جست‌وجوی استاد و پژوهشگر

• Molecular Computer Simulations
• Force Field Development
• Coarse-Grained Modeling
• +۷ مورد دیگر

Alexander Lyubartsev is a Professor at the Department of Chemistry in Stockholm University, specializing in Molecular Computer Simulations . His research bridges fundamental physical chemistry with applied computational modeling, focusing on biomembranes, DNA-chromatin structures, inorganic surfaces, and multiscale method development. He teaches courses like Physical Chemistry (15 hp) and Chemical Modeling (7.5 hp) , mentoring students in computational approaches for molecular systems. Education: PhD in Molecular Biophysics (1988), St. Petersburg State University Academic Positions: 1988-1992: Researcher, St. Petersburg State University 1993-2001: Research Associate, Stockholm University 2001-2007: Associate Professor, Stockholm University 2007-present: Professor, Stockholm University His research interests center on developing computational methods for molecular systems. Key areas include: Force Field Development : Slipids for lipids, inorganic surfaces (TiO2, ZnO), zeolite interactions Coarse-Grained Modeling : DNA, nucleosome core particles, and multiscale approaches using inverse Monte Carlo Advanced Sampling : Expanded ensemble techniques, metadynamics for free energy calculations Software Development : MDynamix for molecular dynamics, MagiC for coarse-graining The articles reflect his focus on: Modeling G-quadruplex elasticity under variable ionic conditions (2017) Extending Slipids force field to polyunsaturated lipids (2016) Analyzing polyelectrolyte conformational transitions with Monte Carlo methods (2017) Investigating ion-DNA binding specificity through molecular dynamics (2016) Developing transferable potentials for gas adsorption in zeolites (2016) Studying TiO2 nanoparticle wetting mechanisms via ab initio simulations (2016) His methodological contributions include inverse Monte Carlo coarse-graining, systematic force field optimization, and multiscale approaches for chromatin components. The research directly impacts drug design, nanotoxicity modeling, and materials science applications.

Giulia Wally Scuratiمشاهده پروفایل

پژوهشگر ارشد

• Mechanical Engineering
• Product Design
• Sustainable Behavior
• +۴ مورد دیگر

Giulia Wally Scurati is a Postdoctoral Fellow at the Department of Mechanical Engineering, Blekinge Institute of Technology, specializing in using virtual reality (VR) , augmented reality (AR) , and serious games for sustainable behavior change and product development. Her research spans mechanical engineering , human-computer interaction , and environmental psychology . She has a PhD in Mechanical Engineering from Politecnico di Milano, with a thesis on design methods for VR/AR in sustainable behavior. Current projects include AXESS (Assessment in XR Environments for Sustainable Solutions). Key Research Areas: Sustainable behavior, VR/AR applications, product-service systems (PSS), multisensory feedback, gamification, and human-robot interaction. Collaborations: Works with institutions like Politecnico di Milano, Politecnico di Torino, and University of Twente.

Jan Carlsonمشاهده پروفایل

استاد

• Software Engineering
• Embedded Systems
• Model-Based Development
• +۵ مورد دیگر

Jan Carlson is a Professor at Mälardalen University's School of Innovation, Design and Engineering, specializing in the Division of Computer Science and Software Engineering. His primary focus is on software engineering, particularly model-based and component-based development of embedded systems. He earned his M.Sc. from Linköping University (2000) and a doctoral degree from Mälardalen University (2007) with a thesis on event pattern detection for embedded systems. Research interests include allocation optimization, timing analysis, code generation, real-time systems, and safety-critical component reuse. His work often addresses challenges such as cache-aware response time analysis, limited preemption scheduling, and integration of GPU components in embedded systems. He has contributed to projects like SCALL (a component allocator for heterogeneous systems) and Rubus (a GPU-aware component model). His research also explores continuous integration practices in model-based development and decision support systems for component selection. Key contributions include studies on architectural change management, ontology development for systems of systems, and tools for consistency checking between system models and implementations. He has authored over 80 publications, with recent work focusing on performance prediction, resource utilization, and safety-critical system integration. No notable awards are mentioned, though his extensive publication record highlights significant academic impact. Jan Carlson has advised numerous students and research teams but no explicit student names are listed in the provided text. His work is closely tied to industrial collaboration, addressing real-world challenges in automotive, railway, and smart industry domains. He actively participates in workshops and conferences, such as MARCH and ModComp, to advance model-driven engineering practices.

Helena Lindgrenمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-AI Collaboration
• Sociotechnical Systems Design
• +۵ مورد دیگر

Helena Lindgren is a Professor of Computer Science at Umeå University, specializing in Human-Centered AI and Human-AI Collaboration at the intersection of Artificial Intelligence , Interaction Design , and Cognitive Science . Her work focuses on developing intelligent and autonomous systems for healthcare through sociotechnical design principles. Department of Computing Science, Umeå University Founder of Interactive Intelligent Systems research group and Collaborative AI Lab (2010-2011) Co-developer of UMeHealth Lab for AI-based eHealth solutions Her research emphasizes: Digital Coaching for health behavior change Participatory Design of adaptive systems Formal Argumentation in human-AI dialogues Clinical Reasoning and knowledge representation Behavior Change Evaluation in aging populations Contextual Adaptation of AI systems Recent publications highlight socially intelligent agents , norm interpretation , and personalized digital coaching for seniors. She serves as Co-Director of Wallenberg Autonomous Systems, AI and Software Program - Humanities and Society (WASP-HS) and holds leadership roles in AI councils at Umeå University.

Rebekka Wohlrabمشاهده پروفایل

استادیار

• Requirements Engineering
• Software Architecture
• Self-Adaptive Systems
• +۴ مورد دیگر

Rebekka Wohlrab is a tenure-track Assistant Professor in Software Engineering at Chalmers University of Technology (Gothenburg, Sweden) and an adjunct faculty member at Carnegie Mellon University’s Software and Societal Systems Department (S3D). Her research focuses on requirements engineering, self-adaptive systems, and robotics. She leads a research group supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Education: PhD in Computer Science and Engineering (Chalmers, 2020), MSc/BSc in Computer Science (Paderborn University, 2016). Postdoc at Carnegie Mellon University (2020-2022). Research interests include requirements conflicts resolution, quality attribute trade-offs, and ethical monitoring in cyber-physical systems. She has published extensively on adaptive systems, architectural tactics, and agile development practices. Notable contributions include frameworks for robot mission planning, security assurance cases, and traceability management in automotive systems. Recent awards include a pedagogical prize from students at Chalmers. She advises PhD students on topics like self-adaptive robotics and WASP-funded projects. Her work spans theoretical research and industry collaboration (e.g., Volvo Cars). Active in conferences like ICSE, RE, and ICSA as a program committee member. Labs/Teams: Leads the Self-Adaptive Systems Research Group at Chalmers, collaborating with WASP and automotive industry partners.

Matthias Beckerمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Edge Computing
• +۶ مورد دیگر

Matthias Becker is an Associate Professor at the Division of Electronics and Embedded Systems within the EECS School at KTH Royal Institute of Technology. He holds a Docent degree from KTH (2024) and has extensive experience in research, including postdoctoral roles at KTH and visiting research at CISTER in Portugal. His primary affiliations include the Digital Futures research center, focusing on edge computing and real-time systems. Education: B.Eng. in Mechatronics/Automation Systems, University of Applied Sciences Esslingen (2011) M.Sc. in Computer Science (Embedded Computing), University of Applied Sciences Munich (2013) Licentiate and PhD in Computer Science and Engineering, Mälardalen University (2015, 2017) Research Interests: Focuses on real-time systems, embedded computing, edge orchestration, automotive systems, and network-on-chip analysis. His work emphasizes safety-critical systems, scheduling algorithms, and design space exploration for multi-core platforms. Articles Trends: Recent publications emphasize edge computing safety, automotive latency optimization, and preemptive scheduling models. Key themes include resource management, timing predictability, and real-time communication protocols. Awards: Best Student Paper Award at RTAS 2021 Grants & Advising: Co-PI of the GPARSE project on edge safety. Supervised over 15 master’s theses on real-time scheduling and embedded systems. Labs/Teams: Active in Digital Futures and collaborates with industry partners on automotive and edge system projects.

• System Resilience
• Digital Twin
• Model-Driven Engineering
• +۴ مورد دیگر

Samuele Giussani is a researcher affiliated with the Faculty of Technology at Linnaeus University , working within the Department of Computer Science and Media Technology . He actively contributes to the Engineering Resilient Systems (EReS) and Smart Industry Group (SIG) research labs, focusing on system resilience and interdisciplinary applications of computer science and mechanical engineering. Giussani’s research centers on Digital Twin of the Organization (DTO) and Adaptable Architectures for Organizations' Digital Twins (Aladino) . These projects aim to develop live organizational models and engineering methodologies for modeling, evaluating, and optimizing systems through digital twins and model-driven engineering. His work spans IoT architectures for energy efficiency, self-adaptive robotic manipulators, and visualization techniques for time series data in simulations. His recent publications (2022–2025) highlight advancements in digital twin technologies , self-adaptive systems , and model-driven engineering . These studies emphasize organizational modeling, business process optimization, explainable AI, and energy-efficient IoT solutions. Giussani teaches courses in Operating Systems , Algorithms and Advanced Data Structures , and Software Engineering , while participating in interdisciplinary collaborations between computer science and mechanical engineering. Giussani’s research groups ( EReS and SIG ) integrate theoretical and practical approaches to enhance system resilience and smart industrial production. His contributions reflect a focus on bridging software engineering with real-world applications in robotics, IoT, and sustainable organizational design.

Niina Hernándezمشاهده پروفایل

مدرس

• Textile Engineering
• Garment Fit
• Virtual Fitting
• +۳ مورد دیگر

Niina Hernández is a University Lecturer at the Department of Textile Technology within the Academy of Textiles, Technology and Economics at the University of Borås. She has been affiliated with the university since 2011, initially serving as a training leader and teacher for Design Techniques while completing her doctoral studies. Education: Textile teacher's degree Licentiate in fit for deviant figures from the University of Gothenburg (Thesis: "Tailoring the Unique Figure") Doctoral thesis: "Does it really fit?" Research Interests: Hernández specializes in the mathematical, virtual, and physical aspects of garment fit. Her work spans systems for product development and production, software for custom-made clothing manufacturing, and virtual fit evaluation methodologies. Core research areas include Textile Engineering, Garment Fit, Virtual Fitting, Custom Clothing Production, Smart Textiles, and Wearable Technology, with emphasis on bridging theoretical models with practical applications. Publication Trends: Her 15 most recent publications (2014-2023) demonstrate concentrated expertise in garment fit prediction, smart textile sensors, and wearable health monitoring systems. Key themes include deep learning for textile degradation analysis, virtual sizing solutions, sensor integration for neurological disorders, and sustainable fashion technologies. The work consistently bridges textile engineering with computer science and healthcare applications, showing progression from theoretical fit modeling to real-world implementations in neurorehabilitation and UN collaborations. Research Projects and Funding: wearITmed: Funded by the Swedish Foundation for Strategic Research (SSF) for neurorehabilitation garments Digimode: Vinnova-funded project extending EC-supported "from Roll to Bag" circular fashion initiative SIIR: Industry collaboration projects for textile companies UN Collaboration (2024-2025): Development of guide clothing for UN headquarters in New York with design students Teaching and Advising: Hernández has instructed Design Techniques since 2011 and mentored students in high-impact projects like the UN guide clothing collection. Her teaching integrates industry experience from CAD/CAM systems development and women's apparel production. Laboratory and Team Involvement: She is a core member of the WearITmed Consortium developing sensor-based garments for neurological rehabilitation. Her work with SIIR facilitates industry-academia knowledge transfer, while the UN project demonstrates applied design leadership in international contexts.

• Air Quality Analysis
• Microdata Analysis
• Urban Pollution Modeling
• +۶ مورد دیگر

Yves Rybarczyk serves as Professor of Microdata Analysis at Dalarna University's Department of Data and Information Management. His academic profile spans computer science, environmental science, and healthcare technology applications, reflecting a highly interdisciplinary research approach. Currently teaching User-centered Design (GIK348), Data Warehousing (AIK232), and Bachelor's thesis in Microdata Analysis (GMI2C8), he maintains active engagement in both undergraduate education and research supervision. Professor Rybarczyk's research focuses primarily on air quality analysis, urban pollution modeling, and machine learning applications for environmental monitoring. His work demonstrates particular expertise in analyzing PM2.5 effects on urban environments, biosphere-atmosphere interactions, and the application of digital twin technology in home care settings. His research methodology frequently employs causal analysis, convergent cross mapping, and ensemble deep learning approaches to address complex environmental challenges. Analysis of his recent publication record reveals a strong trend toward interdisciplinary environmental data science, with approximately 60% of his work focusing on air quality and pollution modeling, 25% on healthcare technology applications including tele-rehabilitation systems, and 15% on broader data science applications. His publications appear in high-impact journals across multiple disciplines including environmental science, computer science, and public health, demonstrating his ability to bridge academic domains through data-driven approaches. While specific grant information isn't detailed in the available text, Professor Rybarczyk's collaborative publication pattern suggests involvement in international research projects, particularly with colleagues from South American institutions as evidenced by multiple studies focused on Quito, Ecuador. His research appears to receive support for environmental monitoring initiatives and digital health technology development.

• Strategic Management
• Innovation
• Open Innovation
• +۵ مورد دیگر

Martin Wallin is a Professor of Innovation Management at Chalmers University of Technology and Associate Professor (PD) of Management at ETH Zurich. He serves as Head of Division at Entrepreneurship and Strategy and holds a Fellow position at the Institute for Management of Innovation and Technology (IMIT). His academic career includes visiting positions at Stanford University and the University of Luxembourg. PhD in Technology Management (Chalmers University) MSc in Industrial Engineering and Management (Chalmers University) Dr. habil in Management (ETH Zurich) His research focuses on strategic management and innovation , particularly open innovation , digitalization , and their implications for business models. He has advised industrial firms and professional service organizations across Scandinavia, Continental Europe, and the UK. Notably, his 2012 paper on open source software motivation was nominated as European Research Paper of the Year by CIOnet. Recent publications highlight trends in AI-driven organizational distrust , internal crowdsourcing , and energy-efficient blockchain technologies . His work spans journal articles , book chapters , and conference proceedings . Scientific contributions include: 2024: Closing Open Innovation (Strategic Management Review) 2023: AI Implementation Challenges (California Management Review) 2022: ICT Industry Convergence Theories (IEEE Transactions) 2018: Business Model Innovation Framework (Long Range Planning) 2007: Spin-off Literature Bibliometrics Martin Wallin actively participates in startup acceleration programs as co-organizer of Born Global and Scale Global . His affiliations include the Academy of Management and the Strategic Management Society .

• Traffic Safety
• Driver Behavior
• Automated Vehicles
• +۴ مورد دیگر

Jonas Bärgman is a Professor at Chalmers University of Technology, leading the Safety Evaluation research group within the Division of Vehicle Safety at the Mechanics and Maritime Department . His work focuses on understanding traffic safety through interdisciplinary research involving driver behavior, vehicle automation, and environmental factors in pre-crash scenarios. Key research themes include: Quantifying driver comfort zone boundaries using naturalistic driving data Developing counterfactual simulation methods for safety impact assessment Analyzing crash causation mechanisms and driver response modeling Integrating human factors into automated vehicle development Recent publications demonstrate expertise in: Automated emergency braking algorithms Lane change dynamics and timing Crash scenario generation across severity ranges Bayesian modeling of driver behavior Validation of human benchmark models for AV approval He actively contributes to: Teaching Vehicle and Traffic Safety (TME202 masters course Lecturing on Active Safety systems International standardization through ISO participation Collaborative research projects like SENSI , V4SAFETY , and SHAPE-IT

• Machine Learning
• Data Science
• Digital Twin Technology
• +۳ مورد دیگر

Manoranjan Kumar is a researcher at Linnaeus University, affiliated with the Industrial Research School Data Intensive Applications (DIA) and the Data Intensive Software Technologies and Applications (DISTA) research group. His work focuses on data-driven methods like machine learning and artificial intelligence, particularly applied to industrial challenges at Volvo CE through digital twin development projects. Key Research Areas : Machine learning, digital twins, fatigue analysis, predictive maintenance, industrial automation. Recent Publications : 4 peer-reviewed works (2023-2025) spanning fatigue analysis in welded joints, wheel loader automation, driving pattern classification, and EGR clogging prediction. These highlight interdisciplinary applications across construction, automotive, and transportation sectors. Collaborations : Actively works with Volvo CE on industrial digital twin platforms, emphasizing customer problem-solving and operational optimization. Affiliated with the Linnaeus University Center for Data Intensive Sciences and Applications (DISA).

• Open Source Software
• Software Engineering
• Embedded Systems
• +۵ مورد دیگر

Henrik Gustavsson is a Senior Lecturer at the University of Skövde within the School of Informatics . He actively contributes to research in software engineering, open source ecosystems, and sustainable digitalization, and teaches courses at both bachelor's and master's levels. Contact details include email henrik.gustavsson@his.se and phone +46 500-448323. Research Projects: 2019 study on GitHub-based assessment in software education, 2005-2007 work on XMI model interchange between proprietary and open-source tools, and the 2021-2023 OSSD project developing a master's program for sustainable digitalization through open systems. Conference Participation: Active since 2005 at venues like OpenSym, ICSM, SIGRAD, and EMMSAD. Collaborations: Frequent co-author with researchers like Björn Lundell, Brian Lings, and Anna Persson across software maintenance, open source adoption, and embedded systems modeling. Publications Trend: Focus on open source integration, model-driven development, and educational innovation in software engineering. Subfields highlight XMI standardization, GitHub data utilization, and toolchain interoperability challenges. Teaching Activities: Coordinates multiple 7.5-credit courses and delivers lectures on software engineering fundamentals, open systems, and digital transformation.

• Business Intelligence
• Data Analytics
• Data-Driven Organizations
• +۳ مورد دیگر

Mikael Berndtsson is a Senior Lecturer in Computer Science at the School of Informatics, University of Skövde , Sweden. He teaches courses in Business Intelligence, systems science, and data-driven organizational development, including a distance learning course on data-driven culture maturity analysis. Academic Rank: Senior Lecturer University: University of Skövde School: School of Informatics Department: Department of Information Technology Research Interests focus on organizational adoption of advanced data analytics, scalability of data-driven initiatives, and cultural transformation toward data-centric decision-making. His work bridges technical systems with behavioral change, emphasizing frameworks for assessing analytics maturity and practical implementation strategies. Scientific Contributions span 15+ years of publications in Communications of the ACM , International Journal of Business Intelligence Research , and Encyclopedia of Database Systems . Key themes include: Scaling analytics beyond pilot projects Cultural and organizational barriers to data adoption Active database systems and complex event processing Visual tools for data-driven transformation Formal verification in event-based systems Teaching methodologies for BI and IT projects Editorial Roles include Editor-in-Chief of the International Journal of Artificial Intelligence in Business and Management and Associate Editor for the International Journal of Business Intelligence Research . He has also collaborated with researchers like Thomas Svahn and AnnMarie Ericsson on practical tools for data maturity assessment.

Emma Söderbergمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Human-Computer Interaction
• Programming Tools
• +۴ مورد دیگر

Emma Söderberg is a Senior Lecturer in the Department of Computer Science at Lund University's Faculty of Engineering with key roles as Project Manager for ELLIIT (Linköping-Lund IT initiative), Coordinator of the LTH AI and Digitalization Profile Area, and Researcher in the NEXTG2COM competence centre. She earned her academic credentials from Lund University: M.Sc. (2007), Licentiate (2011), and Ph.D. (2013) in Computer Science and Engineering, with doctoral research on semantic editors using reference attribute grammars. After a Google tenure (2013-2018) developing Chromium infrastructure, she returned to Lund to pioneer human-centered software engineering research. Her work focuses on the intersection of programming tools, software engineering, and human-computer interaction, specifically how tools can support human needs in development workflows. Recent investigations examine cognitive aspects of code review, AI augmentation (not replacement) of developer judgment, and gamification for tool feedback collection. Analysis of her 2025 publications reveals a cohesive research trajectory centered on developer behavior modeling—spanning cognitive code review frameworks, AI-supported quality assurance, educational program analysis applications, and gamified static analysis feedback systems—all emphasizing human-tool symbiosis over full automation. She actively supervises four PhD candidates: N. Korkakakis on continuous automotive software engineering (CASE-SDV) N. Hagatulah on safeguards for LLM-assisted refactoring (REFORGE) P. Palesetti on AI-driven DevOps for cyber-physical systems A. Bexell on semi-automatic code improvement (SACI) Her leadership extends to organizing NEXTG2COM workshops, delivering industry outreach talks (e.g., 'This thing called programming & AI and programming'), and contributing to UN Sustainable Development Goals through software engineering advancements. Current projects funded through 2030 address critical challenges in automotive software and AI-enhanced development environments.