Recent advances in additive manufacturing enable the fabrication of complex parts with intricate geometries and spatially-varying material composition. Data fusion integrates point cloud data with chromatic attributes, yielding 4D point clouds, a rich representation that jointly encodes shape and material information. We introduce a registration-free framework for jointly monitoring shape and...
Uncertainty quantification for complex physical systems often relies on computationally expensive numerical simulators. When execution times limit the number of feasible runs, surrogate modeling becomes essential for tasks such as sensitivity analysis, design optimization, and safety assessment. Gaussian process regression (GPR) is a leading
surrogate due to its uncertainty...
Skillful predictions in climate and environmental science are essential for planning operations, assessing risks, guiding adaptation strategies, and building resilience. This talk synthesizes key concepts and methods for enhancing predictive performance in these domains, with a particular emphasis on predictive uncertainty estimation, extreme event prediction, and the role of big datasets and...
Machine vision systems are important in Industry 4.0 as they allow fast automated inspection and quality control. Traceable metrology for machine vision systems is critical for the digital transformation of the Industry 4.0 objectives defined by the EU Green Deal. Nevertheless, these systems currently lack well-defined uncertainty frameworks and calibration techniques. For contactless 3D...
In industrial machine vision, camera positioning is traditionally a manual, iterative, trial-and-error process. Even if sufficient accuracy can be reached, this leads to prolonged downtime during initial installation and maintenance, especially for inspection tasks where the camera must be positioned at a precise location, orientation, and working distance. In addition, the operator-dependent...
Join us for one of the most dynamic and interactive sessions of the ENBIS conference — ENBIS-Live: Open Problem Solving in Action!
This is no ordinary talk. In this fast-paced, high-energy session, statisticians and data scientists roll up their sleeves to tackle real-world open problems — live and on the spot. Think of it as a collaborative brain trust powered by the collective wisdom and...
Efforts to mitigate public health crises have been complicated by unreported cases and the ever-changing trends of those monitored health events across geographic regions and socioeconomic cultures. To resolve both challenges, we propose a Bayesian spatiotemporal susceptible-exposed-infected-recovered-removed (BayST-SEIRD) framework that builds the hidden effects of neighboring communities,...
We present a unified perspective on explicit functional ANOVA as a principled decomposition framework for black-box models, bridging explainability, sensitivity analysis, and algorithmic understanding. We derive an exact closed-form functional ANOVA for categorical inputs, valid under arbitrary dependence structures and even on sparse or non-rectangular supports, thereby removing a major...
Statistical jump models have been recently introduced to detect persistent regimes by clustering temporal features while discouraging frequent regime changes. However, they rely on hard clustering and therefore do not account for uncertainty in state assignments.
In this work, we propose a fuzzy extension of the statistical jump model that incorporates uncertainty in cluster membership....
Vision-based systems in industrial applications involve a wide range of software, including fitting, association, and cloud-to-cloud registration. Software verification is required to guarantee the accuracy of estimated parameters. Verification typically relies on realistic datasets generated using ray casting to sample points on the predefined surface, followed by the addition of random...
This session offers a practical and thought-provoking exploration of how generative AI and large language models are transforming the daily work of statisticians, data scientists, and educators.
We start with a concise “kaleidoscope” of real examples illustrating what modern general-purpose AI tools can achieve in practice, with demonstrations that show how complex tasks can now be...
Design of Experiments (DOE) is powerful but rarely intuitive. What is wrong with poking around in design space? Why not vary one factor at a time? The mathematics answers clearly, but the classroom often doesn't.
Physical experiments — where participants can see, touch, and interact with a real system — bring DOE concepts to life. They make abstract ideas like screening, response surface...
Quality control methods such as measurement systems analysis, control charts, capability studies, and design of experiments are central to modern manufacturing and increasingly used in service industries. However, many established software solutions (e.g., Minitab, JMP) are costly or require substantial technical expertise (e.g., R, Python). In this presentation, we introduce the Quality...
JMP continues to develop powerful capabilities for statisticians and data scientists in industry. In the session we will demonstrate capabilities in JMP and JMP Pro 19 for Bayesian Optimization of multiple responses, and a new Causal Inference platform that makes establishing causality from observational data easily accessible to non-statistician researchers. We will also give a preview of new...
Although the CLIC-based model selection approach is widely used to identify spatial extreme models, the complexity of the associated statistical inference limits the reliability of this criterion. In addition, the strong spatial dependence in small or moderate regions may lead to substantial overlap among the spatial extremes models. This potential overlap increases the risk of model...
Understanding extreme environmental phenomena is crucial for risk management in a changing climate. In particular, dry spells, defined as consecutive days without precipitation, play a key role in drought dynamics, with direct impacts on agriculture, water resources, and insurance systems. Dry spell lengths are inherently discrete and often exhibit complex dependence structures across...
Berry greenhouses in the Souss-Massa region of Morocco sustain high-value exports of strawberries, blueberries, and raspberries, but their yield and fruit quality are highly sensitive to microclimate deviations. Dense IoT sensor networks generate high-dimensional, autocorrelated, and non-stationary data that violate classical Shewhart, CUSUM, and MEWMA assumptions.
We propose a hybrid...
Minitab DoE by Effex platform has been expanded to handle random blocks and complex split-plot structures with up to five levels of difficult-to-change factors. In this talk, we will first explain how random factors are considered when generating an optimal design. Then, we will explain how to assess the trade-off between run size and the quality of competing optimal design candidates. Next,...
The increasing availability and complexity of data are transforming decision-making processes across science, industry, and engineering. Modern datasets are often high-dimensional, heterogeneous, and structured over space and time, and are collected on domains with complex geometries, including environmental domains, biological structures, and engineering systems. In many applications, the...
Quantile-oriented sensitivity analysis allows to quantify uncertainty around quantiles, at different levels, while sensitivity analysis is often focused on deviation around mean (as it involves variances). We will consider qunatile-oriented sensitivity indices (QOSA) and quantile-oriented Shapley effects (QOSE). We will present their relevance on some analytical examples, show how to estimate...
We are motivated by the field of air quality control, where one goal is to quantify the impact of uncertain inputs such as meteorological conditions and traffic parameters on pollutant dispersion maps. Sensitivity analysis is one answer, but the majority of sensitivity analysis methods are designed to deal with scalar or vector outputs and are badly suited to an output space of maps. To...
The value of information (VOI) is a decision sensitivity measure that quantifies the expected improvement in decision quality when uncertainty in selected inputs is removed. Unlike many other sensitivity measures, the VOI provides not only a relative ranking of factors but also an absolute metric of decision quality. Despite this, its use has been limited, particularly to decision problems...
Time-series classification faces recurring challenges, including high dimensionality, autocorrelation, and the difficulty of identifying features that capture essential dynamics across temporal scales and phase shifts. We address these issues through shapelet decomposition, a technique that extracts shape-based features from time series while preserving both temporal and frequency information....
In this contribution, we focus on small-area compositional data. These data are defined as vectors whose elements are strictly positive and sum to one (e.g., proportions). Compositional data arise in various fields, including medicine, economics, psychology, and environmetrics. They are defined on the D-part simplex (S^D) and require complex techniques for proper analysis.
A traditional...
In modern industrial settings, advanced acquisition systems allow for the collection of data in the form of profiles, that is, functional relationships linking responses to explanatory variables. In this context, statistical process monitoring (SPM) aims to assess the stability of profiles over time in order to detect unexpected behavior. This talk focuses on SPM methods that model profiles as...
The truth is that some error, no matter how hard we try, simply can’t be modelled away. That irreducible error that stubbornly remains, no matter how time we have spent selecting predictors, or agonising over parameter tuning. Accepting that there will always be some randomness in statistics goes a long way to helping manage a technical team.
In this talk, Sophie will draw on her own...
We consider a stochastic decision-making system with unknown parameters that need to be estimated to make appropriate decisions. We take the standard approach of exploring first and then exploiting. We start with a stylized model but present numerous applications in restaurant bookings, bike-share replinishments, customized order-fulfilment, air traffic control, virtual queueing systems, and...
Identifying predictors associated with specific response categories in multinomial logistic regression is a challenging task. It is furthermore complex in a high-dimensional setting, where the number of covariates is higher than the number of units. To address the variable selection in high dimensional domain and in the presence of multinomial models with unordered responses, we propose a...
Artificial Intelligence (AI) has shown become very popular as modelling strategy within statistical process monitoring (SPM), particularly in detecting abnormal process behaviours. However, for existing AI-based SPM methods, diagnosing features associated with signal remains challenging, as traditional diagnosis methods are not directly applicable. This lack of diagnosis makes it difficult to...