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ENBIS-26 Conference

Sep 6 – 10, 2026
Centro Didattico Morgagni
Europe/Rome timezone

Chair of the Local Organising Committee

Session

Statistics and data science in the technological field: current issues and new proposals

Sep 7, 2026, 2:00 PM

Conveners

Statistics and data science in the technological field: current issues and new proposals

  • Nedka Dechkova Nikiforova (Department of Statistics Computer Science Applications “G.Parenti”- University of Florence)

Presentation materials

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  1. 18. ADeS: A Flexible Adaptive Design Strategy for Ethical and Covariate-Balanced Clinical Trials
    Marco Novelli (University of Bologna)
    9/7/26, 2:00 PM
    Other/special session/invited session

    Adaptive randomization in clinical trials often requires balancing competing goals: improving patient benefit, preserving statistical efficiency, and maintaining adequate randomness in treatment assignment. We propose the Adaptive Design Strategy (ADeS), a flexible group-sequential framework that unifies covariate-adaptive (CA), response-adaptive (RA), covariate-adjusted response-adaptive...

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  2. 134. Online Quality Monitoring in Selective Laser Melting via Image-Based Statistical Methods
    Panagiotis Tsiamyrtzis (Politecnico di Milano)
    9/7/26, 2:30 PM
    Other/special session/invited session

    A major challenge in Additive Manufacturing (AM) is the development of reliable in-situ and online quality monitoring methodologies. Visible and infrared cameras can provide near real-time image data that can be exploited for anomaly detection through Statistical Process Control and Monitoring (SPC/M) methods.
    This work investigates image-based monitoring methods for Selective Laser Melting...

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  3. 54. Fisher’s Principles of Exerimental Design and Why They Are Still Important
    Geoff Vining (Virginia Tech Statistics Department)
    9/7/26, 3:00 PM
    Other/special session/invited session

    Sir Ronald Fisher was one of the giants in both the fields of statistics and genetics. His seminal work was done at Rothamsted Experimental Station outside of London.

    Fisher was both a statistician and a scientist. He was well trained in mathematics, but in the final analysis, he was a scientist who understood how to perfom proper data analysis. Fisher strongly rejected the...

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