Find Academic Supervisors

• Model-Driven Engineering
• Robotics Software Engineering
• Software Architecture
• +3 more

Federico Ciccozzi is an Associate Professor in Computer Science at Mälardalen University's School of Innovation, Design and Engineering, where he leads the ASSO research group and the VR ORPHEUS project. He also serves as Head of Research Education in Computer Science and Electronics at the university. His academic journey includes a M.Sc. in Global Software Engineering (via the GSEEM program) and a Ph.D. in Computer Science and Engineering from Mälardalen University (2014), followed by promotion to Docent (Associate Professor) in 2017. Education: M.Sc. in Global Software Engineering (GSEEM program, joint between Mälardalen, L'Aquila, and Amsterdam) Ph.D. in Computer Science and Engineering (Mälardalen University, 2014) Docent (Associate Professor) in Computer Science (Mälardalen University, 2017) His research focuses on model-driven engineering, robotics software engineering, and software architecture. He has pioneered work in blended modeling approaches, EAST-ADL extensions, and formal verification of complex systems, particularly in robotics and automotive domains. Recent projects emphasize industrial collaborations, such as optimizing ROS2 multi-robot systems and enhancing safety-critical software through model-based methodologies. Research Contributions: Developed frameworks for transforming surface languages into augmented EAST-ADL models Advanced blended modeling techniques across JetBrains MPS and Eclipse tools Examined consistency management in industrial model-driven development Edited special issues on model-driven engineering and low-code development His work bridges academic innovation and industrial practice, with notable projects like the Rubus Component Model for vehicular systems. He actively organizes workshops (e.g., RoSE, ASYDE) and contributes to standardization efforts like the Portable Test and Stimulus Standard. Labs/Teams: Leads the ASSO research group, focusing on advanced software engineering methodologies and robotics systems development.

• Space Robotics
• Control Theory
• Computer Vision

Pedro Delgado Roque is a Postdoctoral Researcher at the Royal Institute of Technology (KTH) in Stockholm, affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP). He is part of the Division of Decision and Control Systems (DCS) and leads the establishment of the Space Robotics Laboratory at KTH. His research focuses on Space Robotics, Control Theory, and Computer Vision, with contributions to projects like DISCOWER, Space Cobot, and MoonHopper. He obtained his Ph.D. in 2024 under Professors Dimos Dimarogonas, Mikael Johansson, and Jana Tumova. **Research Interests**: Space Robotics: Weightless robotics, collaborative robotics, exploration robotics. Control Theory: Model predictive control, collaborative load transportation. Computer Vision: Visual-servoing, distributed vision systems. **Awards**: ICRA 2022 Outstanding Coordination Award for work on decentralized collaborative UAV control. **Advising**: Supervised 11+ Master's and thesis students, including works on failsafe control for space systems, predictive controllers in microgravity, and multi-agent inspection systems. Collaborated with NASA Astrobee and PX4 projects. **Labs & Projects**: Co-founded the DISCOWER project with 4 PhD students, 2 master students, 6 professors, and a postdoc team. Developed the Space CoBot aerial robot for indoor microgravity testing.

• Cybersecurity
• Formal Verification
• Cyber-Physical Systems
• +7 more

Marjan Sirjani is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering, and the Division of Computer Science and Software Engineering. Her research focuses on cybersecurity, formal verification, and cyber-physical systems, with notable contributions to actor-based modeling (e.g., Timed Rebeca) and tools like AFRA for model analysis. She specializes in integrating formal methods into safety-critical systems, including automotive cybersecurity, ROS2 robotics, and blockchain-based IoT systems. Her work emphasizes rigorous analysis of protocols, concurrency, and real-time constraints. Recent projects include the CRYSTAL framework for CPS assurance, Tiny Twins for runtime attack detection, and applying LLMs for automated test generation. She also explores semantic segmentation in construction and compositional analysis of distributed systems. Publications highlight advancements in protocol learning, controller synthesis for safety, and model-driven development. Her research bridges theoretical foundations (e.g., automata theory, temporal logics) with practical applications in autonomous systems, medical device interoperability, and smart mobility. Labs/Teams: Involved in the Rebeca tool development (AFRA) and collaborative projects on CPS security. Grants: Not explicitly listed but implied through project involvement.

• Control Systems
• Robotics
• Embedded Systems
• +3 more

Alessandro Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the Institute for Software Technology and Software Engineering (ITIS), University of Málaga. He leads the Complex Real-Time Embedded Systems (CORE) research group and serves as Scientific Leader for Applied AI under the AI@MDU initiative. His research focuses on control theory, robotics, and real-time embedded systems, emphasizing interconnected systems and uncertainty management. Education: BSc (2008) and MSc (2010) in Computer Engineering from Politecnico di Milano; PhD (2014) in Information Technology, Systems and Control from the same institution. Postdoctoral fellowships at Lund University’s Department of Automatic Control and Politecnico di Milano’s Dipartimento di Elettronica, Informazione e Bioingegneria. Research interests include control theory applications to computing systems, distributed systems, and AI integration. His work addresses challenges in real-time, embedded, and edge computing. Notable contributions include VR starting grant (2020) and SSF strategic mobility grant (2020), and the Most Influential Paper Award at SEAMS 2025. He has advised PhD students like Anna Friebe and Daniel Bujosa Mateu. Grants and collaborations include projects with ABB industrial automation and leadership roles in conferences (e.g., DEBS 2025 Workshop/Tutorial Chairs). His research group explores autonomous systems, cybersecurity in industrial networks, and cloud manufacturing resilience. Labs/teams: CORE group, collaborating with Prof. Thomas Nolte.

• Control Systems
• Networked Systems
• Distributed Control
• +3 more

Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control , Lund University , with a Wallenberg AI, Autonomous Systems and Software Program (WASP) professorship. She holds multiple roles including Deputy Head of Department, Project Manager, and Profile Area Member in AI & Digitalization and Natural & Artificial Cognition. Her research focuses on analysis and control of large-scale networked systems , with emphasis on distributed control, power networks, and social/epidemiological networks. Tegling earned her PhD in Electrical Engineering from KTH Royal Institute of Technology (2019) and held postdoctoral positions at MIT IDSS. She has led projects on network controllability, optimization, and societal applications. Notable contributions include scalable control design for vehicular formations and optimal control of linear cost networks. Her work aligns with UN Sustainable Development Goals related to affordable clean energy and industry innovation. Education: PhD in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. and B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013, 2011) Research Trends: Recent articles emphasize minimax optimal control, compositional design for nonlinear systems, and multipolar opinion dynamics in biased networks. Themes include scalability limits, distributed algorithms, and transients in networked systems. Grants & Projects: Leads WASP-funded projects on learning in networks and dynamic socio-technical systems. Involved in initiatives like the European Control Conference 2024. Supervises PhD students in control systems and network dynamics. Labs & Teams: Active in the Department of Automatic Control research groups focused on network science and AI-driven control solutions, contributing to Lund University's AI and Digitalization profile.

Johan PerssonView profile

Associate Professor

• Computer Aided Product Development
• Surrogate Models
• Finite Element Analysis (FEM)
• +4 more

Johan Persson is an Associate Professor and Head of Unit at Linköping University's Department of Management and Engineering (IEI). He leads the Product Realisation (PROD) division, focusing on advancing Computer Aided Product Development (CAD) and optimization techniques. His research emphasizes computationally efficient surrogate models to enhance FEM/CFD analyses and streamline product development processes. Key areas of interest include design automation frameworks, additive manufacturing integration, and multidisciplinary optimization strategies. He teaches courses such as Machine Elements (TMKT39), Design Optimization (TMKT48), and Collaborative Multidisciplinary Optimization (TMKT79). His work bridges academic research and industry applications, with notable contributions to frameworks supporting Additive Manufacturing (AM) and OpenMDAO-based optimization. Research trends in his publications highlight advancements in AM applications, design automation tools, and collaborative optimization methods. Notable projects include a framework for AM design integration and studies on hydraulic pump design using additive techniques. Johan supervises PhD students including Erik Gustafsson and Javier Villena Toro. His research is supported by collaborations with colleagues like Anton Wiberg and Johan Ölvander. The Automation Lab, a key platform for his work, pioneers innovations in CAD automation and digital design processes.

Silun ZhangView profile

Assistant Professor

• Nonlinear Control
• Large-Scale Complex Systems
• Privacy-Preserving Algorithms
• +3 more

Silun Zhang is an Assistant Professor in the Department of Mathematics at KTH Royal Institute of Technology, affiliated with WASP (Wallenberg AI, Autonomous Systems and Software Program) and the Digital Futures Faculty. He holds a Ph.D. in Optimization & Systems Theory from KTH (2019), an M.S. and B.S. in Automation from Harbin Institute of Technology, China. Prior to KTH, he was a Postdoctoral Associate at MIT's Laboratory for Information & Decision Systems (LIDS) and EECS Department (2019–2023), where he collaborated with Prof. Munther Dahleh and contributed to the MIT MicroMasters Program in Statistics and Data Science. His research focuses on nonlinear control , large-scale complex systems , privacy-preserving algorithms , and risk-averse reinforcement learning . Key areas include control on manifolds (e.g., rigid-body attitude coordination), privacy in federated learning, and moment-based modeling for collective systems. He leads projects such as 'Analysis and Synergy of Hyper-networked Autonomy' and 'Designing Interaction-Aware Multi-Robot Systems.' Current team members include Georgios Vasileiou (Ph.D.), Lantian Zhang (Postdoc), and Malintha Fernando Chakravarthige (Postdoc). His work is supported by the Wallenberg Foundations and Digital Futures. Open positions are available for motivated Ph.D. students and postdocs.

• Automation
• Discrete Event Systems
• Supervisory Control
• +3 more

Martin Fabian is a Professor at the Department of Electrical Engineering , Chalmers University . As Head of the Automation research group , he leads research initiatives in automation and discrete event systems. His research focuses on Supervisory control theory Formal verification Cyber-physical systems Robotics Manufacturing automation Recent publications (2024) highlight advancements in timed automata synthesis, smart contract verification, and disturbance-handling controller design. Earlier works (2023-2022) explore compositional algorithms, automated driving safety, and vehicle routing optimization.

• Robotics
• Control Systems
• Kinematics
• +5 more

Shivesh Kumar is an Assistant Professor in the Dynamics Division of the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. He also holds a part-time Senior Researcher position at the Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany. Assistant Professor at Chalmers Part-Time Senior Researcher at DFKI Co-Chair, IEEE-RAS Technical Committee on Model-Based Optimization for Robotics His research focuses on robotics , particularly in kinematics, dynamics, and control systems for developing animal-like physical/athletic intelligence in robots. Key areas include underactuated robotics, control algorithm benchmarking, and human-robot interaction. Recent publications (2023-2024) highlight work on: Parkour control for monoped hoppers Reinforcement learning for robotic athleticism Energy-efficient brachiation robots Robust co-design of underactuated systems His projects involve collaborations with Wallenberg AI, Autonomous Systems and Software Program and Chalmers' digitalization initiatives. He maintains advisory relationships with the Advanced AI Team: Team Mechanics & Control and Underactuated Robotics Lab at DFKI. Contact: shivesh.kumar@chalmers.se

Mikael FridenfalkView profile

Senior Lecturer

• Artificial Intelligence
• Computer Graphics
• Game Design
• +4 more

Mikael Fridenfalk is a Senior lecturer at the Department of Game Design, Uppsala University, Campus Gotland. He received his PhD in robotics from Lund University in 2003 and completed a postdoctoral research associate position at MIT (2003-2004). Since then, he has been affiliated with Uppsala University Campus Gotland (formerly Gotland University), initially as a lecturer and from 2005 as an associate professor and researcher within computer engineering and AI. With over 40 years of experience in computer programming, primarily using C++, Fridenfalk specializes in artificial intelligence and computer graphics applications. His research spans multiple domains including game design, digital twins, cellular automata, and neural networks. His work demonstrates a consistent focus on the intersection of mathematical principles and practical applications in computer science. Fridenfalk's recent publications (2022-2025) show a shift toward applied research in digital twins for industrial metaverse applications, cloud-edge-terminal collaboration, and game environment design using modular architecture. This reflects an evolution from his earlier theoretical work to more industry-relevant applications while maintaining strong mathematical foundations. Scientific Contributions: Extensive research on cellular automata and modular spaces across numerous publications Development of analytical methods for neural network weight evaluation Innovative applications of L-systems in game environment and music generation Pioneering work on digital twin technology for industrial and marine applications Contributions to photogrammetry techniques for game environments Methods for examination problem distribution in e-learning environments Fridenfalk's work bridges theoretical computer science with practical applications across multiple domains, demonstrating remarkable versatility while maintaining mathematical rigor. His research has been cited in various contexts, including academic papers and Wikipedia references, indicating impact across disciplines.

• Real-Time Computer Systems
• Smart Cities and Communities
• Vehicular Communication
• +7 more

Magnus Jonsson is a Professor of Real-Time Computer Systems at Halmstad University's School of Information Technology. He serves as Deputy Program Director for the university's Smart Cities and Communities profile area. His research focuses on real-time communications, vehicular networks, cooperative systems, and resilient communication architectures. Key research themes include: Autonomous vehicle communication systems Real-time protocols for embedded networks V2X (vehicle-to-everything) infrastructure Wireless network reliability Fiber-optic communication systems Publications emphasize practical applications like: Securing autonomous vehicle systems Platooning applications Low-latency communication frameworks He has edited multiple conference proceedings including Communication Technologies for Vehicles and Resilient Networks Design and Modeling . His work contributes to both theoretical advancements and industry-ready solutions for smart mobility and critical infrastructure systems.

Olov EngwallView profile

Professor

• Social Robotics
• Human-Robot Interaction
• Educational Technology
• +6 more

Olov Engwall is a Professor in Speech Communication at KTH Royal Institute of Technology, affiliated with the Division of Speech, Music, and Hearing within the Department of Intelligent Systems. He serves as Head of Studies for the Department and Track Director for the Computer Science specialization in the MSc program in Industrial Management and Engineering. His research focuses on social robotics, human-robot interaction, and robot-assisted language learning. Engwall holds a PhD in Speech Communication from KTH (2002) and an MSc in Engineering Physics (1998). He has conducted research visits at INPG Grenoble (1999) and LORIA Nancy (2006). His roles include organizing international conferences like SLaTE 2017 and serving on technical committees for IEEE Speech and Language Technical. Key research areas include culturally-aware robots (CIRILA project), NLP-based math tutoring, and robot-led second language conversation practice. He has pioneered projects like the ARTUR articulation tutor system and the ROBERT initiative for dyslexia support. His work bridges speech technology, education, and human-robot interaction with over 80 peer-reviewed publications. Engwall supervises doctoral students in areas like robot-mediated learning and serves as examiner for multiple MSc engineering projects. He is a guest editor for Frontiers in Robotics and AI, focusing on culturally-aware robotics, and has contributed to journals like Computer, Speech and Language.

Mobyen Uddin AhmedView profile

Professor

• Artificial Intelligence
• Computer Science
• Embedded Sensor Systems
• +5 more

Mobyen Uddin Ahmed is a Professor in Artificial Intelligence and Computer Science at Mälardalen University (MDU), affiliated with the School of Innovation, Design and Engineering’s Division of Computer Science and Software Engineering. He leads the Artificial Intelligence and Intelligent Systems group and contributes to the ESS-H research profile (Embedded Sensor Systems for Health). Education: M.Sc. in Computer Engineering (Dalarna University, Sweden), PhD in Computer Science (Mälardalen University, 2011), and postdoctoral research at Örebro University’s Center for Applied Autonomous Sensor Systems (2012–2014). Research focuses on AI-driven healthcare solutions, IoT technologies, sensor systems, and machine learning applications. He leads major projects like H2020 ‘SimuSafe’, ‘BrainSafeDrive’, and national initiatives such as InVIP. He co-organized international conferences like HealthyIoT (2016–2017), ICCBR2018, and pHealth2015. Teaching responsibilities include courses like Applied Artificial Intelligence, Machine Learning with Big Data, and Deep Learning for Industrial Imaging. He develops distance education programs for industrial professionals via projects like Into DeeP. Labs/Teams: Principal Investigator of ESS-H, involved in research groups focused on predictive analytics and intelligent embedded systems.

• Smooth Transition Models
• State-Space Models
• Macroeconometrics
• +8 more

Yukai Yang is a Senior Lecturer at the Department of Statistics, School of Economics and Business, Uppsala University. His research bridges econometric theory with applied statistical methods. Education : B.Eng, Shanghai Jiao Tong University Cand. Polit, University of Copenhagen PhD, Aarhus University (2012) His research spans smooth transition models , state-space models , Bayesian VAR , panel data analysis , and directional statistics , with recent interdisciplinary work in medical data science . Articles highlight methodological innovation in time series , machine learning , and mobile biometrics . Scientific Awards : None listed in provided data. His work includes software development (e.g., mfbvar package) and collaborations in multimedia systems and health economics .

Giulia Wally ScuratiView profile

Research Fellow

• Mechanical Engineering
• Product Design
• Sustainable Behavior
• +4 more

Giulia Wally Scurati is a Postdoctoral Fellow at the Department of Mechanical Engineering, Blekinge Institute of Technology, specializing in using virtual reality (VR) , augmented reality (AR) , and serious games for sustainable behavior change and product development. Her research spans mechanical engineering , human-computer interaction , and environmental psychology . She has a PhD in Mechanical Engineering from Politecnico di Milano, with a thesis on design methods for VR/AR in sustainable behavior. Current projects include AXESS (Assessment in XR Environments for Sustainable Solutions). Key Research Areas: Sustainable behavior, VR/AR applications, product-service systems (PSS), multisensory feedback, gamification, and human-robot interaction. Collaborations: Works with institutions like Politecnico di Milano, Politecnico di Torino, and University of Twente.