جست‌وجوی استاد و پژوهشگر

• Mathematics Education
• Teacher Professional Development
• Lesson Study
• +۳ مورد دیگر

Liping Ding is an Associate Professor in the Department of Teacher Education at the Norwegian University of Science and Technology (NTNU), with research focusing on mathematics education and teacher professional development. Her work bridges educational practices between Western contexts and Shanghai's renowned mathematics education system. Dr. Ding's research interests center on teacher professional development through lesson study, inquiry-based learning approaches in mathematics education, and the application of variation theory in classroom instruction. Her work examines how teachers develop expertise through collaborative learning models, with particular attention to critical thinking development and emotional dimensions of mathematics learning. She investigates the roles of facilitators in professional learning contexts and comparative differences in teacher collaboration across cultural settings. Her publication record from 2009-2025 demonstrates consistent scholarly activity with a trajectory showing increasing international collaboration. Her work spans multiple publication types including journal articles, book chapters, and conference proceedings, with a clear focus on practical applications for mathematics teaching. Recent publications emphasize the integration of critical thinking frameworks with inquiry-based approaches and the emotional aspects of mathematics learning. Dr. Ding has established significant research partnerships with scholars from multiple countries, including Svein Arne Sikko (Norway), Keith Jones (UK), Charlotte Krog Skott (Denmark), and various Chinese researchers, reflecting her comparative research orientation. Her work frequently appears in proceedings from the European Society for Research in Mathematics Education and other prominent mathematics education venues. Her research demonstrates a commitment to improving mathematics teaching practices through evidence-based approaches that draw on both Western and Eastern educational traditions, with particular emphasis on how teachers learn from and with each other in professional settings.

Karina Bakkeløkken Hjelmervikمشاهده پروفایل

استاد

• Fluid mechanics
• Physical oceanography
• Numerical modelling of water currents and waves
• +۵ مورد دیگر

Professor Karina Bakkeløkken Hjelmervik is a Professor in the Department of Maritime Operations , Faculty of Technology, Natural Sciences and Maritime Studies at the University of South-Eastern Norway , Campus Vestfold. Since 2009 she has combined mathematics and oceanography to advance numerical modelling of coastal waters, in particular the Oslofjord system. Education: PhD in Fluid Mechanics, University of Oslo, 2009 – thesis on wave–current interactions in coastal tidal currents. Cand. Scient. in Oceanography, University of Bergen – thesis on cold CO₂ droplets in seawater. Cand. Mag. in Science, University of Bergen. Research interests revolve around fluid mechanics and physical oceanography . She develops and validates high-resolution numerical models that simulate water currents, tides, waves, and dispersion processes. Her work supports operational ocean forecasting, tidal-energy assessment, oil-spill contingency planning, and maritime training simulators. Project leadership spans more than 15 funded initiatives totalling over 30 MNOK. Flagship projects include the FjordOs series (2013–2021) that produced an operational forecast model for the Oslofjord, and collaborative grants on tidal power, clean propulsion, and micro-technology for pathogen detection. Publications and dissemination include 20+ peer-reviewed journal articles since 2012, keynote talks at IEEE OCEANS and JONSMOD, and popular lectures such as Mathematics in the Oslofjord . Student supervision is extensive: she mentors master’s and PhD candidates across mathematics, physics, and maritime programmes, though specific student names are not listed. Contact: karina.hjelmervik@usn.no | +47 31 00 93 25

• Robotics
• Intelligent Systems
• Reconfigurable Computing
• +۳ مورد دیگر

Alexander Wold is an Associate Professor at the University of Oslo, affiliated with the Research Group for Robotics and Intelligent Systems within the Faculty of Mathematics and Natural Sciences. His work focuses on reconfigurable computing, embedded systems, and robotics, with notable contributions to FPGA design, real-time systems, and educational technology. He holds a position at the Institute of Informatics (IFI) and can be contacted at alexawo@ifi.uio.no . Research interests include optimizing hardware-software co-design, thermal management in 3D-IC systems, and developing open-source tools like EasyPR for pattern recognition. His publications span topics such as remote cloud labs for reconfigurable logic education, network traffic management in industrial Ethernet, and constraint programming for module placement in FPGAs. Dr. Wold’s articles reflect a strong emphasis on practical applications of robotics and intelligent systems, with a focus on safety-critical industrial systems and autonomic computing. He has contributed to multi-core system design, thermal-aware FPGA architectures, and self-aware systems.

Johan Sokrates Windمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Neural Networks
• Overparameterized Machine Learning
• +۳ مورد دیگر

Johan Sokrates Wind is a Research Fellow at the University of Oslo's Department of Mathematics, specializing in Differential Equations and Computational Mathematics. His primary affiliation is with the Faculty of Mathematics and Natural Sciences. He holds a Master's in Industrial Mathematics from the Norwegian University of Science and Technology (NTNU) and began his PhD in August 2021. Wind's research focuses on deep learning, neural networks, and overparameterized machine learning systems. He has explored topics such as the Neural Tangent Kernel, Deep Linear Networks, and implicit biases in optimization algorithms. His work bridges theoretical analysis with practical implementations, as evidenced by his blog The Good Minima , where he publishes technical insights on neural network behavior and training dynamics. Notably, he has contributed to projects like real-time visual odometry on smartphones during his part-time role at Arm Ltd. Wind is an active participant in competitive programming and Kaggle competitions, showcasing his problem-solving skills and algorithmic expertise. His research emphasizes analytically tractable models and the mathematical foundations of modern AI systems. Recent investigations include the RWKV language model architecture, efficient CIFAR-10 classification, and the role of initialization and learning rates in SGD's implicit bias. Wind’s publications highlight interdisciplinary approaches, combining elements of optimization theory, computational mathematics, and applied machine learning. He maintains an active blog with detailed technical posts, demonstrating a commitment to open science and knowledge-sharing. His academic journey reflects a balance between theoretical rigor and practical innovation, positioning him as a rising researcher in computational and mathematical aspects of deep learning.

• Optimization
• Chemical Engineering
• Machine Learning
• +۳ مورد دیگر

Bjarne André Grimstad is an Associate Professor at the Norwegian University of Science and Technology (NTNU). His work focuses on optimization, surrogate modeling, and data-driven approaches for complex systems in chemical and petroleum engineering. Key contributions include advancements in B-spline models, hybrid gray-box systems, and neural network applications for virtual flow metering. Research interests span optimization algorithms, machine learning integration with physical models, and industrial process control. Notable publications address multi-task learning frameworks, identifiability in hybrid models, and the efficacy of gray-box modeling in nonstationary environments. Publications (2024–2015) highlight interdisciplinary work combining mathematical programming with real-world applications in oil & gas production and flow measurement. His 2015 doctoral dissertation explored daily production optimization using surrogate modeling techniques. Outreach activities include academic lectures at international conferences (e.g., IFAC OOGP 2015) and poster presentations on hybrid modeling at Geilo Winter School 2021. Collaborations with industry partners like SINTEF and TU Delft underscore his applied research focus.

Niclas Larsonمشاهده پروفایل

دانشیار

• Mathematics Education
• Teacher Education
• Algebra
• +۳ مورد دیگر

Niclas Larson is an Associate Professor at the University of Agder (UiA), affiliated with the Faculty of Engineering and Sciences and the Department of Mathematical Sciences. His academic journey includes teaching positions in Swedish secondary schools, doctoral studies at Linköping University, and faculty roles at Stockholm University. Larson specializes in mathematics education, teacher training, and computer-aided assessment, with a focus on linear equations and algebraic competence. Education: Bachelor's and Licentiate in Mathematics (Stockholm University), PhD in Mathematics Education (Linköping University) Current Roles: Teaches mathematics and mathematics education at UiA; supervises master’s and PhD students Research: Cross-cultural analysis of pre-service teachers' equation-solving explanations; digital assessment systems; algebra in compulsory education Podcast: Runs "Spøkelser etter avdøde størrelser" (Ghosts of Departed Mathematicians), connecting historical mathematical concepts with modern pedagogy Larson's research examines how pre-service teachers explain mathematical solutions, particularly linear equations, across Norway and Sweden. He explores transitions from high-stakes exams to continuous assessment models using digital tools like STACK-tests. His work bridges theoretical frameworks with practical implementation, focusing on pedagogical innovation and comparative judgement in educational settings. Scientific publications demonstrate expertise in mathematics education, teacher development, and digital assessment tools. Recent articles analyze cross-national differences in algebraic reasoning, cognitive aspects of mathematical memory, and ethical challenges in academic writing. Larson contributes to curriculum development through textbooks like "Matematik Origo" and participates in academic committees, including the Research Education Committee for the PhD Programme in Engineering and Science at UiA. His podcast initiatives and collaborative projects highlight his commitment to public engagement and interdisciplinary approaches in mathematics education.

Ben Zwicklمشاهده پروفایل

دانشیار

• Physics Education Research
• Quantum Workforce Development
• Laboratory Education
• +۲ مورد دیگر

Ben Zwickl is an Associate Professor at the University of Oslo's Department of Physics and Rochester Institute of Technology. A Fulbright Fellow and NSF CAREER awardee, he researches physics education, quantum workforce development, and computational literacy. He holds a PhD from Yale University and completed postdoctoral work at University of Colorado Boulder. Zwickl examines expertise development, laboratory education, and career pathways in STEM. Recent projects focus on quantum information science education and equitable graduate admissions. Publications analyze problem-solving in research contexts, remote learning during COVID-19, and authentic engineering practices. He co-directs Rochester Institute of Technology's Center for Advancing Scholarship to Transform Learning. U.S.-Norway Fulbright Fellowship NSF CAREER Award Distinguished Teaching Award (University Level) ConocoPhillips Faculty Award Zwickl teaches Advanced Quantum Mechanics and Statistical Mechanics, integrating computational methods. He leads initiatives preparing students for quantum technology careers.

Helmer Aslaksenمشاهده پروفایل

دانشیار

• Mathematics Education
• Cultural Astronomy
• Calendars and Timekeeping
• +۴ مورد دیگر

Helmer Aslaksen is an Associate Professor at the University of Oslo , affiliated with the Department of Teacher Education and School Research . He holds a Cand. mag. from the University of Oslo and a Ph.D. in Mathematics from the University of California, Berkeley. His research spans mathematics education, cultural astronomy, calendar systems, Lie theory, and the history of mathematics. He has authored numerous publications, including works on the Chinese, Islamic, and Indian calendars, and has contributed to interdisciplinary courses like 'Mathematics in Art and Architecture.' Aslaksen is a recipient of the Outstanding Educator Award (2004) from the National University of Singapore. He directs the Special Programme in Science and the Joint ANU-NUS Master of Science in Science Communication . His teaching focuses on general education modules, including 'Heavenly Mathematics & Cultural Astronomy' and 'Mathematics in Art and Architecture.' He has organized public lectures, Sudoku competitions, and exhibitions, such as 'Art Figures: Mathematics in Art' at the Singapore Art Museum. His outreach activities emphasize connecting mathematics to cultural, historical, and artistic contexts.

• Artificial Intelligence Security
• Adversarial Machine Learning
• Large Language Models
• +۳ مورد دیگر

Ryan Anthony Marinelli is a Doctoral Research Fellow at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, specializing in digital security with a focus on AI-cybersecurity intersections. His work addresses critical vulnerabilities in emerging technologies while contributing to institutional research objectives in secure computing. Marinelli's research spans AI Security , Adversarial Machine Learning , and Responsible AI Development , examining prompt injection attacks, knowledge leakage in large language models, and cryptographic deployment challenges. His methodology integrates causal tracing , reinforcement learning , and chain-of-thought analysis to develop detection frameworks and optimization strategies for real-world security applications. Analysis of his 2023-2025 publications reveals concentrated expertise in LLM security assessment, with recurring themes of societal risk mitigation and ethical AI development. His work demonstrates technical depth in homomorphic encryption optimization and password leakage evaluation, while maintaining strong connections to practical cybersecurity implementations through IEEE conference contributions and arXiv preprints. As part of UiO's Department of Informatics research ecosystem, Marinelli collaborates across multidisciplinary teams including cybersecurity specialists and AI researchers. His affiliation with the Faculty of Mathematics and Natural Sciences positions him within Norway's leading hub for digital security innovation, though specific laboratory structures aren't detailed in the source material.

Odd Kolbjørnsenمشاهده پروفایل

دانشیار

• Geophysics
• Seismic Inversion
• Bayesian Statistics
• +۳ مورد دیگر

Odd Kolbjørnsen is an Associate Professor at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. His primary research focuses on geophysics, seismic inversion, Bayesian statistics, and data-driven reservoir modeling. He holds a strong academic background in mathematical geosciences, with contributions to methodologies like Bayesian inversion, Markov mesh modeling, and deep learning applications in geoscience. His work bridges traditional geophysical analysis with modern computational techniques, emphasizing uncertainty quantification and high-resolution imaging in reservoir characterization. Key research areas include seismic data reconstruction, multi-task neural networks for flow metering, and 4D seismic inversion for lithology-fluid prediction. His publications span journals like Geophysics , Mathematical Geosciences , and Neural Networks , reflecting interdisciplinary collaborations in geostatistics and energy engineering. No scientific awards are explicitly listed, though his extensive publication record underscores his research impact. His advisory role and involvement in research groups like 'Statistics and Data Science' at UiO highlight his academic leadership. Ongoing work focuses on integrating machine learning with geophysical data analysis and CO2 sequestration optimization.

Carsten Griwodzمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• Network Security
• +۵ مورد دیگر

Carsten Griwodz is a Professor in the Department of Informatics (IFI) at the University of Oslo, specializing in digital infrastructure and security. He leads the Distributed Infrastructure and Security (DIS) research group and contributes to several specialized labs including the Sustainable Immersive Networking Lab (SINLAB), Imagine Beyond 5G Blockchain Lab, and the AliceVision Association. Professor at Department of Informatics, University of Oslo Section leader for DIS: Distributed Infrastructure and Security Group member in Networks and Distributed Systems (ND) Active in sustainable immersive networking and blockchain labs Co-founder of AliceVision open-source photogrammetric framework His research focuses on network performance, edge computing, and immersive technologies. He has pioneered work in cloud gaming QoE, real-time 3D reconstruction, and low-latency systems. Current projects explore redirected walking in VR, GPU programming for tracking, and sustainable networking solutions. Recent publications (2021-2024) demonstrate his expertise in network delay analysis, point cloud compression, virtual reality environments, and GPU-accelerated systems. His work bridges computer science fundamentals with cutting-edge applications in multimedia, security, and health informatics. He supervises numerous master's theses covering diverse topics from wireless streaming challenges to medical imaging advancements, consistently mentoring on topics related to network optimization, immersive technologies, and GPU computing. Supervised 40+ master's theses (2004-2024) Thesis topics span network performance, GPU programming, VR systems, and multimedia processing Active in both theoretical and applied research domains Mentoring interests include HCI, distributed systems, and real-time processing Contributes to education through research-led supervision As a permanent faculty member with extensive publication records and active research groups, Griwodz maintains significant influence in academic circles through both his technical contributions and educational mentorship.

Camilla Lingjærdeمشاهده پروفایل

پژوهشگر ارشد

• High-dimensional statistics
• Scalable inference
• Graphical modelling
• +۴ مورد دیگر

Camilla Lingjærde is a Postdoctoral Fellow at the University of Oslo's Department of Mathematics, supported by the Marie Skłodowska-Curie Actions COFUND and Integreat Postdoctoral Fellow programs. Her research focuses on advancing statistical and machine learning methodologies for high-dimensional data analysis, graphical modeling, and causal inference. PhD in Statistics from University of Cambridge (2023) Specializes in integrative/multi-task learning and statistical omics Collaborates with researchers across Mathematics and Informatics departments Her work emphasizes algorithm robustness and equity in critical domains like healthcare and environmental monitoring. Awards include the NR Master Thesis Award, AKER Scholarship, and Abelstipend. Scientific Awards: NR Master Thesis Award (2019) AKER Scholarship (2019-2023) NORAM Scholarship (2018) Abelstipend (2018)

Cagatay Turkayمشاهده پروفایل

دانشیار

• Visual Analytics
• High-Dimensional Data Analysis
• Biomedical Informatics
• +۳ مورد دیگر

Cagatay Turkay is an Associate Professor at the Department of Informatics, University of Bergen, Norway. He leads the VisGroup, focusing on visualization and visual data science. His research integrates computational methods with interactive visual analytics for high-dimensional data, biomedical informatics, and geographical analysis. He holds a PhD from the University of Bergen (2013). His work emphasizes dual analysis frameworks, progressive computation, and visual summaries for complex datasets. Education: PhD in Informatics (2013, University of Bergen), MSc and BSc in Computer Science (details not explicitly stated in text). Research Interests: Visual analytics for biomedical and geographical data, interactive exploration of molecular simulations, high-dimensional data analysis, and user-centric design of analytical tools. Notable contributions include the StratomeX framework for cancer subtype analysis and attribute signatures for multivariate geospatial analysis. His publications span IEEE Transactions, BMC Bioinformatics, and EuroVis venues, addressing challenges in cluster analysis, temporal data, and molecular cavity visualization. He actively contributes to academic hiring, with recent openings for tenure-track and professorial positions in visualization.

• Computer Architecture
• Parallel Processing
• Multi-core Programming
• +۴ مورد دیگر

Professor Lasse Natvig is a faculty member at the Norwegian University of Science and Technology (NTNU) in the Faculty of Information Technology and Electrical Engineering , specifically the Department of Computer Science . His research focuses on computer architecture , parallel processing , multi-core programming , and energy-efficient computing . His work includes studying vectorization techniques , cache management , power emulation models , and task scheduling policies to improve performance and energy efficiency in modern processors. He has published extensively in journals like Expert Systems With Applications , The Journal of Supercomputing , and Lecture Notes in Computer Science , with recent 2024 work on micromobility simulation tools. Lasse Natvig's research spans computer architecture , green computing , parallel algorithms , and energy measurement through publications addressing vectorization , cache optimization , and heterogeneous processing . His articles reveal a consistent focus on performance-energy tradeoffs , hardware-software co-design , and simulation tools for computational challenges. Email: lasse.natvig@ntnu.no

Asieh Abolpour Mofradمشاهده پروفایل

پژوهشگر

• Machine Learning
• Neural Networks
• Associative Memory
• +۷ مورد دیگر

Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.