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 two postdoctoral researchers and three Ph.D. students.

Francesco Vinci

Postdoctoral Researcher

Francesco Vinci is a Postdoctoral Researcher in the Department of Mathematics at the University of Padova, where he did his PhD in the Brain, Mind & Computer Science (BMCS) program. His research focuses on Business Process Simulation: discovering accurate and interpretable simulation models from event data, keeping them aligned with processes that evolve over time, and using them for “what-if” analyses that assess the impact of changes without disrupting real-world operations. His work also covers process-model repair driven by simulation, and applications to public administration and healthcare.

Email: francesco.vinci@unipd.it
Web: Google Scholar profile

Alessandro Padella

Postdoctoral Researcher

Alessandro is a Postdoctoral Researcher at the University of Padova, where he did his PhD in the Brain, Mind & Computer Science (BMCS) program. His research focuses on Responsible Predictive and Prescriptive Process Analytics, with emphasis on Fairness and Explanation: predicting how running process instances will unfold, recommending the actions that improve their outcome, and ensuring that these predictions and recommendations are fair towards the people involved and understandable to process stakeholders.

Email: alessandro.padella@unipd.it
Web: https://pado123.github.io/

Faizan Ahmed Khan

Ph.D. Student

Faizan is currently pursuing a Ph.D. and focuses his research on Robotic Process Automation (RPA), a groundbreaking approach revolutionizing business operations. Through the deployment of software robots, RPA 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 RPA. The project aims to unravel the multifaceted benefits of RPA, including operational efficiency, cost reduction, enhanced accuracy, scalability, and improved compliance. Faizan's research is poised to explore the transformative impact of RPA on business processes, aiming to uncover applications, challenges, and future advancements in this dynamic and evolving field.

Email: faizanahmed.khan@studenti.unipd.it

Ngoc Diem Le

Ph.D. Student

Diem'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

Pavel Savinov

Ph.D. Student

Pavel's Ph.D. project focuses on Object-Centric Process Mining (OCPM), which offers a new way to analyze complex socio-technical systems in which multiple entities interact and evolve together over time (cf. https://doi.org/10.3390/math11122691). Instead of viewing a process as a single sequence of activities to reach a goal, OCPM represents processes as networks of interacting objects such as patients, clinicians, robots, devices, students, courses, business units, or computer-network nodes. This enables a richer understanding of modern processes, where outcomes are not only obtained by executing a sequence of actions but also by dependencies, coordination patterns, shared artifacts, and evolving relationships among many actors and artifacts. The project investigates how advanced Process Intelligence techniques, graph-based representations, Graph Neural Networks, and Explainable AI can be combined to understand, predict, optimize, and redesign such systems. The methods will be applied to domains of interest for the companies and institutions the group collaborates with, such as healthcare, robotics, business processes, cybersecurity, and education, where object-centric process models can reveal hidden bottlenecks, anomalous behaviours, collaboration patterns, and systemic inefficiencies. A key objective is to develop methods that are not only accurate and scalable, but also transparent and interpretable, so that their insights can support trustworthy decision-making in critical real-world contexts.

Email: pavel.savinov@phd.unipd.it