Sections

The NEJSDS welcome submissions including (but not limited to) the following types.

  • Methodology Article: Novel statistical, analytical, computational and data management/manipulation methods.
  • Case Studies, Applications and Practice Article: Innovative applications of existing methods and practices in real-life studies.
  • Timely Review Article: Review and summary of notable developments in quantitative methods, software, data management, and other noteworthy aspects.
  • Commentaries and Historical Perspectives: Perspectives of important research topics and historical notes.
  • Software Tutorials and Reviews: Educational notes on the step-by-step use of new software packages and insightful comments and suggestions about their pros and cons.
  • The journal currently has the following sections:


    Biomedical Research

    Editor: Colin O. Wu (wuc@nhlbi.nih.gov)

    Associate Editors:

  • Jinbo Chen, University of Pennsylvania
  • Yong Chen, University of Pennsylvania, Perelman School of Medicine
  • Leslie Cope, Johns Hopkins University
  • Hyokyoung (Grace) Hong, NCI/NIH
  • Sung Duk Kim, NCI/NIH
  • Elizabeth Schifano, University of Connecticut
  • Yifei Sun, Columbia University
  • Xin Tian, NHLBI/NIH
  • Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health
  • Aims: The NEJSD Biomedical Research section publishes original research and timely review articles of statistical and data science theory/methods, computational algorithms, data management and applications in all types of biomedical research, including biological science, health science, biopharmaceutical and biotechnological research. Scopes of research include innovative designs and analytical methods of clinical trials, observational cohort studies, electronic health records, biological experiments, among others.


    Cancer Research

    Co-Editors:Yuan Ji (jiyuan@uchicago.edu), University of Chicago and Ying Lu (ylu1@stanford.edu), Stanford University

    Associate Editors:

  • Brian Hobbs, The University of Texas at Austin
  • Bo Huang, Pfizer
  • Ying Huang, Fred Hutch
  • Gang Li, University of California, Los Angeles
  • Sheng Luo, Duke University
  • Arvind Rao, University of Michigan
  • Lorenzo Trippa, Harvard University
  • Yanxun Xu, Johns Hopkins University
  • Aims: Discovery: Statistical models for bioinformatics and biomedical data in cancer. Clinical translation: Statistical methods for cancer clinical trials for cancer diagnosis and treatment. Population Science: Statistical models and methods for cancer population research including prevention and surveilance.


    Engineering Science

    Editor: Feng Guo (feng.guo@vt.edu), Virginia Polytechnic Institute and State University

    Associate Editors:

  • Linda Boyle, University of Washington
  • Carol Flannagan, University of Michigan
  • Qing Li, Iowa State University
  • Aims: The NEJSD Engineering Science section publishes original statistical methodology and application research in the field of engineering science, including but not limited to traffic engineering, industrial and system engineering, and electronic engineering. Scopes of the research includes statistical methodology for emerging engineering data, observational and experimental designs methods, risk assessment, and prediction.


    Machine Learning and Data Mining

    Editor: Ali Shojaie (ashojaie@uw.edu), University of Washington

    Associate Editors:

  • Jelena Bradic, University of California
  • Jian Kang, University of Michigan
  • Mladen Kolar, University of Chicago
  • Eric Laber, Duke University
  • Yufeng Liu, The University of North Carolina at Chapel Hill
  • Po-Ling Loh, Colombia, Statistics
  • George Michailidis, University of Wisconsin-Madison
  • Annie Qu, University of California, Irvine
  • Aaditya Ramdas, Carnegie Mellon University
  • Aims: The NEJSDS Machine Learning and Data Mining Section aims to disseminate original methodological, theoretical and computational research on statistical approaches for machine learning and data mining. The section seeks articles in both foundations of machine learning and artificial intelligence as well as novel applications across diverse scientific disciplines. The section also welcomes review articles on cutting-edge topics.


    NextGen

    Editor: Moinak Bhaduri (MBHADURI@bentley.edu), Bentley University

    Associate Editors:

  • Kaushik Ghosh, University of Nevada Las Vegas
  • Davit Khachatryan, Babson College
  • Shaoyang Ning, Williams College
  • Anna Plantinga, Williams College
  • Ray Stefani, California State University
  • Elizabeth Upton, Williams College
  • Justin Zhan, University of Arkansas
  • Qingyang Zhang, University of Arkansas
  • Aims: The newly formed NextGen strives to attract and engage young enthusiasts on problems confronting modern data science. Our section of the journal serves as a platform to further that goal. Articles may describe novel methodologies, applied aspects, and may take the form of expository data analysis or writing, highlighting key facets of conducting and teaching statistics and data science. Case studies, data sets, historical perspectives, review articles, short videos, and statistical art projects are equally encouraged. Contributions from early-career statisticians, graduate/undergraduate students, K-12 kids and their teachers, and those new to data science are especially welcome. The honest value of our column is in creating a climate for young writing to flourish and thrive alongside those from seasoned practitioners, nurturing a spirit of hope and encouragement, and fostering the sense that no matter how deep you have ventured into data science, no matter how different your take on a problem is from established wisdom, if you have an intriguing tale to tell, you would be heard, and if you can write strong, you would be celebrated.


    Software

    Editor: Haim Bar (haim.bar@uconn.edu), University of Connecticut

    Associate Editors:

  • Patrick Flaherty, University of Massachusetts Amherst
  • William Evan Johnson, Boston University
  • Aims: The NEJSDS Software section publishes practical and theoretical articles on all aspects of software development in the context of statistics and data science. Since software tools and methodologies are evolving rapidly, an objective of this section is to disseminate timely information about recent trends and developments in all areas related to statistical practice, computational methods, data sources, computing languages, databases, and hardware, as they pertain to data science applications. Also within the scope of this section are review papers and detailed tutorials about specific tools and implementations.


    Statistical Methodology

    Editor: Grace Yi (gyi5@uwo.ca), Western University

    Associate Editors:

  • Jianwen Cai, University of North Carolina at Chapel Hill
  • Luis Carvalho, Technical University of Munich, Germany
  • Claudia Czado, University of California, Berkeley
  • Peng Ding, University of Toronto, Canada
  • Mike Evans, University of Toronto, Canada
  • Yulia Gel, University of Michigan
  • Xuming He, University of Michigan
  • Michael Levine, Purdue University
  • Ana-Maria Staicu, North Carolina State University
  • Changbao Wu, University of Waterloo, Canada
  • Yiqiao Wu, The University of Illinois at Chicago
  • Chunming Zhang, University of Wisconsin, Madison, USA
  • David Zucker, Hebrew University of Jerusalem, Isarel
  • Aims: The NEJSD Statistical Methodology Section aims to disseminate original research on statistical theory and methodology for a wide range of topics, with an emphasis on relevance to statistical practice and data science. The section seeks articles making broad points of interest to readers with diverse backgrounds in statistics, biostatistics, applied probability, economics, or data science. While the section focuses on publishing research manuscripts, it also welcomes review articles on cutting-edge topics.


    Spatial and Environmental Statistics

    Editor: Gavino Puggioni (gpuggioni@uri.edu), The University of Rhode Island

    Associate Editors:

  • Veronica Berrocal, University of California, Irvine
  • Michela Cameletti, University of Bergamo
  • Rajarshi Guhaniyogi, University of California, Santa Cruz
  • Matthew Heaton, Brigham Young University
  • Huiyan Sang, Texas A&M University
  • Aims: The NEJSDS Spatial and Environmental Statistics section emphasizes original research that uses statistics to interpret the complexity and the rapidly changing features of our environment. This section welcomes manuscripts focused on theoretical contributions, innovative methodological approaches, computational advances in data analysis and visualization, and novel applications in the areas of spatial and spatio-temporal statistics, epidemiology, public health, ecology, and environmental sciences. Review articles in areas of frontier research, as well as discussions on current open problems and future directions will also be considered.