Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm
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نمایش ۱-۱۵ از ۱٬۶۱۹ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.
- Robotics
- Computer Vision
- Machine Learning
- +۳ مورد دیگر
Linda P. Franzoni serves as Professor of Practice and Associate Dean for Student Programs at Duke University, holding dual roles in academic leadership and research. Her research expertise spans: Acoustics of enclosures including rooms and vehicle interiors Underwater acoustics focused on submarine radiation and scattering Structural dynamics and vibration analysis Development of computationally efficient prediction methods for high-frequency sound fields Professor Franzoni's work targets practical Navy applications through novel homogenization techniques using local/global decomposition, significantly reducing computational demands while maintaining accuracy in acoustic modeling of complex submerged structures.
- Acoustics
- Structural Dynamics
- Vibration
- +۳ مورد دیگر
Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.
Pan Xuمشاهده پروفایل
استادیار
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.
Olle Sköldمشاهده پروفایل
مدرس ارشد
Olle Sköld is a Senior Lecturer at the Department of ABM (Archives, Libraries, and Museums) at Uppsala University. He is also the Head of the Department of ABM and holds an Associate Professor position in Library and Information Science. His research spans digital humanities, documentation practices, knowledge production, and digital preservation, with a particular focus on archaeological data processes and virtual world communities. Archaeological data documentation and reuse Digital game preservation Paradata theory and applications Information practices in social media His recent publications explore paradata in data papers, literacy challenges in research data management, and trust technologies like blockchain in digital archives. Olle is involved in the CAPTURE project, investigating documentation workflows for archaeological data. He has contributed to the Master's Programme in Digital Humanities at Uppsala University, blending digital humanities with library and information science education.
- Digital Humanities
- Archaeology
- Information Science
- +۷ مورد دیگر
Lars Erikssonمشاهده پروفایل
پژوهشگر
Lars Eriksson is a researcher at the Department of Chemistry, Stockholm University, affiliated with the Faculty of Science. He is part of Gunnar Svensson's group, which focuses on solid-state inorganic chemistry, including the synthesis of energy-related compounds and their crystal structure analysis. Research interests span inorganic chemistry, solid-state chemistry, crystallography, energy applications, organic synthesis, catalysis, and chemical education. His work bridges experimental and computational approaches, with recent publications exploring molecular design for energy storage, asymmetric synthesis, triplet-to-singlet energy transfer, and pedagogical strategies in chemistry education. Trends in his research highlight applications in materials science, environmental chemistry, and educational methodologies. While no formal scientific awards are mentioned in the provided texts, his contributions include collaborative studies on catalysis, molecular structure, and student learning processes. He has no listed grants or students, but his publications emphasize tutor-student interactions and practical epistemology analysis. The research group he belongs to investigates fundamental properties of synthesized compounds, often with energy-related applications, and maintains strong ties to the broader chemistry community through peer-reviewed publications and educational studies.
Johan Eklöf is a Professor at Stockholm University within the Department of Ecology, Environment and Plant Sciences . His research emphasizes marine ecology , focusing on interactions between marine biodiversity, environmental conditions (including climate), and societal impacts . He teaches courses such as Management of Aquatic Resources in the Tropics , Marine Ecology for the Biogeo Program , and Ecology II . Research Focus: Causal relationships between biodiversity and ecosystem resilience Foundation species as drivers of ecosystem services Optimizing resource management under climate change Projects: Principal Investigator for FORCE (2023-present), a transdisciplinary Baltic Sea recovery project Contributor to NordSalt (climate impacts on Nordic salt marshes) and PlantFish (Baltic Sea vegetation-fish interactions) Scientific Leadership : Supervises 2 current PhD students and co-supervises 6 additional PhD/postdocs Formerly mentored 4 PhD graduates and >40 MSc students Environmental Context: His work spans Baltic Sea and Western Indian Ocean ecosystems, using seagrass beds , mussel beds , and coastal benthic systems as primary models. Recent publications highlight climate change impacts on trophic cascades , habitat connectivity , and policy frameworks for marine conservation .
Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.
Sara Zahedi is a Professor of Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology, working within the Division of Numerical Analysis, Optimization and Systems Theory. She serves as an Associate Editor for the SIAM Journal on Numerical Analysis and contributes to the SCI Faculty Board to enhance collaboration and transparency in academic decision-making. Her educational background includes a doctorate from KTH on numerical methods for fluid interface problems followed by a postdoctoral position at Uppsala University. Doctorate: KTH Royal Institute of Technology Postdoctoral Position: Uppsala University Zahedi's research bridges mathematical theory and practical applications, focusing on computational methods for partial differential equations in evolving domains. She pioneers Cut Finite Element Methods (CutFEM) to eliminate re-meshing requirements in multiphase flow simulations, ensuring accuracy and robustness when interfaces separate immiscible fluids. Her work specifically targets challenges in large deformations and time-dependent geometries. Analysis of her recent publications reveals a concentrated research trajectory in advancing CutFEM for diverse applications including Stokes flow, Darcy flow, Maxwell's equations, and hyperbolic conservation laws. Key trends include high-order conservative schemes, divergence preservation, stabilization techniques for unfitted meshes, and extensions to surface PDEs and multi-physics problems. Her scientific recognition includes: European Mathematical Society Prize (2016) for outstanding contributions by young researchers Wallenberg Fellowship (2019) with extension granted in 2024 Zahedi serves as examiner for Degree Projects in Scientific Computing (SF250X, SF259X) and course responsible for Engineering Mathematics projects (SA120X). Her Wallenberg Fellowship provides substantial research funding supporting her work on numerical algorithm development. While specific lab structures aren't detailed, her research operates within KTH's Division of Numerical Analysis, emphasizing collaborative development of simulation tools for industrial and scientific applications. Her current research focuses on extending CutFEM to complex multi-physics scenarios with emphasis on conservation properties and computational efficiency, with potential applications in aerospace, biomedical engineering, and environmental modeling.
Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.
Georgios Dimitroglou Rizell is a Senior Lecturer in the Department of Mathematics at Uppsala University, Sweden, where he also serves as Head of the Department since 2020. His academic work is centered at the Ångström Laboratory, where he conducts research in symplectic and contact topology. He maintains dual affiliations with both the Department of Mathematics and the Center for Geometry and Physics at Uppsala University. Dr. Dimitroglou Rizell earned his PhD from Uppsala University in 2012 under the supervision of Tobias Ekholm. Following his doctoral studies, he held postdoctoral positions at the Université Libre de Bruxelles (2012-2013), Université Paris-Sud (2013-2014), and the University of Cambridge (2014-2015), all supported by prestigious fellowships from the Knut & Alice Wallenberg Foundation. He returned to Uppsala University as a researcher (2015-2017) and Assistant Lecturer (2017-2021) before being promoted to Senior Lecturer in 2021. His research primarily focuses on symplectic and contact topology, with special emphasis on understanding and classifying Lagrangian and Legendrian submanifolds. His work employs advanced mathematical techniques including pseudoholomorphic curves, pseudoholomorphic foliations, Symplectic Field Theory, and Floer homology. His investigations span a broad range of topics within geometric topology, from the classification of Lagrangians near the Whitney immersion to the study of Legendrian submanifolds and their invariants. His research has significant implications for understanding the geometric structures underlying classical mechanics and quantum physics. His recent publications (2020-2025) demonstrate a consistent focus on Lagrangian and Legendrian submanifolds, with particular attention to their classification, invariants, and interactions with symplectic structures. A notable trend is the development of new techniques for studying C^0-limits of Legendrians, exact Lagrangians in various settings, and the geometric generation of Fukaya categories. His collaborative work with researchers like Michael Sullivan, Roman Golovko, and others has produced significant advances in Floer theory and symplectic field theory. Scientific Awards Wallenberg Scholar (2023-2028, KAW 2023.0294) Wallenberg Academy Fellow (extension 2022-2027, KAW 2021.0191) Wallenberg Scholar (2022-2023, KAW 2021.0300) Wallenberg Academy Fellow (2017-2021, KAW 2016.0198) As Head of the Department of Mathematics, Dr. Dimitroglou Rizell oversees academic programs and research initiatives. His leadership is supported by significant funding from the Knut & Alice Wallenberg Foundation, which has awarded him multiple prestigious fellowships throughout his career. These grants have enabled his research in symplectic geometry and supported collaborative projects with international mathematicians. Dr. Dimitroglou Rizell is actively involved in the Center for Geometry and Physics at Uppsala University, where he collaborates with researchers across mathematical disciplines. His work intersects with theoretical physics, particularly in areas related to geometric quantization and the mathematical foundations of quantum mechanics. He participates in seminar series and reading groups focused on symplectic topology and its applications.
Markus Grillitsch is Professor in the Department of Human Geography at Lund University and Director of CIRCLE (Centre for Innovation, Research and Competence in the Learning Economy). His work spans EU member states, transition economies, and developing countries with focus on spatial transformation processes. His research interests include: Economic Geography and Innovation Studies Regional Development and Knowledge Institutions Place-Based Leadership and Institutional Entrepreneurship Network Analysis and Policy Implementation Recent publications demonstrate strong focus on energy justice transitions, innovation policy limitations, and multi-scale inequality analysis. His work connects theoretical frameworks with empirical studies of regional diversification and agency-structure dynamics. Key recurring themes include sustainable development pathways, capabilities approaches, and institutional transformation processes across diverse geographical contexts. Current research projects include: Multi-level learning journeys for green and just regional transformations (Vinnova-funded) Economic resilience through Swedish hidden champions study Circular value chains and policy dynamics research Klimatneutrala städer 2030 urban climate initiative Grillitsch's work actively contributes to UN Sustainable Development Goals through research on economic growth, reduced inequalities, sustainable cities, and climate action. His fingerprint analysis shows strongest connections to Innovation (100%), Knowledge (93%), and Regional Development (66%) research domains.
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