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Dr. Alessio Benavoli

Associate Professor (Statistics)
      
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Dr. Alessio Benavoli

Associate Professor (Statistics)

 ALESSIO.BENAVOLI@tcd.ie

 


Alessio Benavoli is an internationally recognised researcher in statistics, probabilistic machine learning, and automation. He received his Master's degree (2004) and his Ph.D. (2008) in Computer and Control Engineering from the University of Florence, Italy. From 2007 to 2008, he worked for the international company SELEX-Sistemi Integrati as system analyst. From 2008 to 2019, he was at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Lugano, Switzerland, becoming full professor in 2018. From 2019 to 2021, he was Senior Lecturer in Artificial Intelligence and Machine Learning (ML) at the University of Limerick (Ireland). Currently, he is Associate Professor in Statistics at the School of Computer Science and Statistics, Trinity College Dublin. Alessio has ~130 technical peer-reviewed publications in main scientific journals and conferences in AI, machine learning (ML) and statistics, and his research bridges AI and ML, data science and engineering. He has 16 years experience in developing statistical models and AI-based systems for industry with applications to smart manufacturing, quantitative finance, green energy production forecast and defence sector.
  Artificial Intelligence/Cybernetics   Control Theory (Computer Sciences)   Data Analysis   Probabilistic Machine Learning   Probability   Quantum information physics   Statistics
Project Title
 Humans-in-the-Loop towards a more effective AI in manufacturing
From
01/01/2023
To
30/06/2024
Summary
The adoption of Artificial Intelligence (AI) for Process Optimisation (PO) in manufacturing can significantly contribute to make the Irish manufacturing sector more competitive and sustainable. However, its adoption has been slower than expected. Looking at the way AI is being used in PO, the issue is the need of tailoring AI-solutions for every machine tool and context (used material, shape of the manufactured object). With this approach, the industry will need "tens of thousands"" of unique AI- models, which is clearly infeasible. Instead of developing an AI-solution for each machine tool and context, HLOOP proposes to build a single AI-model exploiting the synergy between AI and human-workers. Assessing the quality of the manufacturing process is more effectively done by humans than AI, while AI is better at dealing with high-dimensional decision problems. We plan to use the feedback of machine operators to train on-the-fly (that is without using any pre-collected dataset) and in real-time an AI-model that can predict when the manufacturing process is good/bad, and then optimise it. https://hloop.scss.tcd.ie/
Funding Agency
SFI
Programme
Future Digital Challenge
Person Months
2
Project Title
 Data analytics for the development of smart infusion pumps
From
01/06/2021
To
01/06/2023
Summary
Funding Agency
BD & SFI-Confirm
Person Months
0

Alessio Benavoli and Dario Azzimonti, Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability, The 40th Conference on Uncertainty in Artificial Intelligence, 2024, Conference Paper, PUBLISHED
Branko Ristic and Alessio Benavoli, Credal Valuation Network for Ongoing Threat Assessment, 2024 27th International Conference on Information Fusion (FUSION), 2024, pp1--7 , Conference Paper, PUBLISHED
Branko Ristic and Alessio Benavoli and Sanjeev Arulampalam, Bayes†Rule Using Imprecise Probabilities [Lecture Notes], IEEE Signal Processing Magazine, 41, (1), 2024, p67-71 , Journal Article, PUBLISHED  DOI
Alessio Benavoli and Dario Azzimonti and Dario Piga, Learning Choice Functions with Gaussian Processes, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, edited by Robin J. Evans and Ilya Shpitser , 216, PMLR, 2023, pp141--151 , Conference Paper, PUBLISHED
Alessio Benavoli and Dario Azzimonti and Dario Piga, Bayesian Optimization For Choice Data, 2023 Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion), July 15--19, 2023, Lisbon, 2023, Conference Paper, PUBLISHED  DOI
Arianna Casanova and Alessio Benavoli and Marco Zaffalon, Nonlinear desirability as a linear classification problem, International Journal of Approximate Reasoning, 152, 2023, p1-32 , Journal Article, PUBLISHED  DOI
Ristic, Branko and Benavoli, Alessio and Arulampalam, Sanjeev, Credal Valuation Networks for Machine Reasoning Under Uncertainty, IEEE Transactions on Artificial Intelligence, 2023, p1-10 , Journal Article, PUBLISHED  DOI
Branko Ristic and Alessio Benavoli and Alex Skvortsov, Robust target area search using sets of probabilities, Digital Signal Processing, 142, 2023, p104195 , Journal Article, PUBLISHED  DOI
Arash Kia and James Waterson and Norma Bargary and Stuart Rolt and Kevin Burke and Jeremy Robertson and Samuel Garcia and Alessio Benavoli and David Bergström, Determinants of Intravenous Infusion Longevity and Infusion Failure via a Nonlinear Model Analysis of Smart Pump Event Logs: Retrospective Study, JMIR AI, 2, 2023, pe48628 , Journal Article, PUBLISHED  DOI
Enrique Miranda, Ignacio Montes, Erik Quaeghebeur, Barbara Vantaggi(ed.), Closure operators, classifiers and desirability, 2023, Proceedings of a Conference, PUBLISHED
  

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My main research interests are in the areas of Bayesian parametric and non-parametric statistics; foundation of probability theory and desirable gambles; quantum mechanics; decision-making under uncertainty; prior near-ignorance; dynamical systems and control; with applications to data analytics, machine learning and control theory.