Rebekka Olsson Omslandseter is an Associate Professor in the Department of Information and Communication Technology at the University of Agder (UiA). She joined UiA in 2014 and completed her bachelor's degree in Electronics (2017), master's degree in Information and Communication Technology (2020), and doctoral degree in Artificial Intelligence (2023) at the same institution. Her research integrates machine learning with telecommunications, focusing on reinforcement learning algorithms for data grouping in dynamic environments. Her core research interests include: Development of hierarchical learning automata for efficient data partitioning Reinforcement learning optimizations for wireless networks Stochastic grouping algorithms with applications in mobile communications Signal processing calibration for broadcast systems Her publications demonstrate a consistent focus on enhancing learning automata efficiency, with recent work exploring applications in 5G NOMA systems and hierarchical decision-making frameworks. The research consistently bridges theoretical machine learning with telecommunications engineering.
Ilias Pappas is a Full Professor in the Department of Information Systems at the University of Agder (UiA), Norway, and also holds a Full Professor position at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). He leads the Human-Centred AI (HCAI) research group, focusing on human-centered AI, data science, digital transformation, and IT adoption. His work integrates fuzzy-set Qualitative Comparative Analysis (fsQCA) digital innovation public sector applications . Pappas serves as Associate Editor for multiple journals including International Journal of Information Management and Information Systems Frontiers , and has been Guest Editor for special issues on AI, Big Data, and Digital Transformation. He chairs tracks at major conferences like ECIS and AMCIS , and contributes to the IFIP Working Group 6.11 on digital society. His scientific contributions include over 150 publications, with recent work exploring AI transparency in public services employee-driven digital innovation responsible AI frameworks fsQCA applications across journals like European Journal of Information Systems and British Journal of Management . As a ERCIM and Marie Skłodowska-Curie fellow , Pappas leads EU-funded projects (Horizon 2020/2022) and Norwegian Research Council initiatives. His research spans AI ethics and fairness digital marketing user experience design social innovation healthcare digitalization with a focus on practical implementation through action design research and configurational methods.
Professor Souman Rudra is a faculty member at the Department of Engineering Sciences within the University of Agder (Norway) since 2022. He holds a Ph.D. in Energy Technology from Aalborg University (Denmark, 2013) and has served as Associate Professor (2013-2022) and Visiting Researcher at the University of Alberta (2012). His career spans institutions including Aalborg University, Ajou University, and CUET. Education : Ph.D. (Aalborg University, 2013), MSc (Aalborg University, 2010), BSc (Chittagong University, 2007) Pedagogical Training : Uniped courses, doctoral supervision qualifications, and lecturing skill development His research focuses on renewable energy systems with specialization in biomass conversion, thermal energy, quad-generation plants, and process simulation. Recent studies emphasize hydrodynamic cavitation for biomass processing, hydrogen production from waste, and advanced battery material development. His publications bridge chemical engineering, energy systems, and material science with applications in: Biofuel production from lignocellulosic materials Quad-generation system optimization Photocatalytic energy storage solutions Machine learning applications in thermal plants Industrial waste-to-energy technologies Current teaching responsibilities include courses on: ENE 415: Combined Heat and Power Systems ENE 227: Thermodynamic and Heating System ENE 230: Fluid Flow and HVAC System ENE 420: Bioenergy
Johan Lie is an Associate Professor at the Department of Mathematics , University of Bergen. His email is johan.lie@uib.no . He has contributed extensively to mathematics education through interdisciplinary projects and digital tools.
Tore Brox-Larsen is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway. His work centers on distributed computing, high-performance systems, and large-scale visualization technologies. He is actively engaged in research involving sensor networks, Arctic observatories, and remote visualization of scientific data. His research interests span distributed shared memory systems, MPI performance, tiled display walls, and networked visualization. He has made significant contributions to improving communication efficiency in cluster computing and enabling scalable interactive visualization environments. His work bridges computer systems engineering with applications in genomics and environmental monitoring. The most recent publications reflect a strong trend toward real-world deployment of large-scale systems, particularly in Arctic observation and healthcare informatics. His work emphasizes practical system design, latency optimization, and cross-platform interoperability in distributed environments. Tore Brox-Larsen has collaborated extensively with researchers such as Otto Anshus, John Markus Bjørndalen, and Brian Vinter. While no formal advising or grant history is listed, his sustained publication record indicates active research leadership and team-based scientific inquiry. He has contributed to major projects including the development of a large-scale Arctic observatory sensor system and interactive tiled display walls. His work supports both academic research and societal applications in health and environmental science.
Professor Ahmad Rafi is a faculty member in the Department of Clinical Bioinformatics at UiT The Arctic University of Norway, located in Tromsø. He leads research focused on rapid clinical diagnostics of bacterial infections and antimicrobial resistance (AMR), leveraging advanced technologies like nanopore sequencing and machine learning. His work emphasizes One Health perspectives, integrating human, animal, and environmental health domains. Research Interests: Development of rapid diagnostic tools for AMR and pathogen identification Integration of genomic sequencing (WGS) and real-time taxonomic analysis Optimization of clinical workflows for sepsis and urosepsis management Multi-omic approaches combining quantitative phase microscopy and machine learning Recent Publications Highlight: His team has achieved diagnostic turnaround times as low as 4–9 hours using nanopore sequencing for sepsis and urinary tract infections. Innovations include culture-independent methods and SERS nanowire chips for strain-level bacterial classification. Advising & Grants: While specific student names or grant details aren’t listed, his involvement in projects like AMR-Educate reflects collaborative efforts in education and innovation around antimicrobial resistance. Labs/Teams: Active member of the Clinical Bioinformatics Research Group, contributing to translational research bridging computational methods with clinical applications.
Enrico Riccardi is an Associate Professor in Computational Engineering at the Department of Energy Resources, Faculty of Science and Technology, University of Stavanger (UIS), Norway. His work bridges computational chemistry, machine learning, and multi-scale modeling, with applications in energy, environmental science, and biophysics. Research Interests: His core expertise lies in molecular dynamics , rare event simulation methods (e.g., reaction kinetics and adsorption), and multi-scale modeling from molecular to continuum levels. He is a key developer of path sampling methodologies and software such as PyRETIS and PyVisA , enabling the study of slow and rare processes in complex systems. His research spans interfacial phenomena in emulsions, membrane permeation, atmospheric chemistry, and data-driven discovery of reaction pathways using machine learning. Recent Publication Trends: Over the past decade, Riccardi has consistently published in high-impact journals such as Journal of Chemical Physics , Physical Chemistry Chemical Physics , and Nature Machine Intelligence . His recent work (2023–2025) shows an expanded scope into educational technology , environmental science , and open-source tool development (e.g., GeoSight), reflecting a growing interdisciplinary impact. The publications reveal a strong focus on algorithmic innovation in simulation methods and their application across chemistry, biology, and engineering. Scientific Contributions: Lead and co-developer of PyRETIS, a widely used open-source library for rare event simulations. Contributor to immuneML, a machine learning ecosystem for immune repertoire analysis published in Nature Machine Intelligence . Active in promoting open science, data sharing, and academic integrity through public commentary and educational initiatives. Advising and Grants: While no formal students are listed in the provided text, Riccardi has mentored or collaborated with numerous early-career researchers and PhD candidates, particularly within the van Erp group. He has contributed to multiple collaborative research projects, likely funded by Norwegian and European research councils, though specific grants are not mentioned. His outreach on postdoctoral challenges suggests engagement with academic policy and mentorship. Labs and Teams: Riccardi is part of a vibrant computational research group at UIS, closely collaborating with Prof. Titus Sebastiaan van Erp and colleagues in the Department of Energy Resources. His work is embedded in a team focused on advanced simulation techniques, with strong ties to international networks in computational chemistry and soft matter physics.
Hassan Gholami is an Associate Professor at the University of Stavanger, affiliated with the Department of Safety, Economics, and Planning within the Faculty of Science and Technology. He also serves as a consultant at Multiconsult in the solar and smart grid division, contributing extensive expertise in renewable energy systems. His academic role emphasizes integrating solar energy into urban and regional planning, with a strong focus on education and student mentorship. His research interests lie at the intersection of solar energy and urban sustainability, particularly in building-integrated photovoltaics (BIPV), solar energy planning, and smart grid technologies. His work spans technical, economic, and environmental assessments of solar systems, with a special focus on optimizing energy performance in urban environments. He actively engages in feasibility studies, lifecycle cost analyses, and policy evaluations to advance the adoption of solar technologies. The recent body of his publications indicates a strong trend toward holistic, multi-criteria evaluations of solar energy deployment in cities. His work covers solar potential mapping, BIPV economic viability, climate impacts, and integration into building envelopes. He frequently analyzes both technical performance and financial feasibility, often using real-world case studies from Norway and Europe. His interdisciplinary approach combines engineering, economics, and urban design to promote sustainable energy transitions. Hassan Gholami has not received any explicitly mentioned scientific awards in the provided text. However, his prolific publication record in high-impact journals such as Solar Energy , Energies , and Energy Conversion and Management underscores his scholarly impact. He mentors master’s students in solar energy and smart grid technologies and contributes to various solar energy projects at UiS. While no specific grants are listed, his applied research and industry consultancy suggest active involvement in funded or collaborative initiatives. His dual role in academia and industry highlights his commitment to translating research into practical, scalable solutions. He is involved in several research and dissemination activities, including workshops at Nordic Edge Expo, public interviews, and collaborative book chapters. These efforts reflect his engagement with both technical and public audiences, promoting awareness and adoption of solar energy solutions in Norway and beyond.
Alf Kristian Gjerstad is an Associate Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His research focuses on automated drilling systems, optimization of drilling parameters (such as rate of penetration and tripping speed), and computational modeling for real-time hazard detection (e.g., kicks, losses, differential sticking). He is based in Stavanger, Norway, and can be reached at alf.k.gjerstad@uis.no. His research interests span automated drilling , drilling fluid rheology , mechanical and flow modeling , multiphase flow , and geothermal drilling . He emphasizes practical simulation tools for real-time applications, particularly in high-pressure, high-temperature (HPHT) environments. His work bridges petroleum engineering, fluid dynamics, and control systems, promoting interdisciplinary collaboration. The recent publications (2012–2024) highlight a strong trend in modeling non-Newtonian and multiphase flows in drilling, with applications in surge/swab pressure prediction, gas kick simulation, and real-time optimization. His work frequently appears in SPE journals and ASME/IEEE conferences, indicating a focus on both theoretical and applied aspects of drilling engineering. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While specific students and grant details are not listed, Dr. Gjerstad has co-authored research with academic and industry collaborators, suggesting involvement in funded projects and student supervision. His publications in optimization and control systems imply engagement in research teams and potential advising of graduate students in petroleum and mechanical engineering. Labs and Teams: Though not explicitly mentioned, his research in real-time modeling and automated drilling suggests affiliation with simulation labs or drilling automation research groups at the University of Stavanger, possibly involved in digital oilfield or smart drilling initiatives.
Bernt Sigve Aadnøy is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His work bridges theoretical and applied petroleum engineering, with a strong emphasis on drilling operations, well design, and geomechanics. His research focuses on drilling optimization, wellbore stability, drilling fluids, and smart well systems. He has extensively studied the use of nanoparticles in drilling fluids, rate of penetration modeling, torque and drag in 3D wells, and closed-loop drilling optimization. His work integrates computational modeling, laboratory experiments, and field case studies. The recent publications highlight a consistent trend in applying advanced modeling techniques—including machine learning, finite element analysis, and stochastic optimization—to solve complex drilling challenges. Topics include nanoparticle-enhanced fluids, real-time mechanical specific energy minimization, and autonomous downhole control systems, reflecting a strong interdisciplinary approach combining petroleum engineering with data science and control theory. Bernt Sigve Aadnøy has collaborated with numerous researchers and students, contributing to advancements in drilling safety, efficiency, and sustainability, particularly in challenging environments such as the Arctic and deep-water reservoirs.
Guttorm Alendal is a Professor in the Department of Mathematics at the University of Bergen. His research focuses on mathematical modeling applied to environmental challenges, particularly CO2 storage monitoring, oceanography, and climate science. He leads projects like ACTOM (Advanced Monitoring for Offshore CO2 Storage) and MATH4SDG, emphasizing cross-disciplinary approaches to sustainability. Education: Ph.D. in Applied Mathematics (1996, University of Bergen, thesis: Implementing and testing of different gravity current models). Key Research Areas: Fluid dynamics, environmental monitoring systems, carbon sequestration risk assessment, and marine pollution modeling. Collaborates with NORCE Norwegian Research Centre and international initiatives like STEMM-CCS. Publications: Over 80 peer-reviewed articles on topics like CO2 seep detection, Bayesian environmental modeling, and particle transport in oceanic waters. Recent work includes data-driven dynamical systems for disease modeling and reinforcement learning for environmental surveillance. Awards: While no specific awards listed, his work has been funded by EC H2020, Research Council of Norway, and JPI-Oceans. Grants: Active projects include ACTOM (2019-2022), MATH4SDG (2021-2026), and SFI CRIMAC (2020-2028). Former roles include leading ECO2 (FP7) and co-authoring technical reports on CO2 plume dynamics. Labs/Teams: Involved in Digital Life Norway's dCOD project (systems toxicology of Atlantic cod) and the Bergen Ocean Model development team.
Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.
Ola Jetlund is an Associate Professor at the Faculty of Technology, Art and Design, Department of Mechanical, Electrical and Chemical Engineering, Oslo Metropolitan University. His work focuses on electronics, signal processing, and digital education. Education: Doctor Engineer (PhD) in Electronics and Signal Processing Research Interests: Jetlund's research spans adaptive multimedia streaming, channel coding optimization, and digital pedagogy. He contributes to the ADvanced hEalth intelligence and brain-insPired Technologies (ADEPT) research group. Publication Trends: His recent works emphasize community-based digital teaching methods and historical contributions to adaptive coded modulation schemes for wireless networks. Administrative Roles: Currently serves as Head of Studies in Electronics and Electrical Engineering.
Benedek Plosz is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Built Environment . His research focuses on environmental engineering, biotechnology, and computational modeling, particularly in water systems and wastewater treatment. His work includes advancements in digital twin applications for water resource recovery, N2O emission modeling , and antimicrobial resistance in biological wastewater systems. Recent publications highlight collaborations with researchers like Vince Bakos and Yuge Qiu, with studies appearing in Water Research and Chemical Engineering Journal . The Smart Water Engineering Group (SWING) drives his research, integrating machine learning and CFD modeling to optimize settling tanks and aeration systems. He has presented at international conferences such as IWA WRRmod and WATERMATEX, addressing topics like biofilm dynamics and gas transfer mechanisms. Benedek Plosz can be reached at benedek.plosz@oslomet.no . No scientific awards are listed in the provided data.
Pål Halvorsen is a Professor at the Department of Computer Science within the Faculty of Technology, Art and Design at Oslo Metropolitan University. He works at the intersection of computer science and applied domains, with a particular focus on multimedia systems, distributed computing, and healthcare applications. Specializes in distributed multimedia systems Active in AI-driven forensic psychology applications Conducts research on medical imaging and diagnostics Develops sports analytics datasets and tools Works on communication and distributed systems His research spans several key areas of computer science, particularly focusing on multimedia systems and their applications in healthcare, sports analytics, and forensic psychology. He leads projects involving AI-driven child avatars for investigative interview training, develops datasets for medical and sports applications, and explores innovative approaches to image analysis and time-series data processing. Recent publications demonstrate strong activity in applying computer vision and deep learning to medical diagnostics, particularly in gastrointestinal tract analysis and ophthalmology. His work on sports analytics includes creating comprehensive datasets for ice hockey and soccer, while his forensic psychology research focuses on AI-enhanced interview training for child abuse investigations. Halvorsen collaborates extensively across disciplines, working with researchers in psychology, medicine, and sports science. His projects often involve developing novel tools for data analysis, including approaches to multimodal data handling, visual deep learning verification, and AI-enhanced prompt generation techniques.
