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Oskar Kviman is a doctoral student at KTH Royal Institute of Technology working in the Lagergren Lab within the Division of Computational Science and Technology. His research bridges machine learning, statistics, and computational biology with a focus on developing and applying advanced probabilistic methods. His primary research interests include: Bayesian phylogenetics and probabilistic machine learning Variational inference, variational auto-encoders, and sequential Monte Carlo methods Generative AI techniques including flow matching, Schrödinger bridges, and diffusion models Computational cancer research focusing on differential expression testing and spatial transcriptomics Kviman's publication record demonstrates significant contributions to variational inference methodology, particularly in phylogenetics and generative modeling. His work spans top machine learning conferences including ICML, NeurIPS, and AISTATS, showing consistent development of techniques that improve efficiency and accuracy in probabilistic modeling. Recent publications focus on multi-marginal flow matching, variational resampling, and mixture learning in black-box variational inference. He has been recognized for his peer review contributions as a Top reviewer (10%) for AISTATS 2023. Kviman has supervised master's theses for Xindi Liu and Ricky Molén at KTH and serves as a lecturer for 'Statistical Methods in Applied Computer Science' since 2021, while previously working as a teaching assistant for 'Machine Learning, Advanced Course' and 'Deep Learning, Advanced Course'.

• Bayesian phylogenetics
• Probabilistic machine learning
• Variational inference
• +15 more

Gergely Ács is an Associate Professor at Budapest University of Technology and Economics (BME), affiliated with the CrySyS Lab. He holds a PhD and MSc in Computer Science from BME, and previously served as a post-doctoral researcher and research engineer at INRIA's Privatics Team in France. His research focuses on data privacy, security, and machine learning applications in these domains. He has contributed to federated learning frameworks, privacy-preserving techniques for healthcare and IoT, and secure aggregation methods. Dr. Ács teaches courses on privacy technologies and security in machine learning at BME. He has received the Pollák-Virág Award and authored over 50 publications in top-tier conferences/journals. His work spans industries, including drug discovery collaborations and automotive cybersecurity. Education: PhD in Computer Science, BME (2009) MSc in Computer Science, BME (2005) Research Interests: Federated Learning, Differential Privacy, IoT Security, Machine Learning Security, Privacy-Preserving Technologies. Key Projects: MELLODDY (cross-pharma federated learning), SIMBIoTA-ML (IoT malware detection), Privacy-Preserving Smart Metering.

• Data Privacy
• Cybersecurity
• Machine Learning
• +3 more

Szabó Sándor is an academic affiliated with the Department of Network Systems and Services at the Budapest University of Technology and Economics (BME). His primary roles include research in telecommunications, sensor networks, and wireless communication systems. He has been active in the Department of Network Systems and Services since 2013 and is also associated with the University Research and Innovation Center ÓE EKIK since 2022. His research focuses on optimizing wireless sensor networks (WSN), energy efficiency in communication systems, and applying advanced technologies like computer vision and machine learning to agricultural challenges, such as livestock monitoring and poultry farming automation. He collaborates frequently with institutions like the University of Bologna and IEEE conferences. Notable contributions include studies on Bluetooth Low Energy networks, payload aggregation techniques, and edge/cloud architecture applicability in farming systems. His work emphasizes practical solutions for real-world problems in both telecommunications and agricultural technology.

• Telecommunication Engineering
• Sensor Networks
• Wireless Communication
• +3 more

Wei Ouyang is an Assistant Professor in the Department of Applied Physics at KTH Royal Institute of Technology. He leads the AICell Lab , funded by the Data-Driven Life Science Fellow program, focusing on developing AI systems for data-driven cell and molecular biology. The lab's work emphasizes integrating advanced computational tools with biological imaging to advance understanding of cellular processes. His research interests include bioimaging analysis , deep learning applications , and cloud-optimized data formats . Notable contributions include open-source platforms like ImJoy and Pycro-Manager , which enhance accessibility and reproducibility in microscopy and bioinformatics. Recent publications highlight innovations in AI-driven microscopy tools , automated imaging frameworks , and data-sharing platforms . Key projects include the ESPressoscope for portable microscopy and the BioImage.IO Chatbot for community-driven bioimaging support. Dr. Ouyang teaches the Data-driven Life Sciences course at KTH and oversees the Degree Project in Applied Physics . His work bridges computational methods with biological discovery, fostering collaborative advancements in life sciences.

• AI in Biology
• Bioimaging Analysis
• Data-Driven Science
• +3 more

Rickard Armiento is an Associate Professor and Head of the Materials Design and Informatics unit at Linköping University's Department of Physics, Chemistry and Biology (IFM). His research focuses on computational methods, AI, and materials design, particularly in semiconductor defects, quantum materials, and high-throughput modeling. He leads the OPTIMADE initiative for materials data standardization and develops tools like ADAQ for defect analysis. His work integrates theory and informatics to discover new materials for quantum technologies and energy applications. Recent research includes predicting magnetic properties in alloys, automated symmetry analysis of defects, and AI-driven crystal structure prediction via models like WyckoffDiff and WREN. Armiento co-organized the OMDI 2021 workshop on materials data interoperability and contributes to open science through ontology development (e.g., materials design ontology, units of measure ontology). His group’s tools and APIs enable global collaboration in materials discovery.

• Materials Science
• Theoretical Physics
• Artificial Intelligence
• +4 more

Guillem Domènech-Gil is an Associate Professor at Linköping University's Department of Thematic Studies. His research focuses on applying machine learning to sensor technologies for environmental and industrial applications. Key areas include optimizing low-cost chemical sensors under challenging conditions, methane monitoring, and electronic nose systems for forensic and food safety purposes. His work bridges computer science, engineering, and environmental science, aiming to enhance sensor reliability and efficiency. Notable projects include the Next-2Digits initiative, which integrates 2D materials into photonic circuits for advanced optical sensors. Recent contributions address sensor performance in humid environments and methane detection, leveraging AI-driven methods. Collaborations involve institutions like the Swedish University of Agricultural Sciences and environmental agencies. Domènech-Gil's research has practical implications for forensic investigations (e.g., electronic noses replacing cadaver dogs) and sustainable environmental monitoring.

• Machine Learning
• Environmental Science
• Sensor Technology
• +3 more

Mohammad Rajiullah is a Senior Lecturer in Computer Science at Karlstad University, Sweden. His research focuses on low latency networking, 5G/6G systems, satellite internet integration, and IoT optimization. He leads major EU initiatives including the MAGDALENA (6G-SANDBOX) project (Principal Investigator) evaluating LEO satellite-5G integration, and the XR health training use case in the 6G-PATH project for immersive medical education. Currently PI of the EU IMAGINE-B5G SIMONE project developing AR/VR platforms over 5G/6G. Active in national projects like DRIVE (8-year initiative for latency-sensitive mobile services) and international collaborations such as the 5G-DiGITs curriculum development. His work spans over 50 peer-reviewed publications, with recent focus on Starlink performance analysis, NB-IoT optimization, and 5G non-standalone network evaluations. He maintains the CARL-W testbed for hybrid 5G/satellite testing. Teaching includes core courses in Operating Systems, Computer Networking, and IoT. His research emphasizes cross-disciplinary projects involving academia-industry partnerships across Europe and Asia. He has contributed to EU deliverables like 6G-SANDBOX D6.3 and NEAT D3.1 , showcasing leadership in collaborative network research.

• Low Latency Networking
• 5G/6G Networks
• Satellite Internet
• +7 more

Eddie Wadbro is a Professor of Mathematics at Karlstad University , Sweden. His research focuses on design optimization , inverse problems , and large-scale computational methods , with applications in acoustic device design , fluid dynamics , and mechanical engineering . He leads projects in topology optimization of antennas, loudspeakers, and acoustic systems, often involving multi-physics simulations. Key contributions include: Development of minimum-size control techniques for topology optimization Investigation of waveguide acoustic black holes for enhanced wave focusing Optimization of superhydrophobic surfaces to delay turbulent transition in boundary layers Integration of deep reinforcement learning for real-world control systems His work bridges applied mathematics and engineering , with publications in high-impact journals and collaborations across disciplines like computational fluid dynamics and machine learning.

• Topology Optimization
• Inverse Problems
• Large-Scale Optimization
• +7 more

Mikael Lundbäck is a Researcher and Postdoctoral Fellow at the Department of Physics, Umeå University, affiliated with the UMIT Research Lab. His work focuses on automation, machine learning applications, and sustainable forest operations. Current projects include the Mistra Digital Forest initiative (2019–2026) and contributions to the Technodiversity e-learning platform for harmonizing forest engineering education across Europe. Research interests include optimizing harvesting systems, tele-extraction technologies, and autonomous machinery through data-driven approaches. He collaborates with institutions like the Faculty of Forestry and Wood Technology, Zagreb University, and contributes to conferences such as IUFRO World Congress. His publications analyze productivity, cost efficiency, and terrain adaptability in forestry machinery, with a focus on deep learning, simulation models, and open data sharing. Lundbäck leads the Digital Physics group and actively participates in COFE, FORMEC, and IUFRO research networks. His work bridges technical innovation with practical forestry challenges, emphasizing global trends and economic feasibility.

• Forest Engineering
• Automation in Forestry
• Machine Learning Applications
• +3 more

Guocai Chai is an Adjunct Professor in Engineering Materials at Linköping University, affiliated with the Department of Management and Engineering (IEI) and the Division of Engineering Materials (KMAT). He has held this position since 2011 and continues to contribute to research in mechanical properties of structural materials. His research focuses on mechanical properties of metallic materials , particularly fatigue behavior, deformation mechanisms, and microstructural analysis. He investigates how materials respond under extreme conditions such as very high cycle fatigue, cryogenic environments, and pre-straining, with applications in aerospace and oil and gas sectors. The recent publications (2024–2025) reveal a strong trend in combining experimental mechanics with advanced computational techniques , including deep learning for fatigue life prediction and multi-scale simulations of strain hardening. His work frequently involves nickel-based superalloys and high-strength aluminum alloys, targeting industrial durability and safety challenges. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While no information is available about students or grants in the provided content, his active publication record suggests ongoing research supervision and likely involvement in funded projects within materials engineering at Linköping University. Labs and Research Teams: Guocai Chai is part of the Engineering Materials (KMAT) division, which conducts interdisciplinary research on metallic materials, ceramics, composites, and polymers. The group emphasizes experimental testing coupled with microstructural investigations to understand material performance in engineering applications.

• Mechanical Properties of Structural Materials
• Fatigue and Fracture
• Deformation Mechanisms
• +4 more

Iulian Emil Tampu is a Postdoctoral Researcher at Linköping University, Department of Biomedical Engineering, and a member of the Center for Medical Image Science and Visualization (CMIV) and the Analytic Imaging Diagnostics Arena (AIDA) research school. His work bridges biomedical optics, image science, and machine learning to advance diagnostic tools in oncology. Research Interests: Iulian's research centers on developing and applying deep learning methods to integrate multi-modal medical imaging data—including MRI, optical imaging, and digital histopathology—for improved diagnosis of diseases such as pediatric brain tumors. His work emphasizes early and accurate detection through fusion techniques and automated analysis. Recent Publication Trends: His recent publications (2024–2025) highlight a strong focus on deep learning applications in pediatric neuro-oncology, particularly using H&E and IHC histology images, MR imaging with age fusion, and multi-center validation of classification models. These works reflect a trend toward clinically deployable AI tools using real-world, heterogeneous data sources. Scientific Affiliations: Center for Medical Image Science and Visualization (CMIV) Analytic Imaging Diagnostics Arena (AIDA) LiU Cancer Collaborations and Advising: He collaborates with senior researchers including Neda Haj-Hosseini, Anders Eklund, and Per Nyman. While no formal students are listed, his collaborative projects involve interdisciplinary teams across imaging, oncology, and AI. No grants or formal advising roles are mentioned in the text. Laboratories and Research Groups: Iulian is embedded in the Biomedical Image Science and Biomedical Optics research environments at Linköping University, contributing to national and clinical translation efforts in medical imaging.

• Biomedical Image Analysis
• Deep Learning
• Medical Imaging
• +5 more

Ankica Babic is a Professor at Linköping University, affiliated with the Department of Biomedical Engineering (IMT) and the Division of Biomedical Engineering (MT). Her work bridges clinical informatics and biomedical engineering, focusing on improving healthcare through advanced information systems and intelligent medical technologies. Her research interests include Clinical Informatics , Biomedical Engineering , Information Systems , and Neuroengineering , with a strong emphasis on AI-driven solutions for medical diagnostics. She actively contributes to the development of digital health platforms and smart monitoring tools, particularly in cardiology and ophthalmology. The recent publications highlight a consistent trend in leveraging deep learning and mobile technologies for non-invasive patient monitoring, especially in heart sound analysis and self-monitoring systems. Her work demonstrates a commitment to translating data-driven innovations into practical clinical applications. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students advised or research grants received. However, her active publication record suggests involvement in funded research projects and collaboration with clinical and engineering teams. Labs and Teams: She is part of the Division of Biomedical Engineering (MT) at Linköping University, a national hub for research and innovation in biomedical engineering, contributing to interdisciplinary efforts in health technology and informatics.

• Clinical Informatics
• Biomedical Engineering
• Information Systems
• +5 more

Gergely Katona is a Professor at the Department of Chemistry & Molecular Biology, University of Gothenburg. His research focuses on structural biology, particularly crystallographic investigations of protein dynamics, and he integrates artificial intelligence (AI) into biophysical data analysis. Chairman of the Chemistry Program Committee Main examiner for Chemistry programs Teaches courses like KEM350 and KEM360 Katona's research explores enzyme mechanisms using X-ray techniques and AI, with applications in cancer biology and autoimmune diseases. He pioneered methods to study reacting enzymes via chemical triggering and Bayesian analysis of MST/BLI/NMR data. His recent work combines structural biology with machine learning to predict protein interactions and mutation fitness, as seen in his Survivin studies. He holds a patent for peptide-targeting technology. Katona supervises doctoral, master, and bachelor students, emphasizing method development and independent research skills. He reviews applications for EMBL beamlines and the iNEXT network.

• Structural Biology
• Crystallography
• Enzyme Dynamics
• +5 more

Viktor Kaldo is a Professor at Linnaeus University and an affiliated researcher at Karolinska Institutet. His work focuses on Internet-delivered Cognitive Behavioral Therapy (ICBT) , particularly for insomnia, depression, and anxiety . He leads the Psychological Interventions research group at Karolinska, which emphasizes innovation, improvement, and implementation of digital mental health solutions. The group has conducted numerous randomized controlled trials (RCTs) and health economic analyses to evaluate ICBT efficacy across conditions like ADHD, chronic pain, and Parkinson’s disease . Education: Docent, Karolinska Institutet (2013). His research integrates machine learning to predict treatment outcomes and optimize precision medicine in mental health. Key collaborations include the Internet Psychiatry Unit and Psykiatri Sydväst . Notable scientific achievements include the Docent title (2013) and contributions to understanding sleep disorders, digital interventions, and their clinical applications. The group’s work has expanded to traditional CBT evaluation and implementation strategies in primary care and psychiatric settings . Viktor’s publications highlight the role of data homogeneity , organizational models , and comorbid condition treatments in digital mental health. He also explores the interplay between suicidal ideation and therapy outcomes , alongside digital tools for stress and ADHD management . Scientific Awards: Docent, Karolinska Institutet (2013). Grants and Collaborations: His team has contributed to large-scale projects like the KAIROS trial and MULTI-PSYCH cohort , emphasizing implementation science and multi-modal data analysis. Collaborations span clinics, primary care centers, and international institutions, focusing on digital mental health dissemination . Labs and Teams: Leads the Psychological Interventions - Innovation, Improvement, and Implementation group at Karolinska, which works closely with the Internet Psychiatry Unit. The group’s expertise in sleep problems and ICBT has informed national and international guidelines.

• Psychiatry
• Clinical Psychology
• Cognitive Behavioral Therapy
• +7 more

Alessandro Vittorio Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the University of Málaga's Institute for Software Technology and Software Engineering. His research focuses on modeling and control of interconnected systems under uncertainty, integrating Control Theory with Embedded Systems , Distributed Systems , and Real-Time Systems . He has served as a Scientific Advisor for companies like ABB and Zero Point Technologies. Education: PhD from Politecnico di Milano (2013), supervised by Alberto Leva; Postdoctoral work at Politecnico di Milano and Lund University's Department of Automatic Control under Karl-Erik Årzén. Research Trends: His articles emphasize Swarm Robotics , Stochastic Scheduling , Privacy-Preserving Control , and Edge-Cloud Continuum . Key subfields include Distributed Control , Uncertainty Modeling , Real-Time Optimization , and Secure Communication . Awards: He received the Most Influential Paper Award at SEAMS 2025, QUALIFICA Fellowship , and Outstanding Paper Award at ECRTS 2021, alongside a Best Artefact Award in 2017. Advising: Supervised PhD theses by Anna Friebe and Daniel Bujosa Mateu. Labs & Collaborations: Affiliated with the Lund Center for Control of Complex Engineering Systems (LCCC) and MDU's research groups.

• Control Theory
• Embedded Systems
• Distributed Systems
• +6 more