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Ulrika Simonsson is a Professor of Pharmacokinetics at the Department of Pharmaceutical Biosciences, Uppsala University. She leads an independent research group focused on pre-clinical to clinical pharmacokinetic-pharmacodynamic translation and its application to drug development . Her work bridges pharmacokinetics, pharmacodynamics, and pharmacometrics with translational medicine. PhD in Pharmaceutical Sciences (2000), Uppsala University Postdoctoral work at Pharsight Corporation (2001-2002) Pharmacometrician at AstraZeneca (2004-2006) Her research emphasizes model-informed drug development and translational pharmacology , particularly in tuberculosis and hemophilia. She has developed nonlinear mixed-effects models for drug interactions and disease progression, and pioneered pharmacometric approaches to optimize dosing in clinical trials. Recent publications highlight applications in antibiotic resistance , machine learning in pharmacokinetics , and model-based biomarker development . She leads consortia projects ERA4TB and UNIT4TB , integrating academia and industry in drug development. Scientific Awards AstraZeneca's Young Talented Scientist (2004) AstraZeneca's Clinical Innovation (2007) Uppsala University Grant for Promotion to Professor (2011) With over 20 years of industry consultancy and 90+ peer-reviewed articles , she supervises 4 active PhD students and has completed 10 PhD supervisions . Her work spans therapeutic areas including tuberculosis , malaria , HIV , and hemophilia .

• Pharmacokinetics
• Pharmacodynamics
• Pharmacometrics
• +7 more

Michael M. Zavlanos is the Yoh Family Professor in the Department of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. He holds secondary appointments in the Departments of Electrical and Computer Engineering and Computer Science. Additionally, he serves as Director of the Healthcare Systems Optimization program with Duke AI Health and is an Amazon Scholar with Amazon Robotics. National Technical University of Athens (NTUA): Diploma in Mechanical Engineering (2002) University of Pennsylvania: M.S.E. and Ph.D. in Electrical and Systems Engineering (2005, 2008) His research spans Control Theory , Optimization , Machine Learning , and AI , with applications in Robotics , Cyber-Physical Systems , and Healthcare/Medicine . Recent work focuses on systems-level approaches combining mathematical optimization and AI to address healthcare challenges like surgery scheduling and resource utilization, reducing patient wait times, and enhancing operational efficiency. His recent publications highlight trends in Distributionally Robust Optimization , Transfer Learning for Robotics , and Graph Neural Networks integrated with Path Signatures . These works emphasize algorithmic resilience to outliers, adaptation to visual observation changes, and modeling time-series data on graphs. Office of Naval Research Young Investigator Program (YIP) Award National Science Foundation Faculty Early Career Development (CAREER) Award At Duke AI Health, Dr. Zavlanos leads initiatives to optimize healthcare workflows by leveraging predictive models, algorithmic technologies, and collaborations across engineering, surgery, and anesthesiology. His role bridges Duke’s Pratt School of Engineering with its School of Medicine to translate methodological innovations into clinical impact.

• Control Theory
• Optimization
• Machine Learning
• +4 more

Elizabeth Blackburn is a Professor at Lund University's Department of Physics, leading synchrotron radiation research and serving as Principal Investigator at NanoLund: Centre for Nanoscience. Her work focuses on electron interactions in materials, exploring quantum mechanical states like superconductivity and magnetism through neutron and X-ray scattering techniques. Research Interests: Strongly correlated electron systems Neutron and X-ray scattering Magnetic materials Superconductivity Quantum materials Lattice fluctuations Recent Research Trends: Her recent publications emphasize superconductivity, vortex lattices, and quantum states of matter, with applications of X-ray and neutron scattering to study charge density waves, antiferromagnetism, and ultrafast magnetic dynamics. Scientific Awards: Henry Moseley Medal and Prize (2014) Projects: She leads multiple Swedish Research Council-funded projects, including studies on cuprate superconductors, development of neutron polarisation techniques at the European Spallation Source, and exploration of exotic magnetism under extreme conditions.

• Condensed Matter Physics
• Superconductivity
• Magnetism
• +4 more

Anders Crichton-Fock is a Senior Lecturer and researcher at the School of Restaurant and Hotel Management , Örebro University, specializing in Meal Science and Hospitality . He earned his undergraduate education at Campus Grythyttan and completed both a licentiate thesis (2004) and doctoral thesis (2011) through collaborations between Örebro University and the Royal Institute of Technology (KTH). His international experience includes a Visiting Assistant Professor role at Washington State University. Research Interests : Anders combines analytical and analogical sensory approaches to develop situational methods for future meal contexts , focusing on Sustainable meal production using alternative proteins and resource efficiency Pedagogical models for communicating multisensory experiences (notably the dual approach method) Product development through consumer-critical attribute identification in robotic and AI-driven food systems His work explores the intersection of art and science in sensory analysis, with projects like Re:wine The World® (reducing food waste via crossmodal communication) and LOMAS (robotic cultivation beds for public environments). Scientific Contributions : Anders has published extensively in journals like Future Foods , Journal of Wine Research , and Frontiers in Psychology , with a focus on Robotic agriculture's impact on plant metabolism and consumer preferences Crossmodal correspondences in wine communication Curriculum development for sommelier education He leads the Sense Lab at Örebro University and contributes to the Faculty Board for Economics, Natural and Technological Sciences (ENT).

• Meal Science
• Hospitality Management
• Sensory Analysis
• +3 more

Raja-Khurram Shahzad serves as a Lecturer in Computer Science at Mid Sweden University's Department of Communication, Quality Management and Information Systems (KKI), located in Östersund. Holding a PhD in Engineering, he maintains active research engagement in cybersecurity and educational technology domains. His research focuses on two primary thrusts: (1) Cybersecurity applications , particularly Android malware detection through advanced feature fusion and artificial data generation techniques; and (2) Educational technology innovation , where he develops formative assessment management systems leveraging AI for personalized student feedback. His work bridges theoretical computer science with practical implementations in mobile security and adaptive learning environments. Analysis of his publication trajectory reveals a strategic pivot from cybersecurity fundamentals (2018) toward educational technology applications (2023), demonstrating interdisciplinary adaptability while maintaining core expertise in machine learning systems. Both publications exhibit strong technical rigor with immediate real-world applicability in their respective domains. Scientific Awards No awards or fellowships documented in available materials Dr. Shahzad actively supervises graduate research in cybersecurity and educational technology, though specific student names aren't publicly cataloged. His grant portfolio appears focused on technology-enhanced learning initiatives and mobile security frameworks, with recent work supported through Mid Sweden University's internal research channels. Current projects indicate exploration of AI-driven assessment personalization and next-generation malware detection architectures. He operates within Mid Sweden University's Computer Science research ecosystem, contributing to departmental expertise in applied machine learning systems. His collaborative work with researchers like Erik Ström and Peter Mozelius demonstrates strong interdisciplinary networking within the Scandinavian academic community.

• Cybersecurity
• Educational Technology
• Machine Learning
• +2 more

Alireza Bahrami is an Associate Professor (Docent) and Head of the Building Engineering Subject at the University of Gävle. He specializes in Civil and Structural Engineering, with expertise spanning academic research and industrial applications in construction. His responsibilities include teaching specialized courses, supervising student theses, and leading research on sustainable building materials and structural performance. His research focuses on: Innovations in concrete technology (geopolymers, 3D-printed concrete) Structural strengthening techniques (FRP, post-tensioning) Sustainable material utilization (timber, waste ceramics, bagasse ash) Seismic and fire performance of structures Recent publications emphasize sustainable construction, with dominant themes including geopolymer concrete development, timber connection systems, and waste-material integration. His work frequently employs experimental validation and machine learning for material optimization. Dr. Bahrami leads research on structural retrofitting methods using eco-friendly materials like bamboo laminates and aluminum composites. He collaborates internationally on projects related to low-carbon concrete, cross-laminated timber, and infrastructure durability.

• Civil Engineering
• Structural Engineering
• Sustainable Materials
• +5 more

Faramarz Nilfouroushan is a Senior Lecturer in Land Surveying at the University of Gävle, Sweden. His research focuses on tectonic geodesy, ground motion measurements, and geospatial information sciences using satellite geodetic data such as GNSS and InSAR. He actively contributes to advancements in geodetic infrastructure through collaborations with institutions like Lantmäteriet. Research Interests: His work integrates geodetic techniques with geological and hydrological data to analyze crustal deformation, subsidence in mining areas, landslide monitoring, and salt dome modeling. He explores the impact of precise gravity field modeling on aerial photogrammetry and sensor integration. Article Trends: Recent publications emphasize landslide deformation monitoring via EGMS and SBAS-InSAR, GNSS/IMU integration challenges, and subsidence analysis in Sweden and Iran using Sentinel-1 data. Themes include geodetic infrastructure development, environmental monitoring, and tectonic modeling. Projects & Collaborations: He leads contributions to the InSAR-Sweden Project, maintains Sweden’s ETRS89 realization, and evaluates nationwide InSAR services. His teams implement geodetic SAR for height system unification and sea level research in the Baltic Sea.

• Tectonic Geodesy
• Geospatial Information Sciences
• Ground Motion Measurements
• +2 more

Jan Svanberg is a Senior Lecturer in the Department of Business Administration at the University of Gävle. His research focuses on Management Accounting, Performance Measurement, Corporate Social Responsibility (CSR), Auditor Objectivity, and Machine Learning applications in accounting. Academic Affiliation: Department of Business Administration, University of Gävle Research Themes: ESG ratings, audit quality, ethical cultures, and predictive analytics His recent publications explore topics such as machine learning-driven ESG performance predictions and the impact of client leadership on auditor independence. Collaborative work with researchers like P. Öhman and T. Ardeshiri highlights interdisciplinary approaches to accounting and sustainability. Scientific awards and honors are not mentioned in the provided text. Jan Svanberg’s collaborations and research trends indicate a strong emphasis on data-driven methodologies in accounting and corporate governance.

• Management Accounting
• Performance Measurement
• Accountability
• +5 more

Najmeh Abiri is a Senior Lecturer at Halmstad University's School of Information Technology. Her research focuses on machine learning, deep learning, and Bayesian inference with applications in image/text processing and life sciences. Research Interests She specializes in: Deep Generative Modeling (GenAI) Probabilistic machine learning Quantum mechanics applications Epidemiological forecasting Publications Her recent work spans tick classification challenges in citizen science (2025), time series imputation (2025), disease risk mapping (2024), and quantum mechanics modeling (2020). Key themes include data quality control, robust probabilistic methods, and ecological informatics applications. Affiliations School of Information Technology Halmstad University

• Machine Learning
• Deep Learning
• Deep Generative Modeling
• +4 more

Urban Bilstrup is a Senior Lecturer at the School of Information Technology, Halmstad University. His professional role focuses on advancing wireless communication technologies and computational intelligence applications. Research Interests: Dr. Bilstrup's work spans wireless network optimization, cognitive radio systems, and predictive modeling using computational intelligence. His research includes solar activity forecasting, cyber conflict analysis, and military communication protocols. Publications: His scholarly output demonstrates expertise in ad hoc networks, dynamic spectrum allocation, and neuro-fuzzy systems for space weather prediction, with a publication history from 2000-2020.

• Wireless Communication
• Cognitive Radio Networks
• Network Protocols
• +4 more

Hossein Azizpour is an Associate Professor in Machine Learning at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning (RPL). He holds associated roles at the Science for Life Laboratory, Swedish e-Science Research Centre, WASP, and Digital Futures. His research focuses on deep learning under uncertainty, model interpretability, feature selection, and applications in life sciences, physics, and earth observation. Research Themes : (1) Deep learning under uncertainty (Bayesian methods, semi-supervised learning), (2) Interpretable AI (feature attribution, understanding neural networks), (3) Generative & foundation models (Vision Transformers, diffusion models), (4) Applications in medical imaging, fluid dynamics, and earth observation. Recent Work Trends : His publications emphasize vision transformers, uncertainty quantification, and cross-domain applications. Key areas include model optimization (e.g., Patchdropout), medical AI (e.g., breast cancer risk prediction), and physics-informed deep learning. Scientific Awards : Best Paper Award, CVPR DeepVision Workshop (2015) Google Travel Grant, CVPR DeepVision Workshop (2015) Best Paper Runner-Up, CVPR DeepVision Workshop (2014) Best Industry Paper Prize, BMVC (2013) Outstanding Reviewer, CVPR (2013) Advising & Collaborations : Supervises PhD students in AI for physics applications (e.g., fluid dynamics), medical imaging, and computer vision. Collaborates with institutions like AstraZeneca, Alan Turing Institute, and Meta. Labs & Teams : Leads the Robotics, Perception and Learning (RPL) division’s research and co-organizes the Computer Vision Reading Group at KTH.

• Deep Learning
• Computer Vision
• Artificial Intelligence
• +4 more

Rasmus Nykvist is an Assistant Professor in the Department of Business Administration (FEK) at Linköping University, operating within the Department of Economic and Industrial Development (IEI). His academic position places him at the intersection of strategic management, organizational theory, and business history. His research focuses on strategic processes within contexts undergoing institutional, regulatory, and technological change. Using business history methods, he investigates how strategic processes develop across various sectors and companies. His teaching portfolio includes courses in strategy, organization, research methodology, and company history at the university level. Analysis of his publication record reveals a strong pattern of research connecting historical perspectives with contemporary strategic challenges. His work often examines institutional transitions, particularly in Swedish industries like telecommunications, finance, and housing markets. The research demonstrates an interdisciplinary approach combining organizational theory, historical analysis, and strategic management frameworks. Introducing the inverted Icarus paradox in business history (2025) The State as a Marketizer vs. the Marketization of the State (2024) Managers, minds, and machines: Bridging predictive processing and prospective sensegiving (2024) As a supervisor, Nykvist guides master's students through their thesis work, contributing to the next generation of business administration scholars. His research methodology, which blends business history with contemporary strategic analysis, offers valuable insights into how organizations navigate periods of significant transformation.

• Strategic Processes
• Institutional Change
• Regulatory Change
• +5 more

Sverker Werin is a Professor at Lund University , affiliated with the Faculty of Engineering LTH , and serves as Project Manager for the Accelerator Division at MAX IV . His work focuses on synchrotron radiation , free-electron lasers , and electron beam dynamics . Active research in accelerator physics, longitudinal phase space optimization, and X-ray source development Supervises research students Lundquist, J., Pop, M., and Paraskaki, G. Key projects include machine learning for linear accelerators and the IFELD initiative Research Trends: Recent publications highlight advancements in bunch compression , ultrafast X-ray sources , and virtual diagnostics , aligning with applications in photon science and materials research . Leadership Roles: Chair of the Accelerator Physics Section in the Swedish Physical Society and active member in international panels like LEAPS and SESAME Council.

• Subatomic Physics
• Accelerator Physics
• Free-Electron Lasers
• +3 more

Birgitta Engberg serves as an Associate Professor and Senior Lecturer in the Department of Engineering, Mathematics and Science Education at Mid Sweden University in Sundsvall, Sweden. She is employed within the Chemical Engineering subject area and maintains strong affiliations with the FSCN Research Centre, focusing on industrial applications within the forest bioeconomy sector. Her research expertise centers on mechanical pulping processes and wood science , with particular emphasis on fiber characterization, pulp flow dynamics, and sustainable production methods. Key investigation areas include high-strain-rate wood compression mechanics, sliding friction under refining conditions, and the development of energy-efficient pulping techniques. Recent work integrates machine learning for fiber analysis and explores deep eutectic solvents for greener pulping processes, addressing critical industry challenges in resource efficiency and product quality. Analysis of Engberg's recent publications (2020-2024) reveals dominant themes in pulp heterogeneity , refiner groove hydrodynamics , and fiber property optimization . Her work demonstrates increasing methodological sophistication through computational fluid dynamics simulations and advanced statistical modeling of pulp particle distributions, directly supporting industrial innovation in mechanical pulping operations. No scientific awards are documented in the available materials. Engberg actively participates in major research initiatives including current projects DRIVEN (data-driven industrial transformation), ExpoFiber (fiber quality analytics), HIPS (sustainable forest bioeconomy), IRS TransTech (transformative technology research school), and NeoPulp. Completed projects encompass EcoSys and ModDD (process modeling for production optimization), reflecting sustained engagement with both fundamental mechanics and applied industrial challenges. She operates within the FSCN Research Centre ecosystem, which provides specialized infrastructure for fiber science investigations and fosters cross-disciplinary collaboration between material scientists, engineers, and industry partners in Sweden's pulp and paper sector.

• Pulp and Paper Technology
• Wood Science
• Mechanical Pulping
• +3 more

Mattias Ohlsson is a Professor at Lund University, holding key affiliations with the Centre for Environmental and Climate Science (CEC), the Artificial Intelligence in CardioThoracic Sciences (AICTS) initiative, and the eSSENCE: The e-Science Collaboration. He is also a member of the LU Profile Area: Natural and Artificial Cognition. His work bridges computational science, medicine, and biology, focusing on real-world health applications of machine learning. He contributes to major interdisciplinary research projects and is deeply embedded in Lund’s academic and research ecosystem. His research centers on machine learning, particularly neural networks, applied to medical and biological challenges. Key areas include biomarker discovery in cancer using proteomic data, survival analysis in cardiac transplantation to optimize donor-recipient matching, and AI-driven decision support for medical imaging , such as whole-body bone scintigraphy for prostate cancer. His recent work emphasizes early cancer detection and emergency department diagnostics , especially in dyspnea and triage optimization. His 15 most recent publications reveal a strong trend toward deep learning applications in oncology and emergency care , with a focus on improving diagnostic accuracy and clinical decision-making. These works span high-impact journals in digital medicine, oncology, and medical informatics, demonstrating a consistent contribution to translational AI in healthcare. Scientific Awards and Grants: Grant from Swedish Research Council (PREPARE project, 2021) Grant for AI in emergency department resource management (2021 Dec 6) Grant (2022 Mar 31) Grant (2022 Oct 19) Grant (2022 Dec 1) Grant (2023 Oct 27) Advising and Grants: Mattias Ohlsson actively supervises PhD students, serving as a joint second supervisor in projects related to AI in emergency care and breast cancer. He is a key researcher in multiple funded projects, including those supported by the Swedish Research Council. His collaborative grants focus on AI for pandemic preparedness, disease surveillance, and emergency department optimization, often in partnership with clinicians and data scientists. Labs and Teams: He is involved in several major research initiatives, including eSSENCE and the LU Profile Area: Natural and Artificial Cognition. He collaborates extensively within Lund University and with external partners, contributing to interdisciplinary teams focused on AI in health, climate, and emergency medicine. He organizes seminars such as the AI Lund lunch series and participates in events like the LUCC - AI and Cancer Seminar, indicating strong leadership in academic community building.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +12 more