Dr. Sam Leewis

HU University of Applied Sciences Utrecht, Netherlands

Dr. Sam Leewis is a researcher and lecturer at Utrecht University of Applied Sciences, where he works within the Digital Ethics research group. His research focuses on decision mining, responsible digital innovation, digital twins, and the use of data and AI in public-sector organizations. He is particularly interested in how digital technologies can improve operational decision-making while safeguarding public values such as transparency, accountability, and equality. His work combines applied research, education, and collaboration with governmental organizations and industry partners.

Title: Can we use generative AI to uncover the business logic hidden inside complex software systems?

Abstract: This talk explores how Large Language Models (LLM) can help reverse-engineer source code into understandable and standardized decision models. Legacy systems often contain decades of accumulated business knowledge, while documentation is incomplete, outdated, or simply missing. Replacing these systems can therefore mean losing essential organizational knowledge. Our research investigates whether LLMs can bridge this gap by automatically identifying decision logic in source code and translating it into Decision Model and Notation (DMN). Based on the development and evaluation of this prototype, the talk proposes an end-to-end approach in which LLMs identify relevant decisions, reconstruct decision dependencies and rules, and generate visual DMN models. Beyond presenting what works, the talk also discusses where LLMs still struggle and what this means for using generative AI to understand, maintain, and ultimately modernize legacy software.

Dr. Matthijs Berkhout

HU University of Applied Sciences Utrecht, Netherlands

Matthijs Berkhout is a researcher and lecturer at HU University of Applied Sciences, Utrecht, affiliated with the research group Digital Ethics. His work focuses on the implementation and evaluation of data-driven technologies that support decision-making in professional practice, particularly in healthcare and public sector organizations.

He completed the Master of Business Informatics at Utrecht University in 2020, where he conducted research on decision mining algorithms.

Within his research, Matthijs studies how artificial intelligence and decision analytics can be implemented responsibly in real-world settings, with specific attention to adoption, organizational learning, and clinical decision support. His work bridges technical innovation with implementation science to ensure that data-driven tools are usable and valuable for healthcare professionals.

In addition to his research, Matthijs has been a lecturer at Hogeschool Utrecht since 2017, where he teaches courses on digital innovation, decision management, and data-driven technologies.

Assoc. Prof. Anibal Tavares de Azevedo

State University of Campinas (UNICAMP), Brazil

Anibal Tavares de Azevedo is a highly accomplished Associate Professor ("Livre-Docente") at the University of Campinas (UNICAMP). His research lies at the intersection of Operations Research, Combinatorial Optimization, System Simulation, and Artificial Intelligence applied to large-scale logistics, energy, and healthcare systems.

He is the founder, chief architect, and coordinator of the BISS (Big Interconnected Smart Science) Project and the R3PO (Rede Paulista de Pesquisa Operacional) Network. Through these pioneering initiatives, Prof. Azevedo has established a groundbreaking framework for decentralized, citizen-driven distributed science, mobilizing hundreds of researchers to solve high-impact, real-world public logistics challenges.

Title: The Green Compute Synergy: Sovereign AI, Volunteer Networks, and the Brazil-Japan Energy-Technology Win-Win

Abstract: The Global AI Infrastructure Crisis: The presentation opens by addressing the severe physical and electrical bottlenecks facing centralized, hyperscale data centers (such as multi-year grid interconnection queues and community noise complaints).

The NVIDIA XFRA Residential Edge Solution: We introduce Span's UL 3141-certified smart electrical panels and XFRA's liquid-cooled edge compute nodes (packing 16 NVIDIA RTX Pro 6000 Blackwell GPUs), demonstrating how they leverage existing residential electrical headroom to run high-performance AI inference close to users.

Brazil's Renewable Energy Paradox (The "Duck Curve"): We examine the contemporary energy challenge in Brazil, where a massive 50GW surplus of distributed solar generation causes overproduction emergencies and forced ONS grid curtailments at noon, while fossil-fuel plants are still activated during night-time spikes.

The Brazil-Japan Strategic Alliance: We propose a global win-win. Brazil provides abundant, clean, and low-cost solar surplus to power edge XFRA nodes during peak solar hours, solving its grid over-generation crisis. Japan and NVIDIA provide the advanced hardware and open-source software—specifically NVIDIA Nemotron Open Models—enabling highly secure, localized, and sovereign AI applications.

The BISS Methodology and the R3PO Volunteer Network: We explain our operational framework. The BISS (Big Interconnected Smart Science) platform fragments complex logistics simulations (like emergency ambulance location in Campinas and São Paulo) into reproducible, cryptographic "Work Units" via unique Combination IDs and MD5 hashing, validated by a factor-2 Biss Checker redundancy check. These tasks are solved by the R3PO student volunteer network and tracked on a gamified, real-time Status Map.

Cognitive Cities and the DeSci Economy: We conclude with the "Sense-Think-Act" loop of edge cities where localized Nemotron models safely optimize urban systems, tokenizing validated resource efficiency into IP-NFTs to fund public university research.

Dr. Pythagoras N. Petratos

Westminster University, UK

Pythagoras N. Petratos joined Westminster University in the summer of 2025 as Lecturer in Finance. Before that he was at Coventry University Lecture in Finance mainly teaching and researching in FinTech.

Previously he was doing research at the Blavatnik School of Government, University of Oxford and he was a Departmental Lecturer in Finance at Saïd Business School, University of Oxford. He has also taught at numerous Universities, in the UK and abroad, including ESCP and the University of London.

He earned his Ph.D. at the University of London and he has obtained master’s degrees in, Software Engineering (University of Oxford), Economics (City, University of London), European Politics (University of London), and Finance (Bayes Business School, City, University of London).

Dr Petratos is a member of many professional organisations and has presented at various conferences, universities (Oxford, Cambridge, Harvard/MIT, U.S. Naval War College, etc.) and cooperated with international organisations (i.e. OECD, World Bank, WHO), including an internship at the United Nations (ITU). He has been invited to NATO in senior delegations of academics and think tankers. His Ph.D. was in corporate finance, entrepreneurial finance and valuation of new technologies.

Dr. Alessio Faccia

University of Birmingham Dubai, UAE

Dr Alessio Faccia is Assistant Professor in Finance at the University of Birmingham Dubai, a Chartered Accountant and Registered Auditor. His academic and professional work covers financial analytics, artificial intelligence in finance, digital finance, risk management, business valuation and data-driven decision-making. He has authored several books and research papers in accounting, finance, financial modelling and emerging technologies. His research has received more than 3,900 Google Scholar citations, with a Scopus h-index of 18. Dr Faccia also delivers executive and professional training programmes for universities, financial institutions, public-sector organisations and international bodies.

Title: From Big Data to Better Business Decisions: AI, Analytics and the New Architecture of Financial Intelligence

Abstract: Organisations now generate and process volumes of financial, operational and behavioural data far beyond the capacity of conventional reporting systems. Artificial intelligence, machine learning and advanced analytics are changing how organisations interpret such data, assess risk, forecast performance and support managerial decisions.

The speech examines how big data analytics is moving from descriptive reporting towards predictive and prescriptive decision support, with particular attention to finance, accounting, risk management and corporate strategy. Practical applications include financial forecasting, fraud detection, credit assessment, valuation, customer analytics, anomaly detection and automated reporting. Attention also focuses on the role of generative AI and AI agents in converting large and fragmented datasets into usable decision information.

Technical capability alone does not produce better decisions. Data quality, model reliability, governance, explainability, human oversight and regulatory accountability remain central issues when analytical outputs influence financial or strategic choices. The discussion therefore considers opportunities created by data-driven systems alongside managerial risks linked to weak data controls, opaque models and excessive reliance on automated recommendations.

The speech proposes a practical framework for moving from raw data to decision-ready intelligence, linking data architecture, analytical methods, AI systems, governance and managerial judgement. The aim is to show how organisations build analytically informed decision processes while retaining accountability, transparency and professional judgement.

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