2026 International Seminar Series on Emerging Technologies in Cyber-Physical Systems (CPS)

Presentation

Presentation

Presentation

Presentation

Presentation

Biography: Prof. Dr. Imre Felde (Engineer of Information Technology) completed his PhD in 2007. He has been working for Bay Zoltán Foundation for Applied Research (BZAKA, Budapest, Hungary) as research fellow between 1995 and 2009. He was General Director of BZAKA between 2010 and 2012. He has been working as Vice Dean (2012-2018) at Obuda Univesity and as Vice rector (2019-2024). He was appointed Full Professor at the John von Neumann Faculty of informatics in 2022. He published more than 240 scientific and technical papers in field of engineering, computer science, mathemathics and process modeling.

Title: Analysis of mobility customs in urban area by processing mobile network data

Abstract: Mobile phones and even more, smart-phones, are now fundamental parts of our life, they are practically, always with us, wherever we go, almost as if they were a part of our body. The continuous communication between a device and the Mobile Phone Networks leaves traces at the Operator’s system of our mobility habits. Via these devices, the Mobile Phone Network can ‘sense’ our movements, which is the basis of the “Smart City” concept. In the last two decades show the potential of the human mobility characterization using Call Detail Records (CDR) in epidemiology, sociology and urban planning. The analysis of the human movement patterns on the basis of the CDR data, that makes it possible to examine a very large population cost-effectively resulted a number of discoveries about human dynamics.  In addition to, by mixing mobility indicators with external data, the hidden relationship between the daily customs and Socio-Economic Status (SES) of the dwellers could be evaulated.

Biography: Tamás P. Haidegger holds MSc degrees in Electrical and Biomedical Engineering and a PhD in Medical Robotics from Budapest University of Technology and Economics (BME), and received his habilitation from Óbuda University in 2022. His research focuses on medical technologies, surgical robotics, teleoperation, image-guided therapy, and digital health. He is a professor at Óbuda University, technical lead of medical robotics research at the Antal Bejczy Center for Intelligent Robotics, and director of Initium Venture Labs. He also holds an Adjunct Associate Professor appointment at Queen’s University and is a research area manager at the Austrian Center of Medical Innovation and Technology (ACMIT). He co-founded HandInScan, a university spin-off dedicated to improving hand hygiene in healthcare.An active leader in IEEE and international standardization, he contributes to the development of standards for medical robotics, patient safety, and innovation management. He serves as Co-Editor-in-Chief of Acta Polytechnica Hungarica and holds editorial positions at several leading journals. The author and co-author of over 350 scientific publications, with more than 5,000 independent citations, he is included in Stanford’s Top 2% Scientists list and has received numerous awards, including the Dennis Gabor and Anyos Jedlik Awards. He has also maintained a professional blog on medical robotics for over 15 years at surgrob.blogspot.com.

Title: Intelligent Medical Robots: From Computer-Assisted Interventions to Autonomous Healthcare Systems

Abstract: Medical robotics has evolved from simple telemanipulation platforms to increasingly intelligent systems capable of perception, decision support, and autonomous task execution. This seminar reviews the current state of intelligent medical robots and their role in modern healthcare, with a particular focus on computer-assisted interventions, surgical robotics, rehabilitation systems, and hospital automation. The presentation discusses how advances in artificial intelligence, machine learning, computer vision, and sensor fusion enable robotic systems to interpret complex clinical environments and support healthcare professionals in performing precise and personalized interventions. Key challenges related to safety, reliability, human-machine collaboration, regulatory compliance, and clinical validation are examined. The seminar also highlights emerging trends, including autonomous surgical subtasks, digital twins, shared-control architectures, and data-driven robotic platforms integrated into cyber-physical healthcare ecosystems. Through representative case studies and recent research developments, participants gain insight into how intelligent medical robots are reshaping clinical workflows and expanding the capabilities of healthcare systems.

Biography: Alessandro Borri is a Senior Researcher at the Institute for Systems Analysis and Computer Science “A. Ruberti” of the National Research Council of Italy (CNR-IASI). He received his Ph.D. in Electrical and Information Engineering from the University of L’Aquila in 2011, where he also teaches Systems Biology. His research focuses on hybrid and nonlinear control, systems biology, and mathematical modelling for biomedical applications. He has spent research periods at the University of California, Berkeley and Santa Barbara, KTH Royal Institute of Technology, and Ford Motor Company. He is an IEEE Senior Member and serves on the editorial boards of IEEE Transactions on Automatic Control, IEEE Control Systems Letters, the International Journal of Robust and Nonlinear Control, and PLOS ONE. He co-received the 2018 IET Systems Biology Premium Award.

Title: Recent trends in model-based digital control for biological applications

Abstract: Biological and biomedical systems combine nonlinear dynamics, physiological delays and stringent safety requirements with the sampling and quantization constraints of digital devices. This lecture presents model-based approaches to their control, using the artificial pancreas as a running example. The focus is on three complementary frameworks: symbolic control based on finite-state abstractions, event-triggered control, and assume-guarantee contracts for interconnected systems. Symbolic methods support controller synthesis from logical and temporal specifications. Event-triggered strategies reduce unnecessary control updates while accounting for digital implementation constraints. Assume-guarantee contracts enable compositional analysis and decentralized design by relating local subsystem requirements to global behaviour. The lecture also discusses how stabilization and safety can be addressed together, including in nonlinear systems with delays. Compact glucose-insulin models and simulation studies illustrate the application of these methods to automated glucose regulation. The aim is to explain how mathematical models and formal guarantees can inform the design of resource-aware biomedical cyber-physical systems.

Short Bio: Aman Desai is an Assistant Professor of Economics and Data Science in the College of Arts and Sciences at Quinnipiac University, where he teaches principles of microeconomics and macroeconomics, econometrics, and machine learning. He earned his PhD in Economics from the Graduate Center at the City University of New York (CUNY) and was a junior scholar at the Stone Center on Socio-Economic Inequality in New York City. His research examines how labor markets, education, household resources, and social institutions shape economic inequality, mobility, and opportunity. Working at the intersection of labor economics, education economics, econometrics, and data science, he uses causal inference and machine-learning methods to study income, wealth, consumption, employment, and disparities across demographic groups.

Abstract: This lecture explores how AI agents can support and enhance different stages of the social science research process. It introduces the concept of AI agents and examines their potential applications in tasks such as literature exploration, data collection, analysis, synthesis, and research planning. Particular attention is given to how researchers can effectively collaborate with AI agents while maintaining methodological rigour, critical thinking, transparency, and research integrity. The lecture also considers current limitations and challenges, including reliability, bias, verification of AI-generated outputs, and responsible use in academic research.

Short Bio: Tamás Ferenci earned his degree with honors in Computer Engineering, specializing in Artificial Intelligence, from the Budapest University of Technology and Economics (BME) in 2009. He also holds a degree in Biomedical Engineering with honors (2010, jointly from BME and Semmelweis University), and degrees in Economics (BA with honors in 2010, MSc in 2013, both from Corvinus University of Budapest), as well as in Applied Mathematics with a specialization in Stochastics and a focus on Multivariate Statistics (MSc with honors, 2013, BME). He obtained his PhD with summa cum laude honors from biostatistics at Obuda University in 2013. He obtained his habilitation in 2019 and was appointed as a full professor in 2025. He is currently the head of the Biostatistics and Epidemiology Research Group at the Physiological Controls Research Center of Obuda University, in addition, he is a part-time full professor at the Department of Statistics at Corvinus University of Budapest. His research areas include biostatistics, pathophysiological modeling, and the critical evaluation of medical research.”

Abstract: Measuring the burden of an epidemic is riddled with intricate methodological challenges. Various burden measures — including reported cases, reported deaths, and death certificate data — all have their characteristic advantages and weaknesses. Excess mortality is a robust, alternative metric that remains relatively insensitive to testing intensity and cause-of-death classifications, though it functions as a gross indicator capturing all direct and indirect effects of a pandemic, among other problems. One of these is its sensitivity and vulnerability to forecasting baseline mortality. These questions can be investigated using simulation with synthetic data, shedding light on the optimal choice of parameters, improving our ability to quantitatively monitor ongoing events causing mortality that are hard to track with traditional tools.”