جستجوی استاد راهنمای دانشگاهی

• Cyber-Physical Systems
• Machine Learning
• Self-Adaptive Systems
• +۴ مورد دیگر

Dr. Ilias Gerostathopoulos is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute and the Department of Information Management & Software Engineering. He specializes in self-adaptive systems, cyber-physical systems, and machine learning operations (MLOps). His work focuses on software architectures for autonomous systems, decision-making under uncertainty, and experiment-driven adaptation frameworks. He teaches courses such as 'Fundamentals of Adaptive Software' and 'Information Management', emphasizing practical applications of adaptive systems and data-driven decision-making. Gerostathopoulos has been awarded the Best Presentation Award (2021) for contributions to evaluating self-adaptive systems. His research addresses challenges in industrial self-adaptation, MLOps architectures, and robotics. Key research themes include: Architecture-based self-adaptation in robotics and CPS MLOps frameworks and systematic analysis of AI systems Uncertainty management in autonomous systems Experiment-driven learning and tool development Notable contributions include the ExpEngine tool for workflow optimization and the ReBeT framework for robotic systems. His work bridges theoretical software engineering with practical industrial implementations.

Petra Heckمشاهده پروفایل

مدرس ارشد

• AI Engineering
• Machine Learning
• MLOps (Machine Learning Operations)
• +۳ مورد دیگر

Dr. Petra Heck is a Senior Lecturer at the Faculty of ICT , Fontys University of Applied Sciences, specializing in AI Engineering and Machine Learning. She obtained her MSc in Computer Science from TU Eindhoven (2011) and a PhD in Software Engineering from TU Delft (2016) focusing on 'Quality of Just-in-Time Requirements'. Academic Roles : Lecturer in Software Engineering (2012-2016), Senior Researcher in AI & Big Data lectorate (2016-present) Research Focus : Building production-ready machine learning systems, with a postdoctoral focus (2019-2021) on practical ML engineering challenges. Her work emphasizes: Transdisciplinary collaboration involving non-ICT specialists Knowledge transfer to industry and education Shaping the emerging field of AI Engineering as a formal discipline Her publications highlight: Systematic approaches to ML operations (MLOps) Testing and debugging ML applications Evolving quality models for AI systems Defining AI engineer competencies through job market analysis She supervises students in practice-oriented projects, such as Real-time communication app (2014), and contributes to educational programs for AI engineers.

Sijia Maمشاهده پروفایل

استادیار

• Sharing Economy
• E-commerce
• Social Media
• +۳ مورد دیگر

Sijia Ma is an Assistant Professor of Information Management in the Department of Management at Tilburg School of Economics and Management (TiSEM), Tilburg University. She holds a Ph.D. in Information Systems from the Guanghua School of Management, Peking University. Her research focuses on the economics of platform businesses, including sharing economy dynamics, omnichannel retail strategies, and social media impact analysis. She has published in journals such as Electronic Commerce Research and Applications and presented at conferences like ICIS and PACIS. Her research explores topics like the societal and industrial impacts of platforms, including how digital platforms influence urban development and consumer behavior. She also investigates the lifecycle of machine learning-based solutions and their alignment with business practices. Her work bridges theoretical insights with practical implications for digital business models. Dr. Ma teaches courses on data science research methods, business AI trends, and digital platforms. She actively contributes to the academic community through her research on platform governance, social media communication dynamics, and the ethical dimensions of emerging technologies. Her teaching portfolio includes modules such as Data Science Research Methods Business AI & Emerging Trends Digital Platforms Information Management MSc Thesis Guidance