Find Academic Supervisors

• Control Theory
• Game Theory
• Machine Learning
• +4 more

Muhammad Umar B Niazi is a Marie-Curie Postdoctoral Fellow at both KTH Royal Institute of Technology and Massachusetts Institute of Technology , working under the guidance of Karl H. Johansson and Munther Dahleh respectively. Holding a Ph.D. in Automatic Control Engineering from Université Grenoble Alpes, Niazi's research spans secure monitoring of cyber-physical systems and dynamic incentive design for sociotechnical systems . Education : Ph.D. (Grenoble INP, 2021); M.Sc. & B.Sc. (Bilkent University, COMSATS) Research Directions : Secure estimation against cyberattacks in transportation networks and epidemic models Physics-informed learning for observer design in nonlinear systems Eco-driving incentives using Stackelberg game theory Aggregated monitoring of large-scale systems Scientific Contributions : 2025 publications on distributed observers and reachability analysis 2024 work on incentive mechanisms and sensor fault detection 2023 developments in physics-informed epidemic control 2022-2019 foundational work on network observability and opinion dynamics Awards : Marie-Curie Postdoctoral Fellowship (2022-2025) Best Student Paper Finalist, European Control Conference 2019 Niazi's interdisciplinary work combines control theory , game theory , and machine learning to address resilience in transportation, epidemic monitoring, and social network dynamics. His methods integrate theoretical rigor with practical implementations through tools like SUMO simulations and physics-informed neural networks.

Danyo DanevView profile

Associate Professor

• Telecommunications
• Coding Theory
• Network Science
• +3 more

Danyo Danev is an Associate Professor at the Department of Electrical Engineering (ISY) , Linköping University , Sweden, specializing in the Division of Communication Systems . He earned an M.Sc. in Mathematics from Sofia University (1996) and a Ph.D. in Electrical Engineering from Linköping University (2001). He holds the Docent title in Data Transmission (2005). Research Pillars : Telecommunications, Coding Theory, Network Science Key Collaborations : Erik G. Larsson, Olle Abrahamsson His research spans secure communication systems , cognitive radio , superimposed codes , and network dynamics , with a focus on mathematical modeling for wireless and satellite networks. Recent work explores opinion dynamics and structural balance in network science, blending statistical signal processing with social network analysis. Scientific Contributions : Co-supervised 5 graduate students (3 PhD, 2 Licentiate degrees) Active in Communication Systems (KS) division Expertise in signal detection and error-correcting codes Advising Legacy : Mentored students on topics including digital fingerprinting , cognitive radio , and bounds for spherical codes , with ongoing work in network science.

• Formal Verification
• Model Checking
• Concurrent Systems
• +6 more

Parosh Abdulla is a Professor in the Division of Computer Systems within the Department of Information Technology at Uppsala University, Sweden. His research focuses on formal methods and program verification with emphasis on concurrent systems, memory models, and verification techniques. With an extensive publication record spanning over three decades, he has established himself as a leading researcher in his field. His primary research interests include formal verification, model checking, concurrent systems analysis, memory models (particularly TSO and PSO), string constraints, timed automata, parameterized verification, and program analysis. His work addresses fundamental challenges in verifying complex software systems, especially those involving concurrency and weak memory models. In recent years, he has expanded his research to include quantum systems verification and the application of machine learning techniques to formal methods. Analysis of his recent publications (2023-2025) reveals a continued focus on advancing verification techniques for concurrent systems, with growing interest in quantum computing verification and the integration of graph neural networks with traditional formal methods. His work demonstrates consistent innovation in reducing the state space explosion problem while maintaining verification completeness. Professor Abdulla has established significant collaborations with researchers including Mohamed Faouzi Atig, Bengt Jonsson, and others, resulting in numerous high-impact publications in top-tier venues. His research has practical implications for developing reliable concurrent software systems and advancing formal verification methodologies. While specific details about his advising activities and grants aren't explicitly mentioned in the available information, his extensive publication record suggests active supervision of graduate students and involvement in multiple research projects. His work likely contributes to formal methods research groups at Uppsala University, focusing on verification tools and theoretical foundations of program analysis.

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +5 more

Dr. Gaoyang Dai is an Assistant Professor at Uppsala University's Department of Information Technology, specializing in Computer Systems research. His work focuses on real-time systems, embedded computing, and scheduling algorithms for complex task models. Research interests include: Deterministic timing analysis in distributed systems Priority inversion handling in multicore environments Non-preemptive node scheduling for DAG tasks Cyber-physical system design paradigms Deep learning applications in wireless networks Recent publications demonstrate expertise in IEEE Transactions and DATE conference proceedings, with particular emphasis on sporadic DAG task scheduling and resource sharing protocols. Collaborative work includes development of the TIMES-Pro toolchain for CPS implementation.

• Probability Theory
• Statistics
• Gaussian Processes
• +5 more

Jonas Wallin is a Senior Lecturer and Project Manager at the Department of Statistics, Lund University. His research bridges probability theory, statistics, and computational methods, with applications to public health, climate modeling, and spatial data analysis. He actively contributes to interdisciplinary projects addressing global challenges. Key Research Areas : Gaussian processes, Bayesian inference, disease risk mapping, climate-health interactions, statistical modeling on metric graphs. Projects : Leading Horizon Europe and Swedish Research Council-funded initiatives on antimicrobial resistance, spatio-temporal modeling, and protein degradation in immunity. Supervision : Primary supervisor for PhD students Erik Karlsson Strandh and Mattias Jönsson, focusing on climate-driven disease dynamics and Gaussian processes. Collaborations : Engaged in international partnerships and peer-review activities for journals and conferences. Wallin’s work aligns with UN Sustainable Development Goals, particularly in health and environmental resilience. Recent publications highlight methodological innovations in Markov random fields and regularization techniques, while his expertise in data quality control impacts disease surveillance practices.

George BaravdishView profile

Associate Professor

• Inverse Problems
• Machine Learning
• Medical Imaging
• +6 more

George Baravdish is a Senior Associate Professor and Head of Unit at the Department of Science and Technology (ITN), Linköping University. He leads the Physics, Electronics and Mathematics (FEM) unit and holds a PhD in Applied Mathematics from Linköping University (1995), focusing on Ill-posed and Inverse Problems under Prof. V. G. Maz'ya. His research spans Inverse Problems , Machine Learning , and Mathematical Oncology , with applications in medical imaging, signal processing, and telecommunications. Notable projects include Mathematics for Machine Learning (focusing on neural ODEs) and Mathematical Oncology (modeling brain tumor growth). Baravdish has supervised two doctoral students and co-supervised two more. He has secured funding as a main or co-applicant for 15 research projects. His work bridges theoretical mathematics and practical applications in healthcare and technology. Current research themes include: Image Processing : Generalizations of the p-Laplace operator for noise reduction Telecommunications : 3D object reconstruction using Wi-Fi signals Inverse Problems : Theoretical advancements and algorithm development He contributes to interdisciplinary collaborations, including AI-driven solutions for healthcare and signal analysis.

Katharina JochemkoView profile

Associate Professor

• Geometric Combinatorics
• Discrete Geometry
• Algebraic Combinatorics
• +3 more

Katharina Jochemko is an Associate Professor in the Department of Mathematics at KTH Royal Institute of Technology. She holds a PhD (summa cum laude) from Freie Universität Berlin and a Diplom in Mathematics (with distinction) from LMU Munich. Her research focuses on geometric and algebraic combinatorics, discrete geometry, and the geometry of polynomials, with recent explorations into applications in artificial intelligence. She leads multiple research projects funded by prestigious organizations including the Wallenberg AI, Autonomous Systems and Software Program and the Swedish Research Council. Her academic contributions include groundbreaking work on Ehrhart polynomials, lattice polytopes, and syzygy bundles, as evidenced by her extensive publication record and the Best Paper Award 2018 from TU Wien. She actively supervises PhD students and postdocs, with current advisees including Jon Pål Hamre and Aryaman Jal. She serves as an associate editor for Beiträge zur Algebra und Geometrie and represents the Swedish Mathematical Society at institutional levels. Her teaching includes advanced courses in enumerative combinatorics and graph theory at KTH. She co-organizes international research initiatives like the Polytopes and Friends conference and the MaP-Trop seminar series on matroids and tropical geometry. Her research spans foundational work in geometric combinatorics with interdisciplinary applications.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +12 more

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Aws Al-ZarqaweeView profile

Researcher

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +3 more

Aws Al-Zarqawee is a Postdoctoral Researcher at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, affiliated with the Division of Network and Systems Engineering. He also serves as an Associate Professor in Norway and is the Project Manager for the AINCEPTION EDF Project, focusing on AI-driven cybersecurity solutions. PhD in Information Security from Universiti Malaysia Pahang Research interests: Cybersecurity, Artificial Intelligence, Digital Twins, and Threat Response Automation His work spans academia and industry, with teaching experience in Norway and Malaysia, and expertise in penetration testing, security architecture, and AI-based threat detection. He collaborates with international teams and has secured grants for projects like AITHREAT: Advanced Machine Intelligence Persistent Cybersecurity Threats. Recent publications focus on integrating AI with cybersecurity for critical infrastructures, digital twins, and automated threat response. His research emphasizes AI-driven detection and mitigation of complex threats. Scientific Awards Marie Skłodowska-Curie Postdoctoral Fellowship He supervises students in cybersecurity and AI, contributes to academic and industrial collaborations, and is actively involved in grant-funded projects like AINCEPTION EDF.

Mojtaba EshghieView profile

Researcher

• Smart Contracts
• Blockchain Security
• Decentralized Finance (DeFi)
• +3 more

Mojtaba Eshghie is a doctoral researcher at KTH Royal Institute of Technology, specializing in blockchain security and smart contract analysis. His PhD research focused on mitigating business logic flaws in smart contracts, addressing vulnerabilities that caused $63.8M in losses in 2024. He is affiliated with the Theoretical Computer Science division and supervised 9 master's and 2 bachelor's students in topics ranging from invariant mining to AI-driven contract repair. Education: MSc in Information Technology Engineering (Network Engineering) from University of Tehran (2019) Supervisors: Cyrille Artho (primary), Dilian Gurov (secondary) Roles: Researcher, Teaching Assistant (Software Security, Computer Security courses) His work includes developing tools like HighGuard for cross-chain monitoring, SoliDiffy for AST differencing, and the DISL dataset containing 514,506 Solidity contracts. He has contributed to conferences such as ASE, SANER, and ICST.

Johan Henrik KoskinenView profile

Senior Lecturer

• Bayesian Inference
• Statistical Network Analysis
• Social Network Modeling
• +7 more

Johan Henrik Koskinen is a Senior Lecturer in Statistics at Stockholm University . He previously held academic positions at the Universities of Melbourne, Oxford, Manchester, and Linkoping . Teaching: Courses in Multivariate Methods and Generalized Linear Models (GLM). Advising: Supervises master’s and PhD students in applied statistics, including Jonathan Januar (funded by ARO Grant W911NF-21-1-0335 for research on missing data in covert networks). Research Interests: Specializes in Bayesian inference and statistical modeling for social networks, focusing on: Partially observed network data and missing data mechanisms Longitudinal and dynamic network analysis Exponential Random Graph Models (ERGM) and stochastic actor-oriented models Computational methods for complex network data Grants: Co-investigator on ARO Grant W911NF-21-1-0335 for covert network analysis Named non-US partner on NSF Proposal No 2005661 for multidimensional networks in car purchases Software Contributions: Development of RSiena (R package for longitudinal network analysis) Maintains MPNet (graphical interface for ERGM)

Jendrik SeippView profile

Associate Professor

• Automated Planning
• Machine Learning
• Classical Planning
• +3 more

Jendrik Seipp is an Associate Professor in Artificial Intelligence at Linköping University (LiU), Sweden. He leads the 'Representation, Learning and Planning' lab within the AIICS division of the Department of Computer and Information Science (IDA). His research focuses on automated planning, particularly integrating machine learning with classical planning methods. Education: Docent in Computer Science, Linköping University (2022) Ph.D., University of Basel, Switzerland (2018) M.Sc. and B.Sc., University of Freiburg, Germany (2009–2012) Research Interests: Seipp specializes in classical planning systems, cost partitioning algorithms, heuristic search, and abstraction methods. His work emphasizes optimizing planning efficiency through techniques like saturated cost partitioning and Cartesian abstractions. He contributes to open-source planning tools such as Scorpion and Fast Downward, aiming to make planning accessible to broader audiences. Awards: IJCAI 2021 Distinguished Paper Award ICAPS 2020 Best Dissertation Award Multiple first/second places in international planning competitions (IPC, CoRe Challenge) Advising & Grants: Seipp leads the development of planning systems and frameworks like Scorpion, which won awards at ICAPS 2022 and IJCAI 2021. He collaborates on projects funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). His lab focuses on advancing symbolic AI methods and their applications in real-world planning tasks. Labs & Teams: He heads the 'Representation, Learning and Planning' lab at LiU, fostering interdisciplinary research in AI planning and optimization. The lab develops tools like Downward Lab for experiment management and contributes to open-source planning communities.

Malintha FernandoView profile

Research Fellow

• Multi-Robot Systems
• Distributed Control
• Autonomous Systems
• +4 more

Malintha Fernando is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology in Stockholm, Sweden. His current project focuses on Designing Interaction-Aware Heterogeneous Multi-Robot Systems , aiming to develop scalable distributed control policies for multi-robot systems in dynamic environments. He holds a PhD from Indiana University (2023) and an undergraduate degree from the University of Moratuwa, Sri Lanka. Prior to KTH, he served as a visiting lecturer in Machine Learning at Indiana University and completed an internship at Open Robotics. Education: PhD in Robotics/Control Systems, Indiana University, Bloomington, USA (2023) Bachelor’s degree in Engineering, University of Moratuwa, Sri Lanka Research Interests: Malintha’s work emphasizes cooperative control policies for multi-drone systems, particularly in scenarios with communication failures or dynamic environments. His research addresses challenges in humanitarian aid, disaster response, and urban air mobility (UAM) applications. He explores frameworks to balance task prioritization in real-time, minimizing human bias in system design. Key areas include: Decentralized decision-making in multi-agent systems Swarm intelligence and UAV coordination Robust control under uncertainty Grants & Supervision: His fellowship is hosted by KTH’s Department of Mathematics, under the supervision of Assistant Professor Silun Zhang (Optimization and System Theory) and Professor Petter Ögren (Robotics, Perception and Learning). The project aligns with Digital Futures’ mission to address societal challenges through digital innovation. Labs & Teams: Malintha is affiliated with the Division of Optimization and System Theory and the Robotics, Perception and Learning (RPL) group at KTH, contributing to cross-disciplinary research in robotics and control systems.

Qianwen XuView profile

Assistant Professor

• Power Systems
• Renewable Energy Integration
• Reinforcement Learning
• +5 more

Qianwen Xu is an Assistant Professor at KTH Royal Institute of Technology and a Digital Futures Faculty member. She specializes in power systems, renewable energy integration, and control systems, with a focus on smart grids, hydrogen energy, and machine learning applications in energy infrastructure. Her work addresses challenges in sustainable energy systems, grid stability, and decentralized control mechanisms. Active research projects include the C3.ai DTI-funded initiative on data-driven control of smart converters using deep reinforcement learning, and the 'Autonomous coordination and control of smart converters' project. She explores topics like electric mobility systems, climate-resilient building designs, and cyber-physical system security. Her research spans technical areas such as reinforcement learning for power converter control, hybrid energy storage systems, and resilience of distribution networks against climate change. She collaborates with Digital Futures, a center focused on advancing digital technologies for societal impact, established jointly with Stockholm University and RISE Research Institutes.

Fredrik SävjeView profile

Associate Professor

• Causal Inference
• Econometrics
• Experimental Design
• +3 more

Dr. Fredrik Sävje is a Senior Lecturer and Associate Professor at the Department of Economics, Uppsala University , specializing in causal inference and econometrics. His research focuses on experimental design, variance estimation, and treatment effect analysis under complex interference structures. Academic Affiliation: Department of Economics, Uppsala University Email: fredrik.savje@nek.uu.se Dr. Sävje’s research interests span causal inference, econometrics, and statistical methodology. Key areas include: Design-based frameworks for causal inference Threshold blocking and covariate balancing Exposure-response models in bipartite experiments Robust variance estimation under interference His recent work emphasizes: Foundations of causal inference in randomized controlled trials Optimized covariate balancing via Gram–Schmidt Walk Design Relaxing assumptions in exposure mapping for treatment effects Generalizing inference methods to sparse and complex experimental graphs Dr. Sävje has co-authored software packages including GSWDesign.jl for covariate balancing and quickmatch for generalized full matching in R. His publications appear in top journals like Biometrika , Journal of the American Statistical Association , and Electronic Journal of Statistics .