Research output per year
Research output per year
Personal profile
Professional Information
Dr. Lucas Rizzo received his Ph.D. in Artificial Intelligence from the Technological University Dublin in 2020. Currently he is a Lecturer and MSc Coordinator at the same university. He has participated in a number of conferences and published several papers. Alongside research, he is currently supervising master students in data analytics and advanced software development. He has lectured several modules for undergraduate and graduate students in computer science, such as Object Oriented Programming, NoSQL databases and Data Wrangling (Python, R, and SQL). Previously he has worked as an optimisation analyst and performed research for the development of heuristics for computational hard problems.
Research Interests
Dr. Rizzo’s research focus is more specifically on automated reasoning and computational models of argument. Recently, he has been working in the field of XAI, trying to enhance computational argumentation and other knowledge-based systems through the use of machine learning algorithms and data-driven models.
Education/Academic qualification
Post-Graduate Diploma, Postgraduate Certificate in University Learning and Teaching, Technological University Dublin
23 Jan 2021 → 31 May 2021
Award Date: 31 May 2021
PhD, Artificial Intelligence: Evaluating the impact of defeasible argumentation as a modelling technique for reasoning under uncertainty, Technological University Dublin
1 Sep 2015 → 30 Apr 2020
Award Date: 1 Apr 2020
Master, Computer Science, Operational Research, Universidade Federal de Minas Gerais
1 Feb 2011 → 1 Apr 2013
Award Date: 1 Apr 2013
Bachelor, Computational Mathematics, Universidade Federal de Minas Gerais
1 Feb 2007 → 1 Dec 2010
Award Date: 1 Dec 2010
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI
Rizzo, L., Verda, D., Berretta, S. & Longo, L., Sep 2024, In: Machine Learning and Knowledge Extraction. 6, 3, p. 2049-2073 25 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
A novel structured argumentation framework for improved explainability of classification tasks
Rizzo, L. & Longo, L., 2023, Springer.Research output: Other contribution › peer-review
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ArgFrame: A multi-layer, web, argument-based framework for quantitative reasoning
Rizzo, L., Sep 2023, In: Software Impacts. 17, 100547.Research output: Contribution to journal › Article › peer-review
Open Access -
Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts
Rizzo, L. & Longo, L., Jan 2023, In: Information Fusion. 89, p. 537-566 30 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning
Longo, L., Rizzo, L. & Dondio, P., 9 Jan 2021, In: Knowledge-Based Systems. 211, 20 p., 106514.Research output: Contribution to journal › Article › peer-review
Open Access
Datasets
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Subjective Mental Workload (Nasa-TLX) of third-level classes delivered at Technological University Dublin
Rizzo, L. (Contributor) & Longo, L. (Contributor), Technological University Dublin, 1 Jan 2018
DOI: 10.13140/RG.2.2.32607.10403
Dataset