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Prof. Ing. Massimiliano de Leoni |
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The Process Science Research Group
Prof. de Leoni leads a research group that focuses the activities in the fields of Business Process Management, Process Analytics and Mining. The main driver of the group's research is the presence of process' transactional data, which records how processes are actually executed within organizations. The presence and analysis of these data allows gaining insights that are based on data-driven evidence, putting aside any subjectivity of experts, process' stakeholders and actors. These objective insights enables the improvement of the real processes and not of the supposed ones: this is the fundamental difference with traditional business process intelligence (cf. Six Sigma) where, conversely, any consideration is based on the subjectivity of the experts and stakeholders, which can naturally be biased and partial.
The group's research work revolves around AI, formal methods and Data Science, and their application for Business Process Management and Analytics. The group puts a strong emphasis to ensure the practical applicability of the research carried on: a lot of attention is paid on the application's domains, which range from traditional business domains (e.g. the financial or service sectors), to healthcare, IoT and education.
The research group is currently complemented by four Ph.D. students.
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Francesco VinciFrancesco Vinci is currently a PhD student in the Department of Mathematics at the University of Padova, specializing in Computer Science for Societal Challenges and Innovation. He is involved in efforts to optimize public administration processes. The project focuses on integrating data and feedback from stakeholders, departing from traditional methods that rely solely on subjective feedback. Francesco Vinci employs user-evaluation techniques, Machine Learning, AI, and Process Mining for effective optimization. The project aims to interpret normative frameworks and provide insights through case studies. Additionally, it explores the use of business process simulation for "what if" experiments, assessing the impact of changes without disrupting real-world systems. The ultimate goal is to propose methodologies that leverage AI to enhance the quality of business process simulation models, contributing to advancements in the research field. Email:
francesco.vinci.1@studenti.unipd.it |
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Alessandro PadellaAlessandro is currently a PhD student at the University of Padova, specializing in Computer Science for Societal Challenges and Innovation. Alessandro's project focuses on Process Mining to extract knowledge about business processes within organizations. The aim is to improve processes through a Process-aware Recommender System and model repairs at the design stage. The project integrates Process-Mining with Machine- and Deep-Learning techniques to develop software tools with advanced Graphical User Interfaces for process improvement suggestions. Techniques from human-computer interaction are also utilized.
Email: alessandro.padella@phd.unipd.it |
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Faizan Ahmed KhanFaizan is currently pursuing a Ph.D. and focuses his research on Process Robotic Automation (PRA), a groundbreaking approach revolutionizing business operations. Through the deployment of software robots, PRA automates rule-based tasks, optimizing efficiency, and significantly reducing operational costs. Methodologically, Faizan's work involves meticulous process analysis, design, rigorous testing, and strategic deployment of PRA. The project aims to unravel the multifaceted benefits of PRA, including operational efficiency, cost reduction, enhanced accuracy, scalability, and improved compliance. Faizan's research is poised to explore the transformative impact of PRA on business processes, aiming to uncover applications, challenges, and future advancements in this dynamic and evolving field. |
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Ngoc Diem LeDiem's research aims to enhance the efficiency of public administrations by leveraging Explainable AI techniques for the analysis of transactional data within their information systems. Moreover, the research aims to facilitate the digital transition while considering sociological aspects. It proposes solutions for a global acceptance of changes, incorporating Explainable AI techniques. Ethical considerations are paramount, ensuring fairness and responsibility in choices, avoiding discrimination based on gender, ethnicity, or social class. Email: ngocdiem.le@phd.unipd.it |