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Isak SamstenView profile

Senior Lecturer

• Data Science
• Machine Learning
• Time Series Analysis
• +3 more

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +4 more

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

• 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.

Siegfried RouvraisView profile

Associate Professor

• Engineering Education
• Higher Education Systems
• Curriculum Design
• +8 more

Siegfried Rouvrais is an Associate Professor and Research Scientist at IMT Atlantique, part of the Institute Mines-Télécom, and a CNRS research fellow at Lab-STICC. He specializes in engineering education and software systems research, focusing on curriculum design, decision-making skills in VUCA contexts, and enterprise architecture. His affiliations include IMT Atlantique (Brest, France), CNRS, and international collaborations like the CDIO initiative. Education: PhD in Software Architecture and Distributed Systems Modeling from University of Rennes/IRISA (2002) Research Interests: Engineering Education Systems (curriculum transformation, accreditation frameworks, project-based learning) and Software Systems (non-functional properties, service composition, model-driven engineering). His work integrates educational and technical domains to enhance program quality and student readiness for volatile environments. Recent Articles Highlight: Analysis of curriculum resilience in VUCA contexts, design of expedition learning models, and frameworks for higher education systems. His publications emphasize interdisciplinary methods, quality assurance, and global educational collaboration. Awards: Best Paper Award (MOPAS 2011), Emerging Innovative Course Prize (2021). Grants & Projects: Coordinator of DECART (€400k), HOOD (ongoing), DAHOY (€395k), and others. Advised over 50 student projects, with roles in program evaluations (e.g., French Engineering Accreditation Commission). Labs/Teams: Member of Lab-STICC’s P4S team (Safe & Secure Systems) and former leadership in PASS and ACS2 research groups.

• Digital Twins
• Machine Learning
• Data-Driven Approaches
• +3 more

Joel Cramsky is a Master of Science in Engineering Physics and Industrial PhD student at Linnaeus University within the Data Intensive Applications (DIA) program. He is employed as an R&D engineer at Volvo CE in Braås , focusing on digital twin developments and machine learning applications in industrial contexts. His research explores data-driven solutions for material handling optimization and vehicle diagnostics. University: Linnaeus University School: Industrial Research School Email: joel.cramsky.2@volvo.com Joel’s work emphasizes digital twin platforms and machine learning for commercial vehicle systems. He contributes to projects under the Data Intensive Software Technologies and Applications (DISTA) research group and the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). His publications focus on improving industrial efficiency through advanced data analytics.

• Software Engineering
• Self-adaptive Software Systems
• Software Reuse
• +3 more

Dr. Jesper Andersson is a Professor of Computer Science and Dean of the Faculty of Technology at Linnaeus University. He holds a PhD from Linköping University (2007) and has extensive leadership experience, serving as Department Chair from 2013–2020. His research focuses on self-adaptive software systems, software reuse, and cyber-physical systems. He has published widely in top venues like ACM Transactions on Autonomous and Adaptive Systems and Computing , and actively contributes to organizing international conferences. Education: Responsible for advanced courses in software design and development processes. Engaged with industry through technical advising for global companies. Completed major projects include developing a master’s program in computer science and the PROSSES project on self-protecting systems. Current research projects include Digital Twin of Organizations (DTO), DIACCESS for sustainable cities, and Aladino for adaptable architectures. His work emphasizes resilience frameworks, decentralized control, and industrial adaptation practices. Key collaborations include leading the AdaptWise research group and co-chairing SEAMS 2023. His articles span self-adaptive patterns, trust-aware systems, and IoT applications, reflecting a strong focus on both theoretical and applied software engineering challenges.

• Software Development
• Enterprise Application Integration
• Open Source Infrastructure
• +5 more

Benneth Christiansson is an Assistant Professor at Karlstad University, focusing on software development paradigms, enterprise application integration, and open-source infrastructure. His research bridges the gap between emerging cloud-based tools (e.g., Software-as-a-Service) and traditional enterprise systems, emphasizing lean integration approaches. Research Interests: His work centers on component-based development, integration challenges in heterogeneous systems, and the adoption of open-source solutions to streamline enterprise processes. He explores frameworks for aligning software components with business processes, addressing interoperability and data standardization issues in public and private sectors. Publications: Over 15+ publications since 1995, with recent focus on lean integration strategies (2011-2012), open-source adoption in municipal systems (2007), and foundational component specification models (2003-2005). Key themes include COTS component acquisition, business-process-driven software specifications, and agile modeling techniques. Awards: No scientific awards explicitly listed in the text. Advising & Grants: No advisees or grants mentioned in the provided text. His contributions appear to focus on theoretical and applied research in software systems. Labs/Teams: No specific labs or collaborative teams described, though his work implies engagement with enterprise IT and software development communities.

• Game Development
• Serious Games
• Game Analytics
• +7 more

Henrik Engström is a Professor of Informatics at the University of Skövde, affiliated with the School of Informatics and Department of Information Technology. He holds a PhD in Computer Science from the University of Exeter. His research focuses on game development processes, serious games, and interdisciplinary collaboration in game design. Engström has led multiple projects, including the Game Tech Academy and Sweden Game Arena initiative, aiming to leverage game technology for societal challenges in health, education, and industry. He coordinates the Game Development Master's Programme and has published extensively on topics like game analytics, inclusive design, and mobile game publishing strategies. His work often bridges academia and industry, emphasizing practical applications in training simulations and healthcare. Education: PhD in Computer Science (University of Exeter) Key Projects: Sweden Game Arena, Game Hub Scandinavia, Digital Parental Support Tools Research Interests: Game design processes, serious games for health, inclusive game development, and mobile game business models Engström’s research outputs span over two decades, with notable contributions to understanding game development challenges, stress detection in games, and the application of AI in game narratives. His book Game Development Research (2020) synthesizes empirical insights from industry practices. He collaborates internationally, including with Japanese and Scandinavian institutions, to advance educational game models and industry-academia partnerships. His articles highlight trends in data-driven decision-making for indie game publishers, the psychological impact of game design on player experiences, and leveraging game jams for addressing heavy societal topics. Engström also leads grants focused on digital health tools for expecting parents and sustainable urban planning via game-based participation. Labs/Teams: Key contributor to the Sweden Game Conference and collaborator with Game Tech Academy, fostering innovation in game-based solutions for societal challenges.

• 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.

Nicholas PearceView profile

Assistant Professor

• Structural Biology
• Macromolecular Structure Determination
• X-ray Crystallography
• +7 more

Nicholas Pearce is an Assistant Professor at Linköping University, affiliated with the Department of Physics, Chemistry and Biology (IFM) and the Bioinformatics (BIOIN) division. He leads the Data-Driven Determination of Macromolecular Structures (D3MS) group, also known as PearceLab @ LiU, and is affiliated with SciLifeLab and the Wallenberg Centre for Molecular Medicine (WCMM) through the Data-Driven Life Sciences (DDLS) program. His research focuses on advancing structural biology by developing computational and experimental methods that capture the dynamic nature of proteins. Key interests include protein flexibility, disorder, and functional dynamics, with applications in drug discovery and macromolecular modeling. The group employs techniques such as X-ray crystallography, cryo-EM, machine learning, and statistical modeling to improve the resolution and interpretability of macromolecular structures. The recent publications highlight a strong focus on software and method development for structural biology, particularly in multi-dataset analysis and crystallographic data processing. The work contributes to open-source tools like the CCP4 suite and introduces novel approaches such as PanDDA and PanDEMIC.adp for detecting ligand binding and modeling disorder. While no formal scientific awards are listed in the provided text, his involvement in major collaborative projects and national research initiatives underscores his growing impact in the field. He actively mentors students and welcomes Master’s candidates to join his research group. Nicholas began his independent research career at Linköping University on October 1, 2022, with funding supporting the launch of his lab. His group emphasizes data-driven decision-making in structural analysis and collaborates extensively within Sweden and internationally. Current projects include multi-dataset analysis for fragment screening and decomposition of protein disorder using elastic net models. The D3MS group is active in outreach and scientific communication, maintaining a blog and public presence, including a Twitter feed. They have participated in international research visits, such as a recent trip to the University of Hamburg, and national conferences like Sweprot in Tällberg.

• Urban Morphology
• Urban Planning
• Space Syntax
• +7 more

Meta Berghauser Pont is a Professor of Urban Morphology and Urban Planning at Chalmers University of Technology. She leads the Spatial Morphology Group (SMoG), focusing on quantitative analysis of urban form, space syntax, and design theory. Key research themes: urban density, sustainable cities, social-ecological systems, pedestrian movement modeling Authored the 2023 book Spacematrix: Space, Density and Urban Form , redefining density metrics Her work bridges analytical urban morphology with practical urban planning applications, particularly in noise/air quality management, transport infrastructure, and digital twin city modeling. She manages pedagogical development for architecture programs at Chalmers. Active projects include: Digital Twin Cities Centre (EU/VINNOVA) Sustainable Urban Form (Formas) Green Infrastructure Integration (Mistra Urban Futures) Multi-scale Climate Proofing (VINNOVA) Urban Design Calculator (Naturvårdsverket) Her lab develops open-source tools like the Place Syntax Tool (PST) for morphological analysis of cities.

Evan Patrick O'ConnorView profile

Associate Professor

• Computational Astrophysics
• Core-collapse Supernovae
• Neutrino Physics
• +4 more

Evan Patrick O'Connor is an Associate Professor in the Department of Astronomy at Stockholm University. His research focuses on computational astrophysics, particularly core-collapse supernovae, neutrino physics, and black hole formation. He leads research in the Computational Astrophysics group at the Department of Astronomy, where development of computational tools spans research areas from solar physics to cosmology. Dr. O'Connor received his Ph.D. from Caltech in the TAPIR group, following a bachelor's degree in Science (Physics, Honours, Co-op) from the University of Prince Edward Island. He was a postdoctoral fellow at the Canadian Institute of Astrophysics from 2012-2014 and a Hubble Fellow at North Carolina State University from 2014-2017 before joining Stockholm University. His research interests span computational astrophysics with a focus on core-collapse supernovae mechanisms, black hole formation, neutrino physics, gravitational waves, and the nuclear equation of state. He develops and utilizes sophisticated computational models to study the dynamics of compact objects and their connection to detailed microphysics. His work often involves multimessenger approaches, connecting theoretical models with potential observational signatures across neutrino, electromagnetic, and gravitational wave channels. Dr. O'Connor has made significant contributions to open-source scientific software development, creating tools like NuLib, GR1D, and various equation of state resources that have become valuable community resources. Analysis of his recent publications reveals a strong focus on understanding the complex interplay between stellar structure, nuclear physics, and explosion mechanisms in core-collapse supernovae. His research increasingly incorporates multi-dimensional effects, phase transitions in dense matter, and their observational consequences across multiple messenger channels. Recent work shows growing attention to data-driven approaches for connecting simulations with potential observations. Dr. O'Connor has received notable recognition including: Hubble Fellowship (2014-2017) He has developed and maintains several open-source tools including NuLib (neutrino interaction library), GR1D (spherically-symmetric general-relativistic hydrodynamics code), and various equation of state resources. His research group collaborates extensively with international teams studying supernova mechanisms and related phenomena, contributing to projects like SNEWS (Supernova Early Warning System). Dr. O'Connor leads the Computational Astrophysics group at Stockholm University's Department of Astronomy, which develops computational tools spanning research areas from solar physics to cosmology. The group maintains strong connections with international supernova research communities and contributes to global efforts in multi-messenger astronomy.

Seif HaridiView profile

Professor

• Parallel Computing
• Distributed Computing
• Cloud Computing
• +4 more

Seif Haridi is a Professor at KTH Royal Institute of Technology in Stockholm, Sweden, specializing in parallel and distributed computing systems. He holds dual roles as Chair-Professor of Computer Systems and Chief Scientific Advisor at RISE SICS. His research integrates systems engineering with theoretical foundations, focusing on programming systems, distributed computing, and big data technologies. Key contributions include co-designing SICStus Prolog, the Mozart Programming System, and Apache Flink, as well as leading the development of HOPS, a European big data platform awarded the IEEE Scale Prize 2017. He has led major EU projects like EIT-Digital’s cloud computing initiative and co-founded startups such as LogicalClocks and HiveStreaming. His teaching includes courses on distributed algorithms and peer-to-peer computing at KTH. Notable awards include the European Data Science Technology Innovation 2019. His work spans systems like HOPS, Flink, and Kompics, emphasizing scalability and robustness in distributed environments. Current projects include CDA (Continuous Deep Analytics) and ExtremeEarth for geospatial data analysis. Research interests include distributed algorithms, consensus protocols, and cloud-native systems. His lab’s contributions to scalable storage (e.g., HopsFS) and stream processing (Apache Flink) highlight his impact on both academia and industry.

• Software Engineering
• Decision Models
• Framework Evolution
• +6 more

Michael Mattsson is a Professor in the Department of Software Engineering at Blekinge Institute of Technology (Faculty of Computing). His research focuses on software engineering practices, particularly in decision models for asset selection, framework evolution, and software architecture evaluation. Industrial collaboration and empirical validation are central to his work. Research Themes: Decision models, framework stability, quality attribute trade-offs, and industrial software evaluation. Projects: Completed the VITS project on test data visualization and contributed to the ORION project (funded by the Knowledge Foundation). Publications: Key topics include asset selection surveys, framework evolution comparisons, and systematic mapping studies in software engineering.

Nasrine OlsonView profile

Researcher

• Accessibility Research
• Human-Centered Design
• Cultural Heritage Technology
• +3 more

Nasrine Olson is a researcher at the Department of Library and Information Science, University of Borås. She serves as Director of INCLUDE - Center for Inclusion Research and coordinates the Horizon Europe MuseIT project, focusing on inclusive digital heritage technologies. Her work bridges accessibility research, human-centered design, and cultural heritage preservation.