جستجوی استاد راهنمای دانشگاهی

Markus Kowalewskiمشاهده پروفایل

مدرس

• Photochemistry
• Coherent Control
• Polaritonic Chemistry
• +۳ مورد دیگر

Markus Kowalewski is a Lecturer at Stockholm University's Department of Physics (Fysikum), leading the Molecular Quantum Dynamics and Spectroscopy research group. His work focuses on photochemistry, coherent control, polaritonic chemistry, and ultrafast spectroscopy, leveraging quantum dynamics and numerical methods. Research Interests: - Polaritonic Chemistry: Manipulating chemical reactions via optical cavities to modify potential energy landscapes and create hybrid light-matter states. - Ultrafast X-ray Spectroscopy: Developing techniques to observe conical intersections and nonadiabatic dynamics at femtosecond timescales. - Quantum Light-Matter Interactions: Theoretical frameworks for cavity-coupled molecular systems, including ab initio methods and cavity-Born-Oppenheimer approaches. Recent Publications Trends: - Dominant themes include strong light-matter coupling effects on molecular geometries, vibrational dynamics, and chemiluminescence. - Exploration of X-ray and attosecond-based spectroscopic methods to probe conical intersections and electronic coherences. - Applications of machine learning to enhance quantum dynamics simulations. Grants and Collaborations: - Group actively investigates funded projects on polaritonic materials and X-ray detection methodologies. - Collaborations span experimental and theoretical groups in quantum optics and ultrafast spectroscopy. Labs/Teams: - Leads the Molecular Quantum Dynamics and Spectroscopy group, advancing theoretical tools for polaritonic chemistry and nonadiabatic dynamics.

• Machine Learning
• Computer Vision
• Health Informatics
• +۴ مورد دیگر

Hasan Fleyeh serves as Associate Professor and Senior Lecturer in Computer Technology within the Department of Computer and Information Science at Dalarna University's School of Information and Engineering. His research bridges machine learning applications between healthcare diagnostics and transportation infrastructure systems. His primary research interests focus on Machine Learning and Computer Vision with specialized applications in Health Informatics (Parkinson's disease classification via gait analysis, dementia detection through speech patterns) and Transportation Engineering (road sign retroreflectivity modeling, color degradation prediction). Recent work demonstrates strong interdisciplinary collaboration across medical and engineering domains. His publication trends reveal a dual-track research trajectory since 2023: healthcare AI projects analyzing biomechanical and vocal biomarkers for neurological disorders, alongside transportation studies applying computer vision to road safety infrastructure. Both streams employ comparative algorithmic approaches with emphasis on practical implementation. As academic supervisor, he currently advises graduate students including Mustafa Al-Hammadi and Roxan Saleh on machine learning applications. His research integrates clinical data with engineering solutions, securing active project funding in both healthcare AI and smart transportation systems.

• Machine Learning
• Artificial Intelligence
• Vehicular Communication
• +۴ مورد دیگر

Cristofer Englund is the Dean of the School of Information Technology at Halmstad University and holds a professorship in Information Technology. He earned his PhD in Electrical Engineering in 2007. His teaching focuses on supervising bachelor and master’s theses in machine learning and artificial intelligence (AI), with students receiving awards such as Wimanska priset (2019) and the SAIS Master Thesis Award (2017, 2021). His research spans machine learning applications in emotion/posture recognition, behavior prediction, anonymization, and vehicular communication for traffic safety and automated driving. He currently supervises Felix Rosberg (Engage Studios) on image/video anonymization and Maytheewat Aramrattana (VTI) on connected transportation safety analysis. Research interests include AI ethics, safety-critical systems, and smart city technologies. Recent work emphasizes de-identification systems, drone detection, and federated learning in automotive ecosystems. His articles analyze AI applications in urban infrastructure, cybersecurity for anonymization, and real-time drone surveillance. Notable student awards include grants for Felix Rosberg’s work on anonymization defense mechanisms and Maytheewat’s contributions to traffic safety modeling. Englund collaborates with industry partners like Engage Studios and VTI, integrating academic research with real-world challenges. His work bridges theoretical advancements in machine learning with practical implementations in transportation, cybersecurity, and urban planning.

• Machine Learning
• Predictive Maintenance
• Forklift Activity Recognition
• +۵ مورد دیگر

Thorsteinn Rögnvaldsson is a Professor of Computer Science and Deputy Vice Chancellor for Research at Halmstad University. He leads the Center for Applied Intelligent Systems Research (CAISR), focusing on machine learning applications in predictive maintenance, healthcare, and satellite-based poverty analysis. His research spans meta-learning, survival analysis, and synthetic data generation. Education: Not explicitly listed in the provided texts, but inferred to include doctoral training in computer science or related fields given his academic rank. Research Interests: Machine learning for knowledge creation, predictive maintenance for heavy vehicles, forklift activity recognition, synthetic healthcare data, and satellite imagery analysis for poverty mapping. Collaborations include projects with Lund University on neural networks for satellite data interpretation. Articles Trends: Recent work emphasizes meta-learning frameworks, survival analysis model interpretability, and synthetic data evaluation. Key domains include healthcare, industrial IoT, and cross-domain adaptation. Awards: None explicitly listed in the provided texts. Grants/Advising: Coordinates CAISR's research projects and supervises doctoral students in applied machine learning. Active in industry partnerships, particularly with automotive and healthcare sectors. Labs/Teams: Director of CAISR, which hosts interdisciplinary teams in AI, IoT, and big data applications.

Christian Bierlichمشاهده پروفایل

پژوهشگر

• Particle Physics
• Nuclear Physics
• High-Energy Physics
• +۳ مورد دیگر

Christian Bierlich is a Researcher in Particle and Nuclear Physics at Lund University. His work focuses on hadronization processes, Monte Carlo event simulations, and heavy-ion collisions. He actively contributes to projects like RAGDOLL (Retrieval-Augmented Generation for Data-Oriented Large Language Learning in Particle Physics) and collaborates internationally on event generator development (e.g., Pythia). Bierlich teaches courses such as MNXA29 (Exploring the Scientific Method) and lab instruction in FytA11/FytA12. He blogs about fundamental physics at fundamental-fysik (in Danish) and conducts outreach lectures at the Peoples' University ( fuau.dk ). His research interests span theoretical modeling of particle interactions, data-driven approaches to hadronization, and Monte Carlo reweighting techniques. Recent articles explore strangeness conservation, normalizing flows for fragmentation models, and dynamic hadronization in heavy-ion collisions. Bierlich has received the Young Scientist Award (2017) . He leads projects funded by the Swedish Research Council and collaborates with institutions globally. His work supports initiatives like the ESSnuSB neutrino experiment and CLASH retreats for small-collision system studies.

Philip Pärnametsمشاهده پروفایل

پژوهشگر

• Social Psychology
• Cognitive Science
• Decision-Making
• +۵ مورد دیگر

Philip Pärnamets is a Senior Research Specialist at Karolinska Institutet's Department of Clinical Neuroscience, working within the Social and Affective Learning and Decision-Making research group led by Andreas Olsson. He maintains dual affiliation with both the Department of Clinical Neuroscience and Department of Psychology at KI's Faculty of Medicine, reflecting his interdisciplinary approach to studying human decision processes. His research spans social psychology, cognitive science, and behavioral neuroscience, with particular expertise in moral decision-making, political psychology, and collective behavior during crises. Pärnamets has developed a distinctive research profile examining how social identity, moral values, and situational factors shape individual and collective responses to global challenges like pandemics and climate change. His work frequently employs cross-cultural methodologies, with numerous large-scale international collaborations spanning dozens of countries. Analysis of his recent publications reveals a strong emphasis on applying behavioral science to real-world problems, particularly in understanding pandemic responses and climate change attitudes across diverse cultural contexts. His research demonstrates sophisticated integration of cognitive neuroscience methods with social psychological theory to investigate the mechanisms underlying human cooperation and decision-making in complex social environments. As evidenced by his extensive collaborative network, Pärnamets works closely with leading researchers including Jay Van Bavel, Andreas Olsson, and other members of the Social and Affective Learning and Decision-Making group. His publication record shows consistent high-impact contributions across top journals in psychology, neuroscience, and interdisciplinary science.

• Digital Transformation
• E-Learning and Technology-Enhanced Learning (TEL)
• Applied Artificial Intelligence
• +۴ مورد دیگر

Fisnik Dalipi is an Associate Professor of Informatics at Linnaeus University (LNU), Sweden, and holds an Associate Professorship in Information Systems at the University of South-Eastern Norway. He specializes in digital transformation, e-learning technologies, AI-driven solutions, and blockchain applications in education and industry. His research bridges computational methods with societal needs, focusing on sustainable data management, technology-enhanced learning, and privacy-preserving systems. Current Affiliations: Department of Informatics, Faculty of Technology at LNU Research Groups: Data-driven Business Innovation (DBI), Interaction Design Research Group, Linnaeus University Systems Community (LNU-STC) Key research areas include: - Digital transformation across agriculture, education, and manufacturing - Blockchain-based systems for academic credentials and product passports - AI applications in education, security, and healthcare - Human-computer interaction and inclusive technologies for special needs He leads EU/VINNOVA-funded projects like STELLA, APPEND, ENTRUST, and FOREST 4.0, aiming to enhance digital solutions in sectors like furniture industry and agriculture. His work emphasizes secure data practices and AI ethics.

• Artificial Intelligence
• Sensor Technologies
• Drug Delivery Systems
• +۴ مورد دیگر

Professor Amit Kumar-Mishra is affiliated with Halmstad University's Department of Artificial Intelligence and Sensor Technologies. His research spans interdisciplinary areas including AI-driven solutions for precision agriculture, drug delivery systems, cancer biology, and molecular imaging. He has authored over 30 publications in 2024-2025 alone, focusing on topics like pH-responsive nanocarriers for cancer therapy, circular RNA roles in cytokine signaling, and weed management strategies in mustard crops. Notable research contributions include development of deep neural network architectures for sensor technologies, investigation of salt stress adaptations in eggplant, and exploration of proteases for cancer diagnostics. His work integrates machine learning with agricultural challenges, such as real-time weed detection systems and hydroponics automation. Recent studies also explore novel biomarkers for ovarian cancer detection using circulating DNA analysis. Publications demonstrate a strong focus on translational research with applications in both clinical oncology and agricultural productivity. His research bridges computational methods with biological systems, emphasizing cross-disciplinary innovation in healthcare and food security domains.

Sonja Aitsمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Medicine
• Cell Death Mechanisms
• Lysosomal Biology
• +۸ مورد دیگر

Sonja Aits is an Associate Senior Lecturer and Research Team Manager at Lund University, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Cell Biology, and Environmental Science. She leads studies on lysosomal biology, cell death mechanisms, and their implications for human health and environmental sustainability. Her work bridges computational methods with biomedical applications, including AI-driven image analysis and text mining in medicine. Affiliations: Lund University Cancer Centre (LUCC), BECC (Biodiversity & Ecosystems), and multiple AI-focused profile areas (e.g., AI and Digitalization). Research Themes: Lysosome regulation, environmental toxin effects, AI in microscopy, and sustainable healthcare. Her research interests emphasize leveraging AI to analyze large-scale biomedical datasets, develop predictive models for protein function, and integrate multi-omics data. Collaborations include NEUBIAS, TRANSAUTOPHAGY, and international networks like eSSENCE and EpiHealth. Publications highlight contributions to high-content microscopy datasets, AI ethics in healthcare, and interdisciplinary AI education. She actively participates in conferences and public outreach on AI’s societal impact, particularly in Sweden. Grants and Projects: Over 22 funded projects, including FORMAS-supported studies on environmental toxins and AI-based text mining for pandemic response. Supervised one doctoral student to date. Labs/Teams: Manages research teams in Cell Death studies and coordinates initiatives like the 'Cell Death Census' and OpenChart-SE medical data projects.

• Machine Learning
• Data Privacy
• Medical Technology
• +۳ مورد دیگر

Azra Abtahi Fahliani is a Researcher in the Department of Secure and Networked Systems at Lund University’s Faculty of Engineering (LTH). She is affiliated with LTH’s profile areas in Engineering Health and AI/Digitalization, and is a member of the ELLIIT initiative in IT and mobile communication. Her work focuses on advancing privacy-preserving technologies, machine learning for healthcare applications, and secure wearable systems. Current roles: Postdoc (Secure and Networked Systems), Researcher (ELLIIT), and Project Researcher (NextG2Com) Key affiliations: ELLIIT, LTH Profile Areas, and the Secure and Networked Systems group Her research interests span machine learning interpretability, privacy-preserving protocols, wearable health monitoring systems, and cybersecurity for IoT devices. She has contributed to innovative projects such as DP-ACT (a privacy-preserving contact tracing system) and EpilepsyNet (a low-power seizure detection framework). Her articles highlight trends in federated learning, medical device security, and environmental modeling using ML. Key contributions include lightweight inference algorithms for neural networks and frameworks for transparent decision-making in healthcare AI. Azra is actively involved in the NextG2Com project, a Vinnova-funded initiative exploring next-generation communication infrastructures. She collaborates with interdisciplinary teams to bridge gaps between theoretical AI and practical healthcare/telecom applications.

Emma Hammarlundمشاهده پروفایل

مدرس ارشد

• Geobiology
• Cancer Research
• Evolutionary Biology
• +۶ مورد دیگر

Emma Hammarlund is a Senior Lecturer and Researcher at Lund University, where she serves as Principal Investigator for multiple research initiatives including LUCC (Lund University Cancer Centre), EpiHealth, and StemTherapy. She leads the Molecular Evolution research team and specializes in interdisciplinary studies bridging geobiology and cancer research. Her research focuses on understanding evolutionary mechanisms through tumor modeling, geobiological signatures in the fossil record, and hypoxia dynamics in cancer. Key interests include: Animal evolution and diversification drivers Tissue maintenance mechanisms in malignant/non-transformed tissues Chemical fingerprinting of early-stage cancers Stem cell dynamics in evolutionary contexts Her publications demonstrate strong interdisciplinary trends, with recent works spanning geobiology (Neoproterozoic oxygen variability), cancer biophysics (hypoxia sensing in metastasis), bioinformatics (missing data analysis), and origins of life research. This reflects her unique approach to studying evolutionary principles through both geological and biomedical lenses. Awards include: Jan Bergström Young Geoscientist Award (2014) She leads 10 active research projects with grants from the Swedish Research Council, Sjöberg Foundation, and Johns Hopkins University, focusing on isotopic signatures of cancer, elemental fingerprinting of tumors, and evolutionary oncology. Hammarlund manages research teams at the Molecular Evolution lab and contributes to national stem cell initiatives, frequently organizing academic workshops like UniStem Day 2024.

Laura Carrollمشاهده پروفایل

استادیار

• Computational Microbiology
• Pathogen Genomics
• Bioinformatics
• +۱ مورد دیگر

Laura Carroll is an Assistant Professor in Clinical Microbiology at Umeå University and a SciLifeLab & Wallenberg Data-Driven Life Science researcher. Her lab develops bioinformatic approaches for pathogen surveillance, combining genomic epidemiology with machine learning for outbreak detection and antimicrobial resistance prediction. Research focus areas: Phylodynamic modeling of zoonotic pathogens Machine learning for phenotype prediction Multi-omics approaches for virulence assessment Accessible computational methods for microbiologists

David Källbergمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

David Källberg is an Associate Professor at the School of Business, Economics and Statistics (USBE), Umeå University , specializing in Statistics . His research focuses on statistical methods for high-dimensional data analysis, entropy estimation, and mixture modeling. Research Interests: David's work bridges statistical theory and practical applications in genomics and machine learning. Key areas include: Entropy balancing for causal effect estimation Feature selection in high-dimensional clustering (e.g., RNA-seq data) Mixture models for biomedical applications Nonparametric methods for entropy-type functionals Image matching algorithms in large databases Integration of epigenetic and clinicopathological data Collaborations: He collaborates with researchers like Patrik Rydén, Linda Vidman, and Ingeborg Waernbaum, contributing to journals such as Frontiers in Genetics , PLOS ONE , and Lecture Notes in Computer Science . He is also a member of the Stat4Reg research group.

Filip Edströmمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +۳ مورد دیگر

Filip Edström is a PhD student in Statistics at the Umeå School of Business, Economics and Statistics (USBE), Umeå University. He is affiliated with the Centre for Transdiciplinary AI. His research focuses on causal models and their applications in AI, particularly addressing fairness in machine learning and synthetic data generation. Education: Current PhD candidate in Statistics at USBE. Previous academic background not explicitly detailed in provided texts. Research Interests: Explores causal inference frameworks within AI systems, emphasizing ethical considerations like algorithmic fairness. Engages with synthetic data methodologies to enhance AI robustness and transparency. Collaborates on projects such as ROCC – Robots with Causal Capabilities (2023–2026), aiming to integrate causal reasoning into robotics. Teaching: Serves as a teaching assistant for Statistik A1 and instructor for Data Analytics with R at Umeå University. Labs/Teams: Active member of the Centre for Transdiciplinary AI, fostering interdisciplinary AI research.

Joakim Wallmarkمشاهده پروفایل

پژوهشگر ارشد

• Item Response Theory
• Psychometrics
• Educational Statistics
• +۵ مورد دیگر

Joakim Wallmark is a Postdoctoral Fellow at Umeå School of Business, Economics and Statistics , affiliated with the Department of Statistics at Umeå University. His work focuses on psychometrics and statistical analysis. His research specializes in Item Response Theory (IRT) , including applications to clinical rating scales, polytomous test data, and kernel equating methods. He has developed the IRTorch Python package for IRT modeling and explored flexible monotone multiple-choice models. Publications highlight his contributions to test data analysis , with a focus on improving statistical efficiency and equating techniques for mixed-format assessments. His collaborations include researchers like James O. Ramsay, Juan Li, Maria Josefsson, and Marie Wiberg.