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ENBIS-26 Conference

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

Chair of the Local Organising Committee

Session

INFORMS Session - Quality Statistics Reliability (QSR)

Sep 9, 2026, 9:00 AM

Conveners

INFORMS Session - Quality Statistics Reliability (QSR)

  • Mostafa Reisi Gahrooei (University of Florida)
  • Christian Capezza (Department of Industrial Engineering, University of Naples "Federico II")

Presentation materials

There are no materials yet.

  1. 70. Response Grid Plots for Model-Agnostic Explainable AI
    Russell Barton (Pennsylvania State University)
    9/9/26, 9:00 AM
    Other/special session/invited session

    Machine learning (ML) models are used to provide predictive characterization of response functions based on observed or simulated data. Insight on the nature of the approximation to the underlying response function is essential for the model output to be trusted and used by decision makers, a key part of exlainable AI. When a response function is well-behaved, methods that identify marginal...

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  2. 77. FedCOT: Personalized Federated Transfer Learning With Conditional Optimal Transport for Manufacturing Predictive Modeling
    Jia Liu (University of Florida)
    9/9/26, 9:30 AM
    Other/special session/invited session

    Effective predictive modeling in large-scale manufacturing is hampered by the isolated and limited data from individual organizations, collected from costly experiments and various inspections. Collaboration across organizations can handle these limitations, but it faces two main challenges: privacy concerns over organizations and heterogeneous features from varied sensing and inspection...

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  3. 127. Spatial–Temporal Large Language Models for Time-Series Data
    Ziyue Li (Technical University of Munich)
    9/9/26, 10:00 AM
    Other/special session/invited session

    Since 2023, large language models (LLMs) have begun to reshape the landscape of time-series analysis. In this talk, we present our latest research, insights, and perspectives on using LLMs to model time-series data— both as a standalone modality and in combination with other spatial or contextual information/modality. We will explore three key questions:
    (1) What are spatiotemporal LLMs?
    (2)...

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