ENBIS-19 in Budapest

2 – 4 September 2019; Eötvös Loránd University, Budapest Abstract submission: 16 December 2018 – 29 April 2019

ENBIS (the European Network for Business and Industrial Statistics) is a platform where statistical practitioners and academic statisticians from Europe and beyond meet, exchange ideas and design new projects. Individual membership of ENBIS is free. Many ENBIS network activities are web-based, but some take place face-to-face. Spring meetings are always dedicated to a special topic (e.g. data mining, reliability, DOE, etc.), whereas the annual conference (which traditionally takes place every September) hosts presentations from a wide variety of sectors, ranging from manufacturing to service, from private to public sector. Conference contributions can consist of introductions and overviews (presenting an area of expertise to the uninitiated), reviews (presenting state of the art advances in scientic research), and best practice communications (sharing experiences of practitioners). In short: The annual conference features both theoretical and practical papers. In addition, people from industry and business are invited to present their problems and current solutions and pick the brains of other conference participants to find better solutions than their current ones.

The annual conference session topics include but are not limited to, the special areas of interest that exist within ENBIS (formalised in the so-called Special Interest Groups). The non-exhaustive list includes:

  • Design of Experiments
  • Measurement Uncertainty
  • Process Modeling and Control
  • Reliability and Safety
  • Statistics in the Pharmaceutical Industry
  • Statistical Computing
  • Operational Risk Management
  • Statistics in Practice
  • Stochastic Modelling
  • Quality Improvement and Six Sigma
  • Data Mining and Warehousing
  • Machine Learning
  • Predictive Analytics
  • Statistical Education for Business and Industry
  • Business and Finance

The annual conference also features distinguished keynote speakers, special thematic sessions, workshops and panel discussions, as well as pre- and post-conference courses and an industrial visit.

Confirmed keynotes for the ENBIS-19 conference in Budapest are:

  • Christine M. Anderson-Cook (Los Alamos National Laboratory, USA), on “Big Decisions: How Statisticians Can Contribute”.
  • Andrea Saltelli (University of Bergen, Norway), on “Sensitivity Analysis. An introduction”.
  • Ronald J.M.M. Does (University of Amsterdam, the Netherlands, Box Medal recipient)
  • Rebecca Killick (Lancaster University, England, Young Statistician Award recipient)
  • Eugene Tuv (Intel Corp, USA, Best Manager Award)

Highlights of the ENBIS Annual Conference 2019 include:

  • Eight Organized Sessions related to the most important Journals and/or Statistical Associations; among others: the ISBIS Session, the JQT, Technometrics and QE Session, the US Session, the Latin American Session, the Bayesian Methods in Industrial Statistics (ISBA session), and the European Statistical Engineering Session.
  • Eleven Special Sessions that spread among many topics, such as: Uncertainty for deep learning, Business Statistics, Kansei Engineering, Statistical Fusion of Heteregenous Measurement Networks, Competences and Skills in Business World, Modern Approach to Process Monitoring, Reliability, and others.
  • Finally, two Special sessions dedicated to Young Statisticians,  and to Statistical Software, like JMP and StatEase

The following pre- and post-conference events in the framework of ENBIS-19 can also be booked (click on the course title for more information):

  1. A joint ECAS-ENBIS 1-Day Summer Course on September 1st, with the topics:
    - New Product Growth Models: Theory, Practice and a Look to the Future by Mariangela Guidolin (University of Padua, Italy)
    - Causality: A Half Day Workshop by Ron S. Kenett (KPA Ltd and the Samuel Neaman Institute, Technion, Israel)
  2. Functional Data Analysis by Chris Gotwalt (JMP Division of SAS Institute, USA), on September 4th,
  3. Random Forests by Jean-Michel Poggi (Paris-Descartes University and Lab. Maths Orsay, France), on September 5th