Conveners
Machine Learning: 1
- Shirley Coleman (NU Solve, Newcastle University)
Machine Learning: 2
- Jean-Michel Poggi (University of Paris-Saclay)
Machine Learning: 3
- Fabian Mies (Delft University of Technology)
Machine Learning: 4
- Fabian Mies (Delft University of Technology)
Machine Learning: 5
- Fabian Mies (Delft University of Technology)
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Prof. Linda Ho (University of São Paulo)16/09/2025, 09:00Machine Learning
Project monitoring practices have significantly evolved over the past decades. Initially grounded in traditional methodologies such as Earned Value Management (EVM), these practices have advanced to incorporate control charts and sophisticated techniques utilizing Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict final project costs and durations. Despite these...
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MICHAIL MAKRIS (UNIPI)16/09/2025, 09:20Machine Learning
Retrieval-Augmented Generation (RAG) offers a robust way to enhance large language models (LLMs) with domain-specific knowledge via external information retrieval. In banking—where precision, compliance, and accuracy are vital—optimizing RAG is crucial. This study explores how various document parsing, chunking, and indexing techniques influence the performance of RAG systems in banking...
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Dr Yariv N. Marmor (BRAUDE - College of Engineering, Karmiel)16/09/2025, 09:40Machine Learning
Purpose: Industrial applications increasingly rely on complex predictive models for process optimization and quality improvement. However, the relationship between statistical model performance and actual operational benefits remains insufficiently characterized. This research investigates when model complexity provides genuine business value versus statistical...
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Prof. Amalia Vanacore (Department of Industrial Engineering University of Naples Federico II), Armando Ciardiello (Dept. of Industrial Engineering, University of Naples Federico II; Deloitte Consulting SRL SB)16/09/2025, 10:05Machine Learning
Timely crop yield estimation is a key component of Smart Agriculture, enabling proactive decision-making and optimized resource allocation under the constraints of climate variability and sustainability goals. Traditional approaches based on manual sampling and empirical models are constrained by labour intensity, limited spatial coverage, and sensitivity to within-block (-site) heterogeneity....
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Youba ABED (IFPEN, Rond-point de l'échangeur de Solaize, France, Université lyon 2, Université Claude Bernard Lyon1, ERIC, 69007, Lyon, France)16/09/2025, 10:25Machine Learning
The IFP group is a leader in research and training in the energy and environmental sector, particularly in the development and commercialization of catalysts. Building accurate predictive models for these catalysts usually requires expensive and time-consuming experiments. To make this process more efficient, it’s helpful to leverage existing data from previous generations of catalysts. This...
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Mahmoud Awad (American University of Sharjah)16/09/2025, 10:45Machine Learning
Manual Welding is an important manufacturing process in several industries such as marine, automotive and furniture among others. Despite the widespread welding, it still causes a significant percentage of rework in many companies, especially small to medium sized companies. The objective of this project is to develop an economic online monitoring method for detecting defective welds using...
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Chris Gotwalt (JMP Division of SAS Institute)16/09/2025, 15:20Machine Learning
Bayesian Optimization (BO) has received tremendous attention for optimizing deterministic functions and tuning ML parameters. There is increasing interest in applying BO to physical measurement data in industrial settings as a recommender system for product/process design. In this context multiple responses of interest are the norm, but "basic" BO is only defined minimization/maximization of a...
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Konstantinos Bourazas (Athens University of Economics and Business)16/09/2025, 15:40Statistical Computing
In this work, we address the problem of binary classification under label uncertainty in settings where both feature-based and relational data are available. Motivated by applications in financial fraud detection, we propose a Bayesian Gaussian Process classification model that leverages covariate similarities and multilayer network structure. Our approach accounts for uncertainty in the...
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Dr Laura Marie Helleckes (Department of Chemical Engineering, Imperial College London)16/09/2025, 16:00Machine Learning
Early process development in biopharma traditionally relies on small-scale experimentation, e.g. microtiter plates. At this stage, most catalyst candidates (clones) are discarded before process optimisation is conducted in bioreactors at larger scales, which differ significantly in their feeding strategies and process dynamics. This disconnect limits the representability of small-scale...
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Abdelaziz Berrado (Mohammed V University in Rabat, EMI)16/09/2025, 16:25Machine Learning
PRIM is a Bump Hunting algorithm traditional used in a supervised learning setting to find regions in the input variables subspace while being guided by the data analyst, that are associated with the highest or lowest occurrence of a target label of a class variable.
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We present in this work a non-parametric PRIM-based algorithm that involves all the relevant attributes for rule generation... -
Nathan Gaw (Air Force Institute of Technology)16/09/2025, 16:45Machine Learning
Data privacy is a growing concern in real-world machine learning (ML) applications, particularly in sensitive domains like healthcare. Federated learning (FL) offers a promising solution by enabling model training across decentralized, private data sources. However, both traditional ML and FL approaches typically assume access to fully labeled datasets, an assumption that rarely holds in...
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Kyriakos Skarlatos (University of Piraeus)16/09/2025, 17:05Machine Learning
Real-time monitoring systems play a crucial role in detecting and responding to changes and anomalies across diverse fields such as industrial automation, finance, healthcare, cybersecurity, and environmental sensing. Central to many of these applications is multivariate statistical process monitoring (MSPM), which enables the concurrent analysis of multiple interrelated data streams to...
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Panagiotis Biris (University of Patras)17/09/2025, 09:00Machine Learning
Real world datasets frequently include not only vast numbers of observations but also high dimensional feature spaces. Exhaustively gathering and examining every variable to uncover meaningful insights can be time consuming, costly, or even infeasible. In order to build up robust, reliable and efficient regression models, feature selection techniques have therefore become inevitable. Yet many...
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Grigorios Papageorgiou (University of Patras)17/09/2025, 09:20Machine Learning
Extracting meaningful insights from vast amounts of unstructured textual data presents significant challenges in text mining, particularly when attempting to separate valuable information from noise. This research introduces a novel deep learning framework for text mining that identifies latent structures within comprehensive text corpora. The proposed methodology incorporates an initial...
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Emma Pajak (Department of Chemical Engineering, Imperial College London)17/09/2025, 09:40Machine Learning
A major challenge in the chemicals industry is coordinating decisions across different levels, such as individual equipment, entire plants, and supply chains, to enable more sustainable, autonomous operations. Multi-agent systems, based on large language models (LLMs), have shown potential for managing complex, multi-step problems in software development (Qian et al., 2023). This work...
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