, Scalable Algorithms

Editorial Board Members:

  1. Dr. Emily Johnson

    • Affiliation: Microsoft Research
    • Expertise: Fairness in Machine Learning, Ethical AI

  2. Dr. Mohammad Khan

    • Affiliation: Google AI
    • Expertise: Deep Learning, Computer Vision

  3. Dr. Alessandra Rossi

    • Affiliation: University of Cambridge
    • Expertise: Bayesian Statistics, Causal Inference

  4. Dr. Juan Martinez

    • Affiliation: MIT Sloan School of Management
    • Expertise: Business Analytics, Decision Support Systems

  5. Dr. Mei Ling

    • Affiliation: National Institutes of Health (NIH)
    • Expertise: Bioinformatics, Genomic Data Analysis

Managing Editor: Ms. Sarah Thompson

  • Affiliation: ITDS Editorial Office
  • Responsibilities: Overseeing manuscript submissions, peer review process, and journal operations.

The editorial team collaborates closely to ensure the timely and rigorous review of submitted manuscripts, maintain the quality and relevance of published content, and foster an inclusive and supportive scholarly community within the field of data science. They are committed to promoting interdisciplinary research, facilitating knowledge exchange, and contributing to the advancement of data science on a global scale.