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Ingo SanderView profile

Professor

• Embedded Systems Design
• Models of Computation (MoCs)
• Mixed-Criticality Systems
• +3 more

Ingo Sander is a Professor in Electronic Systems Design at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty and the Division of Electronics and Embedded Systems. He joined KTH in 1993 and has held his current professorship since 2018. His research focuses on formal system design methodologies like ForSyDe, emphasizing embedded systems, mixed-criticality applications, and design automation. He co-founded the cross-disciplinary Digital Futures research center, which addresses societal challenges through digital technology innovation. Education: MSc in Electrical Engineering (Technical University of Braunschweig, 1990), PhD and Docent at KTH (2003, 2009). Professional experience includes work at Ericsson (1991–1993). Research Interests: Design methodologies for embedded systems, models of computation (MoCs), formal verification, and cyber-physical systems. Key contributions include the ForSyDe framework and design space exploration techniques for multiprocessor platforms. His work bridges theoretical foundations with practical implementations, targeting safety-critical and high-performance embedded systems. Teaching: Ingo Sander supervises numerous master’s degree projects in computer engineering, electrical engineering, and ICT innovation. He leads courses on embedded software, systems design, and simulation. Labs & Projects: Digital Futures collaborates with Stockholm University and RISE, advancing innovations in digital technologies. Sander’s projects include the SAFEPOWER initiative for energy-efficient mixed-criticality systems and CONTREX for control systems design.

• Machine Learning
• Computational Biology
• Cancer Genomics
• +3 more

Golnaz Taheri is an Assistant Professor at KTH Royal Institute of Technology and a Data-Driven Life Science SciLifeLab and Wallenberg (DDLS) Fellow. She holds a Ph.D. in Computer Science and specializes in applying machine learning to biological data, particularly in cancer genomics and personalized medicine. Her work bridges computational methods with life sciences to uncover biological system insights. Previously, she served as an Assistant Professor at Stockholm University's Department of Computer and System Sciences (DSV), where she coordinated large-scale courses and supervised graduate students in computational biology. Her research focuses on developing novel machine learning frameworks for analyzing complex biological datasets, including cancer mutation analysis and drug interaction prediction. Awards: DDLS Fellowship (SciLifeLab/Wallenberg) Key Projects: GenePioneer Python package, drug repurposing for SARS-CoV-2 Teaching: Examinations and course coordination for statistical methods in computer science Her research spans machine learning applications in oncology, virology, and systems biology, with a focus on translational medicine and computational tools for healthcare innovation. Current PhD openings exist in machine learning/deep learning for cancer and drug interaction studies.

• AI in Biology
• Bioimaging Analysis
• Data-Driven Science
• +3 more

Wei Ouyang is an Assistant Professor in the Department of Applied Physics at KTH Royal Institute of Technology. He leads the AICell Lab , funded by the Data-Driven Life Science Fellow program, focusing on developing AI systems for data-driven cell and molecular biology. The lab's work emphasizes integrating advanced computational tools with biological imaging to advance understanding of cellular processes. His research interests include bioimaging analysis , deep learning applications , and cloud-optimized data formats . Notable contributions include open-source platforms like ImJoy and Pycro-Manager , which enhance accessibility and reproducibility in microscopy and bioinformatics. Recent publications highlight innovations in AI-driven microscopy tools , automated imaging frameworks , and data-sharing platforms . Key projects include the ESPressoscope for portable microscopy and the BioImage.IO Chatbot for community-driven bioimaging support. Dr. Ouyang teaches the Data-driven Life Sciences course at KTH and oversees the Degree Project in Applied Physics . His work bridges computational methods with biological discovery, fostering collaborative advancements in life sciences.

Mikael LundbäckView profile

Researcher

• Forest Engineering
• Automation in Forestry
• Machine Learning Applications
• +3 more

Mikael Lundbäck is a Researcher and Postdoctoral Fellow at the Department of Physics, Umeå University, affiliated with the UMIT Research Lab. His work focuses on automation, machine learning applications, and sustainable forest operations. Current projects include the Mistra Digital Forest initiative (2019–2026) and contributions to the Technodiversity e-learning platform for harmonizing forest engineering education across Europe. Research interests include optimizing harvesting systems, tele-extraction technologies, and autonomous machinery through data-driven approaches. He collaborates with institutions like the Faculty of Forestry and Wood Technology, Zagreb University, and contributes to conferences such as IUFRO World Congress. His publications analyze productivity, cost efficiency, and terrain adaptability in forestry machinery, with a focus on deep learning, simulation models, and open data sharing. Lundbäck leads the Digital Physics group and actively participates in COFE, FORMEC, and IUFRO research networks. His work bridges technical innovation with practical forestry challenges, emphasizing global trends and economic feasibility.

Philipp HallerView profile

Associate Professor

• Programming Languages
• Type Systems
• Concurrency
• +11 more

Philipp Haller is an Associate Professor in the Department of Computer Science at KTH Royal Institute of Technology, affiliated with the Theoretical Computer Science Group, Digital Futures, and WASP. He holds a PhD from EPFL (2010) and has held research positions at Stanford and EPFL, and industry roles at Typesafe Inc. Research Interests: His work centers on programming language design, concurrency (especially actor and futures models), distributed programming, type systems, and serverless computing. He is a leading contributor to the Scala language, having co-created its async/await syntax and futures library. His recent research includes the Portals framework for stateful serverless applications and formal models for consistency and concurrency safety. Publication Trends: His recent work (2020–2024) shows a strong focus on safe and scalable concurrency, stateful serverless systems, formal verification of language features, and distributed data consistency. He frequently publishes in top venues like ECOOP, OOPSLA, and PLDI, often combining theory with practical implementations in Scala. 2019 ACM SIGPLAN Programming Languages Software Award (for Scala) 2017 Teacher of the Year, EECS, KTH Advising and Grants: He supervises PhD students including Jonas Spenger, Xin Zhao, and Viktor Palmkvist. He led the Software Development Academy, a reskilling program for immigrants funded by the Wallenberg Foundations. He actively participates in academic service, co-chairing workshops like PLACES and AGERE!, and serving on program committees for OOPSLA, ECOOP, and POPL. Labs and Teams: He is part of the Theoretical Computer Science Group at KTH and leads research related to the Portals framework, which unifies dataflow and actor models for edge and serverless computing.

Håkan SundellView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Parallel and Distributed Systems
• +7 more

Håkan Sundell is an Assistant Professor of Computer Science at the University of Borås , affiliated with the Faculty of Librarianship, Information, Education and IT and the Department of Information Technology . Since 2006, he has been active as a teacher in informatics and a research group leader for the CSL@BS research group , while also serving as program manager for the System Architecture Education with a Focus on Software Development . His research spans Artificial Intelligence , Parallel and Distributed Systems , Concurrent Programming , and Machine Learning Applications , with a particular emphasis on lock-free data structures and GPU algorithms . He has developed over 30 courses and supervised multiple doctoral students to completion. Scientific contributions include advancements in non-blocking algorithms, real-time systems, and AI-driven marketing tools. His article trends reflect interdisciplinary work in data science for fashion retail , district heating analytics , and marketing campaign optimization . Key Awards: Best Paper Award at IPDPS 2003 Recognized as a pioneer in Swedish computer game development As a main supervisor since 2012, he has driven externally funded research projects (VR, SSF, KKS, HUR) and served on university committees, including chairing the education committee from 2018–2022.

Moa JohanssonView profile

Associate Professor

• Artificial Intelligence
• Automated Reasoning
• Neuro-Symbolic Methods
• +4 more

Moa Johansson is an Associate Professor (Docent) at the Department of Computer Science and Engineering, Chalmers University, Sweden. She serves as Co-Director of Chalmers' Area of Advance in Information and Communication Technology, driving interdisciplinary AI research. PhD in Automated Theorem Proving (University of Edinburgh, 2009) Postdoc at University of Verona (2009-2011) Her research spans neuro-symbolic AI methods , combining neural networks with symbolic reasoning for applications in mathematics, sports science, and political science. Current projects include: NeSSy (2023-): Neuro-symbolic synthesis via reinforcement learning Neurodynamic Programming (2023-): Agent communication learning Countering Bias in AI (2023-): NLP applications in social sciences Bias in Political Analysis (2020-2025): WASP-HS funded Recent publications focus on theory exploration , neuro-symbolic methods , and AI applications across disciplines. Scientific achievements include a VINNMER Marie Curie Fellowship and Best Paper Award at CICM 2014. Led TRACKS project course on sports digitalization Developed HipSpec automated inductive prover

• Geometric Variation
• Robust Design
• Statistical Variation Simulation
• +7 more

Rikard Söderberg is a Professor of Product and Production Development at Chalmers University of Technology , where he earned his PhD in 1995. As director of the Wingquist Laboratory , he leads research initiatives in Geometry Assurance & Robust Design . His work bridges academia and industry through collaborations with automotive and aerospace sectors, focusing on minimizing geometric variations in manufacturing. Research Focus: Robust design, variation simulation, assembly optimization Key Collaborations: Automotive (Volvo, CEVT) and aerospace industries Scientific Leadership: Director of Wingquist Laboratory (since reorganization) Research Trends in his recent publications reveal interdisciplinary efforts combining digital twin technology , physics-based modeling , and evolutionary optimization . Articles address challenges in additive manufacturing , welding precision , and perceived product quality through computational frameworks. Scientific Recognition: Fellow, American Society of Mechanical Engineers (ASME) Fellow, International Academy for Production Engineering (CIRP) Member, Royal Swedish Academy of Engineering Sciences (IVA)

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Dag Björnberg is a researcher at Linnaeus University, affiliated with the graduate school Data Intensive Applications (DIA) and the research groups Data Intensive Software Technologies and Applications (DISTA) and Linnaeus University Centre for Data Intensive Sciences and Applications. His research focuses on applying artificial intelligence and machine learning techniques to solve challenges in the forest industry. Key projects include developing algorithms for log tracking using end-grain fingerprints, generating photorealistic log end images via conditional Generative Adversarial Networks (cGANs), and improving forest inventory efficiency through AI-driven methodologies. Doctoral project: Advanced identification methods for the forest industry via computer vision and AI ForestMap: Global forest mapping methodology Tree volume measurement by AI: Sweden-Brazil collaboration His recent publications highlight the integration of GANs, image translation, and data-intensive approaches in forest resource management and AI development.

• Open Source Software
• ICT Standardization
• Software Engineering
• +2 more

Jonas Gamalielsson is an Associate Professor in Computer Science at the Department of Information Technology , University of Skövde. He leads software engineering research projects and serves as course coordinator for advanced master's programs in information technology. Academic affiliation: School of Informatics, University of Skövde Research focus: Open Source Software, ICT Standardization, Digital Government Active in: Empirical Software Engineering, Standards for Smart Systems, Open Collaboration His research explores intersections between open standards and open source implementations, with recent projects analyzing: Public sector open source governance Swedish procurement of SaaS solutions PDF/A document standard compliance Industrial IoT ecosystem standardization Open source business adoption strategies Publications span 2014-2025, with key contributions in the Journal of Standardisation , Empirical Software Engineering , and International Journal of Standardization Research . Collaborates extensively with Björn Lundell and other scholars in software standardization and open source analysis.

• Information Security Risk Management
• Cybersecurity
• Smart Systems Security
• +4 more

Martin Lundgren is a Senior Lecturer in Informatics at the School of Informatics, University of Skövde. His work focuses on Information Security and Cybersecurity, particularly in air traffic management, automotive systems, and smart home environments. Specializes in human-centric cybersecurity approaches Active in certificateless cryptography for transportation systems Investigates stress factors in security risk management His recent publications analyze cybersecurity readiness in modern cars (2023-2024), threat intelligence integration (2023), and risk management tools for aviation (2025). Research trends show cross-domain applications of security frameworks and human factors in cybersecurity.

• Serious Games
• Game Development
• Human-Computer Interaction
• +5 more

Ran Zhang is a Senior Lecturer in Media Arts, Aesthetics, and Narration at the Department of Information Technology, School of Informatics, University of Skövde. Their work focuses on the intersection of game design, visual communication, and educational technology, particularly in the context of serious games and digital storytelling. University: University of Skövde School: School of Informatics Department: Department of Information Technology Academic Rank: Senior Lecturer Ran Zhang's research explores how visual elements and storytelling mechanics enhance learning outcomes in serious games. Key areas include cross-cultural game design, game-as-a-service models, and the application of combinatorial optimization to balance game units. They have published in journals like the International Journal of Serious Games and presented at conferences such as IEEE ICEBE and Sweden Game Conference . Recent publications highlight trends in: Visual communication for educational effectiveness Transition from product to service-oriented game development Localizing serious games for Nepalese audiences Optimization techniques in game mechanics Ran Zhang collaborates with researchers such as Hua Bai, Ulf Wilhelmsson, and Björn Berg Marklund. They coordinate bachelor-level courses in media and storytelling, emphasizing practical applications of game design principles in education and health contexts. Ran Zhang is actively involved in the University of Skövde's initiatives for sustainable digitalization and international collaboration through programs like INGENIUM and ShiftLabs . Their work bridges academic research with industry needs, focusing on human-centric technology development.

• Automation Engineering
• Functional Programming
• Optimization
• +7 more

Richard Senington is a Senior Lecturer in Automation Engineering at the School of Engineering Science, University of Skövde. He holds a PhD from the University of Leeds (UK) obtained in 2013 and has been working at the University of Skövde since 2016 after several years as a software consultant. His primary research and teaching focus on automation technologies within industrial production systems. Senington completed his doctoral studies at the University of Leeds in 2013 before working as a software consultant for several years. He returned to academia in 2016 when he joined the University of Skövde, where he has since established himself as a researcher in automation engineering and related fields. His research interests span Functional Programming, Optimization techniques, Industrial Internet of Things, and Human-Robot Collaboration. Senington's work particularly focuses on applying advanced computational methods to industrial production problems. His recent research has explored Knowledge Graphs for manufacturing decision support, Monte Carlo Tree Search applications in production systems, and containerization technologies for factory software management. He actively investigates how these technologies can improve production efficiency, decision-making processes, and system resilience in manufacturing environments. His publication record shows consistent output in industrial simulation and production engineering venues, with a focus on practical applications of computational methods in manufacturing. His most recent work (2024-2025) centers on Knowledge Graph applications for production systems, while his earlier work (2018-2021) focused on containerization technologies and Monte Carlo Tree Search applications. Senington is currently involved in two major research projects: 'Future-Proof Factories: The SMART Way to Resilient Production' (December 2025 - November 2029), which explores how factories can maintain operations during disruptions like rising energy prices or machine failures, and 'Virtual Factories of the Future' (October 2018 - September 2026), an eight-year research profile focused on knowledge-driven optimization in manufacturing. He previously contributed to the 'SUDO' project (October 2020 - September 2024), which addressed lock-in effects in IT system development for complex application domains. Within the EU-funded SYMBIO-TIC project, Senington collaborates with Magnus Holm on Human Robot Collaboration research. His teaching responsibilities include multiple courses at both bachelor's and master's levels in engineering disciplines, with a focus on advanced production technologies and automation systems.

Johan Bengtsson-PalmeView profile

Assistant Professor

• Antibiotic resistance
• Microbial ecology
• Pathogenesis
• +5 more

Johan Bengtsson-Palme is an Assistant Professor at the Division of Systems Biology at Chalmers University of Technology in Gothenburg and affiliated with the Sahlgrenska Academy at University of Gothenburg. He is a DDLS Fellow funded by the Wallenberg DDLS initiative and leads a research group focused on data-driven microbiology and microbial ecology. His work bridges bioinformatics, molecular methods, and ecological theory to address critical issues in human and environmental health. His research spans three interconnected areas: pathogenicity mechanisms , effects of disrupted microbiome interactions , and antibiotic resistance development and spread . He combines large-scale experimental work, metagenomics, and bioinformatics to investigate how environmental factors influence microbial communities. His lab develops essential bioinformatics tools like ITSx and Metaxa that are widely used in microbial ecology research. Analysis of his recent publications reveals a strong focus on environmental antibiotic resistance monitoring, with significant contributions to establishing baseline resistance levels across environments, developing frameworks for standardized monitoring, and investigating novel resistance mechanisms. His work increasingly connects laboratory findings with policy implications, particularly regarding EU wastewater monitoring requirements. Scientific Awards: DDLS Fellow (Wallenberg DDLS initiative) As principal investigator of the EMBARK program and contributor to the SEARCHER initiative, Bengtsson-Palme secures substantial research funding for environmental antibiotic resistance research. His group currently includes doctoral students Emil Burman and Mirjam Dannborg, postdoc Anna Abramova, bioinformatician Marcus Wenne, and external associated doctoral student Lisa Teichmann. He actively collaborates with researchers across Europe on antibiotic resistance surveillance projects. The Bengtsson-Palme Lab maintains strong connections with multiple research centers including CARe, DDLS, EMBARK, and UNITE. They coordinate international efforts to standardize environmental antibiotic resistance monitoring and develop innovative approaches to resistance gene discovery. The lab's work directly informs policy discussions through contributions to EU monitoring requirements and participation in symposia focused on data-driven environmental health assessment.

Hazem TorfahView profile

Assistant Professor

• cyber-physical systems
• formal verification
• runtime monitoring
• +5 more

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.