Find Academic Supervisors

• Business Process Management
• Process Mining
• Object-Centric Process Mining
• +6 more

Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.

Marc HellmuthView profile

Associate Professor

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +10 more

Marc Hellmuth is an Associate Professor of Computational Mathematics at the Department of Mathematics, Stockholm University, Sweden. His academic career includes previous positions as Junior Professor for Biomathematics and Computer Science at University of Greifswald, Germany (2015-2020), Lecturer at School of Computing, University of Leeds, UK (2020), and PostDoc positions at Saarland University and Max-Planck Institutes. Dr. Hellmuth earned his PhD in Computer Science from University of Leipzig, Germany (2007-2010, summa cum laude), supervised by Peter F. Stadler, and completed his Venia Legendi (habilitation) at Saarland University, Germany (2016). His research spans the interface of discrete mathematics, computer science, and life sciences with emphasis on: Discrete Mathematics including Graph Theory, Combinatorics, and Optimization Algorithm Design and Complexity Theory Mathematical and Computational Biology Phylogenomics and Evolutionary Analysis Computational Chemistry and Atom Tracking His publication record demonstrates a consistent focus on developing mathematical frameworks and efficient algorithms for biological problems, particularly in phylogenomics, orthology detection, and evolutionary analysis. His work bridges theoretical computer science with practical applications in biology and chemistry, often resulting in open-source software tools. Dr. Hellmuth has developed numerous software tools including AsymmeTree for phylogenetic simulation, tralda for tree algorithms, and specialized tools for phylogenomics, atom tracking, and graph analysis. His collaborative work extends internationally with research visits to institutions including Yale University, University of Leoben, Vienna University of Economics, University of Southern Denmark, Université de Montréal, and institutions in China.

Klas MarkströmView profile

Professor

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

Markus H. FlierlView profile

Associate Professor

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +3 more

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

• Control Engineering
• Process Control
• Optimal Control
• +11 more

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

Anna LombardiView profile

Associate Professor

• Predictive Control
• Neural Networks
• Control Systems
• +3 more

Anna Lombardi is an Associate Professor at the Department of Science and Technology (ITN) of Linköping University, Sweden. She is responsible for courses in control systems across engineering programs at Campus Norrköping. She holds a Master's (1992) and PhD (1996) in Control Systems from Università di Firenze, Italy, with prior research at the Neural Network Group at Università di Firenze and Università di Siena. Her research focuses on predictive control, neural networks, and modeling biological systems. Key contributions include controllability analysis of networks and community structures in complex systems. She collaborates internationally, as seen in co-authored works with institutions like DII of Università di Siena and the ISMIS conference. Her academic affiliations include Physics, Electronics, and Mathematics at Linköping University. No specific grants or awards are listed in the provided texts, though her work reflects sustained engagement in interdisciplinary technical research.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +6 more

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Robotics
• Autonomous Systems
• Spatial Cognition
• +6 more

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

Christiane SchmidtView profile

Academic

• Algorithm Design and Analysis
• Computational Geometry
• Air Traffic Management
• +3 more

Christiane Schmidt is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Communications and Transport Systems (KTS) research group. Her work focuses on algorithm design, air traffic management, railway optimization, and computational geometry. She contributes to both academic research and practical solutions for transportation systems. Education and Research: While specific educational details are not provided, her research emphasizes algorithmic solutions for complex transportation challenges. Her team develops models for air traffic controller workload prediction using machine learning and explores late train-crew rescheduling via heuristic approaches. Research Interests: Key areas include railway traffic optimization, air traffic management systems, and computational geometry applications. Recent projects involve guard route optimization for polygons, multi-agent path planning, and automated aircraft arrival scheduling. Publications: Her 2025 work highlights advancements in tabu-search-based scheduling, machine learning for air traffic analysis, and train path insertion methods. Earlier papers address workforce scheduling and disaster recovery in transportation networks. Labs and Teams: Active within the Communications and Transport Systems group, she collaborates on projects like the Flight Logistics Unit (FL) and contributes to initiatives like B-PREPARED for disaster preparedness.

• Community Detection
• Graph Clustering
• Graph Representation Learning
• +4 more

Susanna Pozzoli is a doctoral student at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. She also serves as the vice chairperson of the EECS PhD Council, representing doctoral students at the school level. Education: Bachelor of Science in Engineering of Computing Systems from Politecnico di Milano (2017) EIT Digital Master's Programme in Data Science (2017–2019), completed first year at Politecnico di Milano and second year at KTH in Stockholm, Sweden Research Interests: Susanna’s work focuses on advancing graph-based machine learning techniques, including community detection, graph clustering, role discovery, and unsupervised learning. She explores how graph representation learning can enhance performance evaluation in complex networks, with applications ranging from social network analysis to biomedical data science. Publications: Her recent work highlights innovations in graph embedding techniques and diffusion-driven role recognition, alongside interdisciplinary research in domain-agnostic feature selection for breast cancer analysis. These studies reflect her expertise in bridging theoretical computer science with practical data-driven solutions. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Teams: While no specific lab affiliations are listed, her research is embedded within the collaborative environment of KTH’s SCS division.

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.

• Information Visualization
• Network Visualization
• Human-Computer Interaction
• +4 more

Claudio Linhares is a Senior Lecturer at Linnaeus University within the Department of Computer Science and Media Technology , Faculty of Technology in Växjö, Sweden. His work bridges theoretical research with practical applications in visualization and human-computer interaction. Teaching: Masters courses in Computational Network Analysis, Information Visualization, and Data Analysis Projects; Bachelor thesis supervision; Industry-focused Visual Analytics training Research: Focuses on information/network visualization, visual analytics, and human-in-the-loop AI with cross-sector applications in forestry and healthcare Projects: Leads the Smart Dat initiative for industrial digitalization and collaborates with the Information and Software Visualization (ISOVIS) research group His recent publications demonstrate interdisciplinary applications of visualization techniques, including fairness-aware urban planning tools and temporal network analysis frameworks. Current work explores topological persistence methods and augmented reality training systems for manufacturing SMEs. Linhares contributes to academic collaboration through active involvement in research groups and industry partnerships, while maintaining a teaching-focused role in both undergraduate and graduate programs.

Angela FontanView profile

Assistant Professor

• Networked Systems
• Nonlinear Dynamics over Networks
• Signed Networks Theory
• +4 more

Angela Fontan is an Assistant Professor at the Division of Decision and Control Systems, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology in Stockholm, Sweden. She specializes in networked systems and nonlinear dynamics with applications to social networks and cyber-physical-human systems. Her educational background includes: B.Sc. in Information Engineering (2013) from the University of Padova, Italy M.Sc. with honors in Automation Engineering (2016) from the University of Padova, Italy Ph.D. in Automatic Control from Linköping University (2021), with dissertation titled "Collective decision-making on networked systems in presence of antagonistic interactions" Dr. Fontan's research focuses on networked systems and nonlinear dynamics over networks , particularly examining how antagonistic interactions affect collective decision-making processes. Her work spans signed networks theory and cyber-physical-human systems (CPHS) , with applications to smart cities and Live-in Laboratories. She investigates how social networks influence individual decisions and how these dynamics can be leveraged to promote sustainable behaviors in areas like food choices and building occupancy. Her methodological approach combines control theory, graph theory, and behavioral science to develop mathematical models of complex socio-technical systems. Her recent publications demonstrate a clear trajectory from theoretical foundations in signed network dynamics toward applied research addressing societal challenges. Early work focused on consensus algorithms and stability properties of signed Laplacian systems, while more recent publications apply these theoretical insights to sustainability challenges in food systems, building energy use, and social behavior change. This evolution shows her commitment to translating theoretical control concepts into practical solutions for societal-scale problems, particularly in the domains of sustainability and human-system interaction. Dr. Fontan has contributed to significant collaborative efforts, including the "Control for Societal-Scale Challenges: Roadmap 2030" initiative through the IEEE Control Systems Society. Her work has been presented at major conferences including IEEE CDC, IFAC World Congress, and European Control Conference. She is actively involved in mentoring and research leadership, recently announcing PhD positions in "Learning and control for networked cyber-physical-human-systems." Dr. Fontan has also organized and participated in numerous workshops, including the Stockholm Workshop on Emerging Topics in Systems and Control and the IEEE CSS Workshop on Control for Societal-Scale Challenges. Her research is conducted within the context of the KTH Live-In Lab, a unique facility that functions as a "Live-in Laboratory" for studying human interaction with cyber-physical systems in real building environments. She collaborates with interdisciplinary teams to investigate how technology can support sustainable behaviors in urban settings.

• Control Systems
• Cyber-Physical Systems Security
• Power Systems Resilience
• +3 more

Kamil Hassan is a Doctoral student and Researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. His work focuses on securing cyber-physical infrastructure through advanced control methodologies. His research spans Control Systems , Cyber-Physical Security , and Power Systems Resilience , with emphasis on mitigating attacks in time-critical networks. Key contributions include finite-time control barrier functions for power inverters and randomized detector tuning for attack impact reduction. His methodologies integrate hardware-in-the-loop validation with theoretical guarantees. Recent publications (2021–2025) reveal a trajectory toward resilient control architectures for energy systems, blending multiagent consensus theory with security-aware design. Work on power inverter networks dominates his output, addressing grid stability under cyber threats through novel barrier function frameworks and simulation-validated approaches. Hassan serves as course assistant for Cyber-Physical Security in Time-Critical Systems (EL2850) and operates within KTH's Decision and Control Systems division, contributing to hardware-in-the-loop testing environments for critical infrastructure protection.