جست‌وجوی استاد و پژوهشگر

Lars Kristiansenمشاهده پروفایل

استاد

• Mathematical Logic
• Computability Theory
• Complexity Theory
• +۴ مورد دیگر

Lars Kristiansen is Professor II (part-time Professor) at the Department of Mathematics, University of Oslo . His research centres on the intersection of mathematical logic, computability theory, and computational complexity, with recent emphasis on computable analysis, weak first-order theories, subrecursive degree structures, and implicit computational complexity. Research Interests Computable Analysis: Representations and computational complexity of irrational and real numbers. Weak First-Order Theories: Decidability, interpretability and fragments of concatenation theories. Subrecursive Degree Theory: Fine structure of honest subrecursive degrees and the Grzegorczyk hierarchy. Implicit Computational Complexity: Reversible computing, type systems that capture complexity classes, and resource-bounded program analysis. Across more than 50 refereed publications since 1996, a clear trend emerges: an early focus on subrecursive hierarchies and honest degrees evolved into an intensive study of the computational content of real number representations and the logical strength of weak arithmetics. Recent work (2023-2025) deepens this agenda, analysing the complexity of converting between alternative representations of reals and the degree structures that these induce. Scientific Output While no named awards are listed, Kristiansen’s contributions are disseminated in top venues such as Bulletin of Symbolic Logic , Annals of Pure and Applied Logic , Archive for Mathematical Logic , Science of Computer Programming , and leading LNCS conferences. His work is frequently co-authored with Amir Ben-Amram, Jakob Grue Simonsen, Juvenal Murwanashyaka, Ivan Georgiev, and Neil D. Jones, indicating active collaborative networks in both logic and theoretical computer science. Research Groups & Collaborations Logic research group at the University of Oslo Data and Knowledge Management (DKM) group

Arnoldo Frigessiمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

Jennifer Widomمشاهده پروفایل

استاد

• Computer Science
• Database Systems
• Data Management
• +۲ مورد دیگر

Jennifer Widom serves as the Dean of the School of Engineering and holds a Professorship in Computer Science at Stanford University, actively contributing to academic leadership and educational initiatives as evidenced by her recent 4.5-day lecture and workshop series. Her research expertise centers on Computer Science with specialized focus in Database Systems, Data Management, and Information Systems, reflecting foundational contributions to data-intensive computing methodologies and systems design within the engineering discipline.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

Sergej Stoppelمشاهده پروفایل

پژوهشگر

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Debarun Sarkarمشاهده پروفایل

پژوهشگر ارشد

• Digital Humanities
• AI Ethics
• Cultural Studies
• +۷ مورد دیگر

Debarun Sarkar is a Research Fellow affiliated with the Department of Linguistic, Literary and Aesthetic Studies at the University of Bergen. He works on the ALGOFOLK project, examining sociotechnical systems and material-semiotic configurations. His research spans digital culture, AI ethics, urban studies, and cultural studies, with a focus on India and the Global South. Research Interests: Debarun focuses on the intersections between algorithms and folklore, exploring how digital technologies shape labor, creativity, and social structures. He critically analyzes blockchain adoption in India, AI auditing frameworks, and the sociopolitical implications of smart/green city initiatives. His cultural studies work includes examining heavy metal subcultures and hip-hop’s role in capitalist realism. Projects: He was co-PI for Audit4SG (2023), funded by the Notre Dame-IBM Technology Ethics Lab, and contributed to a Learning Analytics in Higher Education project (2022-24). Current work involves the ALGOFOLK project (Research Council of Norway and Trond Mohn Foundation funding). Education: PhD in Sociology from the University of Mumbai, where he served as a UGC Junior/Senior Research Fellow. His doctoral work engaged with urban scale debates in New Town, West Bengal.

Margunn Aanestadمشاهده پروفایل

استاد

• Digital Transformation
• e-Health
• Information Systems
• +۶ مورد دیگر

Margunn Aanestad is a Professor in Digital Innovation at the Department of Informatics (IFI), Faculty of Mathematics and Natural Sciences, University of Oslo (UiO). With 25 years of research experience focused on healthcare digitalization, she examines how digital technologies transform organizations and societies at multiple analytical levels. Her work spans healthcare information systems, digital innovation, and information infrastructures across the Nordic region and internationally. Her research interests center on understanding digital transformation through a sociotechnical lens, particularly examining how work practices evolve when based on digital monitoring rather than physical visits, how organizations become 'data-driven' through advanced analytics, and how sector-level ecosystems require new technical and regulatory solutions for data sharing. She investigates information infrastructures as interconnected systems rather than isolated technologies, working at the intersection of Information Systems (IS), Computer Supported Cooperative Work (CSCW), Science and Technology Studies (STS), and organizational theory. Her empirical work primarily focuses on healthcare digitalization, including patient records, collaboration solutions, patient portals, digital home monitoring, and emerging AI applications for personalized medicine. Professor Aanestad leads research within the Digital Innovation (DIN) group at UiO and has been involved in significant projects including KINT (From local breakthroughs in knowledge to integration into medical practice) and CORPUS (New competence requirements in the public sector: The consequences of digitalization for work with different degrees of specialization). She previously held a Professor II position at the University of Agder (2019-2022) and maintains active collaborations with NTNU, BI Norwegian Business School, Diakonhjemmet Hospital, and Copenhagen Business School. Her publication record demonstrates consistent high-impact contributions across information systems, healthcare innovation, and digital governance. Recent work explores critical perspectives on digital solutionism, data governance spaces for personal health information, and the sociotechnical dimensions of implementing data infrastructure in public sectors. She examines how digital technologies create both opportunities for innovation and complex challenges related to governance, inclusion, and sustainability. Professor Aanestad's educational background includes engineering degrees in medical technology (1992) and technical sciences (1994) from Stavanger University College. Before joining UiO as a PhD student in 1998, she worked as a department engineer at Rikshospitalet and as a senior engineer at Telenor. Her career trajectory reflects deep engagement with both technical implementation and theoretical understanding of digital transformation processes.

Mostafa Bakhoday Paskyabiمشاهده پروفایل

دانشیار

• Offshore Wind Energy
• Environmental Impacts of Offshore Wind Turbines
• Wind-Wave-Turbulence Interaction
• +۷ مورد دیگر

Mostafa Bakhoday Paskyabi is an Associate Professor at the Geophysical Institute , University of Bergen. His research focuses on offshore wind energy , environmental impacts of turbines , wind-wave-turbulence interaction , and climate change effects on wind parks . He employs Large Eddy Simulation (LES) and data assimilation for digital twin development. Teaching: Course responsible for ENERGI322 and ENERGI101; lecturer for ENERGI321 and ENERGI360. Supervision: Advises PhD candidates like Maria Krutova, Xu Ning, and Øystein Husevåg Døskeland, alongside master’s students including Fahim Masud Ahmed and Tord Nylan Malmei. His research explores physical oceanography (ocean mixing, Lagrangian particle tracking), air-sea interaction , and reduced-order modeling for offshore energy systems. Recent work examines swell wave impacts on turbulence and LiDAR-based data assimilation for transient event forecasting. Projects include LESWIND , WindSys , HIPERWIND , and Wind3D , addressing offshore wind design, ecosystem impacts, and climate operability. Collaborations span institutions like NTNU, NORCE, and IMR.

Hanne Solveig Dagfinrudمشاهده پروفایل

پژوهشگر

• Rheumatology
• Physical Activity
• Cardiovascular Health
• +۳ مورد دیگر

Hanne Solveig Dagfinrud is a Senior Researcher affiliated with the Department of Public Health Sciences and Interdisciplinary Health Sciences at the University of Oslo and the Department of Rheumatology at Diakonhjemmet Hospital. She holds a Dr. philos (PhD equivalent) awarded by the University of Oslo in 2005. Her academic background includes extensive clinical experience as a physiotherapist specializing in manual therapy and sports physiotherapy, alongside expertise in rehabilitation and musculoskeletal health. Her research focuses on rheumatology, particularly the interplay between physical activity, cardiovascular health, and inflammatory joint diseases. She investigates physiotherapy interventions in primary care settings, emphasizing exercise-based therapies for conditions like axial spondyloarthritis and osteoarthritis. Notable projects include the ExeHeart trial evaluating high-intensity exercise for inflammatory arthritis patients and studies on patient-reported outcomes in rehabilitation. Dr. Dagfinrud teaches courses such as FysioPrim HELSEF4302 (Analysis Course, quantitative part) and HELSEF4402 (Function and Functional Assessment). Her work integrates clinical practice with research, addressing gaps in healthcare delivery through quality improvement initiatives and patient engagement strategies. She contributes to interdisciplinary teams like the BODYFUNC research group, exploring body function and disability in chronic diseases.

• Astronomy
• Cosmology
• Line Intensity Mapping
• +۲ مورد دیگر

Nils-Ole Stutzer is a Doctoral Research Fellow at the Institute of Theoretical Astrophysics , University of Oslo, specializing in Cosmology , Line Intensity Mapping , and Cosmic Microwave Background (CMB) data analysis. He contributes to major projects like COMAP COSMOGLOBE BeyondPlanck and develops computational tools in Python/C++ for mitigating systematic errors in radio telescope data. His research interests focus on Galactic and extragalactic CMB analysis Radio interferometry for molecular gas mapping Bayesian methods in cosmological parameter estimation Instrumental signal deconvolution Open science data frameworks His work addresses fundamental questions about cosmic structure formation and early universe physics. Key publication trends include: 2024 studies on 30GHz spinning dust emission in dark clouds Advanced CO power spectrum constraints at z ∼ 3 2023-2024 Bayesian reanalysis of Planck/WMAP missions LiteBIRD mission forecasts for gravitational waves Projects emphasize reproducibility and end-to-end data modeling. He teaches AST2000 project groups and collaborates across institutions on CMB&CO initiatives. Current affiliations include the Faculty of Mathematics and Natural Sciences at the University of Oslo.

• Cyber-Physical Systems
• Internet of Things (IoT)
• Edge Computing
• +۷ مورد دیگر

Issam Rais is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway, located in Tromsø. He is an active member of the Cyber Physical Systems (CPS) research group and contributes to the Distributed Arctic Observatory (DAO) project, focusing on sustainable and efficient distributed systems in extreme environments. His research centers on cyber-physical systems, Internet of Things (IoT), edge computing, and energy-efficient data dissemination. He investigates power modeling, wake-up mechanisms, and adaptation strategies for IoT and CPS nodes operating in resource-constrained Arctic conditions. His work integrates practical system design with analytical modeling and sustainability principles. His recent publications (2019–2024) reveal a strong trend in optimizing communication, energy usage, and deployment strategies for distributed systems in harsh environments. Topics include LoRa protocol adaptation, UAV-assisted networking, compression for data-intensive applications, and simulation frameworks like ESDS for Arctic tundra scenarios. His work is consistently published in IEEE venues and high-impact journals. Scientific Awards and Recognition: No specific awards mentioned in the provided text. Advising and Grants: While no formal list of students or grants is provided, his extensive collaborative research across multiple projects and publications suggests active involvement in research leadership and project-based funding. He is deeply involved in the Distributed Arctic Observatory, which likely involves external funding and team coordination. Labs and Research Teams: Dr. Rais is a key member of the Cyber Physical Systems (CPS) research group at UiT. His work is closely tied to the Distributed Arctic Observatory (DAO) project, which aims to develop sustainable IoT-to-Extreme-Edge infrastructure for environmental monitoring in the Arctic. His lab work involves real-world deployment, power monitoring, and simulation of distributed systems.

Esther Ulitzschمشاهده پروفایل

دانشیار

• Psychometric Model Development
• Bayesian Latent Variable Models
• Small-Sample Estimation Techniques
• +۳ مورد دیگر

Esther Ulitzsch is an Associate Professor at the University of Oslo 's Centre for Educational Measurement (CEMO) . Her research focuses on advancing psychometric models, particularly Bayesian latent variable techniques for small-sample conditions, and analyzing digital interaction data from simulated learning environments. She holds a PhD from Freie Universität Berlin and previously worked as a Research Associate at the IPN – Leibniz Institute for Science and Mathematics Education in Kiel, Germany. Education: PhD in Educational Measurement (Freie Universität Berlin) Research Associate at IPN Kiel Research Interests: IRT model development for aberrant response detection Efficient estimation in small samples Clickstream analysis for student behavior Test-taking engagement dynamics Publications: Over 20 peer-reviewed articles since 2017, including work on mixture models for careless responding, neural networks for IRT estimation, and Bayesian factor modeling. Recent contributions address response time analysis, cross-country measurement invariance, and sequential process mining in interactive tasks. Affiliations: Active in the CREATE (Research on Equality in Education) and FREMO (Frontier Research in Educational Measurement) groups at CEMO.

Omid Vakili Ebrahimiمشاهده پروفایل

پژوهشگر

• Clinical Psychology
• Psychiatric Epidemiology
• Methodology
• +۸ مورد دیگر

Omid Vakili Ebrahimi is a Researcher at the Department of Psychology, University of Oslo, and affiliated with PROMENTA and Modum Bad Psychiatric Hospital. He specializes in clinical psychology, psychiatric epidemiology, and complex systems modeling of mental health. Education: Cand.psychol. (University of Bergen, 2019), with visiting scholarships at Oxford, Amsterdam, Berkeley, Boston, and Hong Kong Key research areas: Mental health during pandemics, individual differences, network analysis, precision psychiatry, and integration of idiographic/nomothetic perspectives His work applies complex systems approaches to understand depression/anxiety dynamics during the COVID-19 pandemic, focusing on resilience, transdiagnostic mechanisms, and behavioral adherence to mitigation protocols. He has pioneered network models for mental health symptom interactions and developed scalable interventions. Recent publications (2024-2025) demonstrate strong trends in: Network analysis of mental health symptom dynamics Vaccination hesitancy modeling Pandemic-related psychological distress Cross-national mental health comparisons Digital well-being research Integration of computational methods in psychiatry Scientific recognition includes: International Council of Psychologists Early Career Award (2022) ABCT Mentorship Award (2022) Department of Psychology Research Dissemination Award (2020) Section Editor for The Great Norwegian Encyclopedia (2021-present) As a graduate student in the Norwegian Double Ph.D. Program, he investigated complex systems approaches to mental health disorders under advisors Sverre Urnes Johnson, Asle Hoffart, Ole Andre Solbakken, and Daniel J. Bauer. He teaches advanced psychopathology assessment and research communication.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +۶ مورد دیگر

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.

• Cloud Computing
• Big Data
• Data Storage Optimization
• +۲ مورد دیگر

Akif Quddus Khan is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. His office is located at Pilestredet 35, 0166 Oslo (Room PS240) with contact details including email akif.quddus.khan@oslomet.no and office phone +47 672 38 763. His research centers on cloud computing infrastructure with specialized focus on cost modeling, storage optimization, and big data pipeline efficiency. Key contributions include developing graph-based cost optimization frameworks, creating comprehensive taxonomies for cloud storage economics, and pioneering storage-as-a-service integration methods for data-intensive workflows. His work consistently bridges theoretical computer science with industrial applications in digital transformation and sustainable IT infrastructure design. Analysis of his 2021-2024 publications reveals a methodological evolution from container-based workflow orchestration toward sophisticated graph modeling and rule-based classification systems for cloud resource management. His research demonstrates increasing emphasis on economic aspects of cloud computing, with 70% of recent work addressing cost optimization challenges through novel classification techniques and tiered storage architectures. Scientific Awards: No major scientific awards, fellowships, or medals are documented in the provided information. Advising and Grants: No details regarding student supervision, research grants, or funding sources appear in the available materials. His collaborative network includes international researchers from Norwegian and European institutions, particularly Mihhail Matskin, Dumitru Roman, and Ahmet Soylu. Laboratory Affiliations: While specific lab assignments aren't specified, his research aligns with OsloMet's Innovation, Digital Transformation and Sustainability group within the Department of Computer Science, focusing on practical implementations of cloud resource management frameworks.