جستجوی استاد راهنمای دانشگاهی

Andrea Fossatiمشاهده پروفایل

استادیار

• Bacterial Defense Systems
• Phage Biology
• Virology
• +۵ مورد دیگر

Andrea Fossati is a DDLS Fellow and Principal Investigator at Karolinska Institutet, leading the Fossati Lab at SciLifeLab. His research focuses on bacterial defense systems and phage biology, utilizing interaction proteomics and machine learning to develop novel strategies against antibiotic-resistant bacteria by disabling bacterial immunity during phage-bacterial warfare. Dr. Fossati's work centers on discovering bacterial defense mechanisms and phage counter-defense systems through advanced proteomic techniques like DIP-MS and quantitative interaction mapping. His lab integrates systems biology and machine learning to analyze host-pathogen interactions, with emphasis on jumbo phage infection mechanisms and lipid compartment formation. This research directly targets the enhancement of phage therapy efficacy by tilting the evolutionary balance toward phages. His publication trends reveal deep specialization in virology and microbiology, with dominant themes in bacterial immunity, phage-bacteria interactions, and proteomic methodology development. Over 80% of recent work involves jumbo phages and defense system characterization, while computational frameworks like PCprophet demonstrate cross-disciplinary integration of machine learning. Scientific awards: DDLS Fellow Dr. Fossati actively mentors the next generation of scientists, currently supervising two PhD students and collaborating with three postdoctoral researchers. His DDLS fellowship provides substantial research funding enabling cutting-edge instrumentation for proteomic analysis and high-throughput screening of phage-bacterial interactions, with recent grants focusing on quantitative interaction mapping and defense system discovery. The Fossati Lab operates within SciLifeLab's national infrastructure, maintaining close collaborations with leading proteomics and virology groups across Europe. The team specializes in next-generation interaction proteomics, developing novel methodologies for complex deconvolution while maintaining strong computational biology capabilities for data analysis and machine learning applications in host-pathogen systems.

Yonghao Xuمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +۴ مورد دیگر

Yonghao Xu is an Assistant Professor at the Department of Electrical Engineering , Linköping University , and affiliated with the Computer Vision Laboratory (CVL) and the Wallenberg Autonomous Systems Program (WASP) . His research bridges remote sensing , machine learning , and AI security . Research Trends Xu's recent publications focus on adversarial attacks and defenses in remote sensing, domain adaptation for semantic segmentation, and benchmark dataset creation (e.g., Sen2Fire). His work addresses challenges in urban sustainability , geospatial data analysis , and deep learning robustness . Labs & Programs He is associated with the Computer Vision Laboratory (CVL) , contributing to autonomous systems through the Wallenberg Autonomous Systems Program (WASP) , a major Swedish initiative in AI and robotics.

• Nanophotonics
• Optical Tweezers
• Active Matter
• +۴ مورد دیگر

Giovanni Volpe is a Professor at the Department of Physics, University of Gothenburg. His research focuses on nanophotonics, optical tweezers, active matter, and machine learning applications in microscopy and neurodegenerative diseases. He leads the Soft Matter Lab (http://softmatterlab.org/) and collaborates internationally in interdisciplinary projects. His work bridges physics, biology, and AI, addressing challenges in nanoparticle manipulation, biomedical imaging, and neurodegenerative disease modeling. Key research areas include optical trapping of nanoparticles, self-assembly of colloidal systems, and leveraging deep learning for microscopy and sensor technologies. Recent publications span neurodegenerative disease pathways, advanced optical microscopy techniques, and plasmonic sensor development. Volpe's team has pioneered methods in computational neuroscience and nanotechnology. His lab's innovations include novel optical tweezers control algorithms and neural network models for analyzing complex biological systems. Collaborations span materials science, biophysics, and cognitive neuroscience, with applications in healthcare and environmental monitoring.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Signal Processing
• Wireless Communications
• MIMO Communications
• +۴ مورد دیگر

Joakim Jaldén is a Professor at the Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He holds a Ph.D. in Electrical Engineering from KTH (2007) and completed post-doctoral studies at Vienna University of Technology (2007-2009). With affiliations at Stanford University and ETH Zürich, his academic journey reflects global expertise. 2002: M.Sc. in Electrical Engineering, KTH 2007: Ph.D. in Electrical Engineering, KTH 2007-2009: Post-Doctoral Researcher, Vienna University of Technology Jaldén's research spans Signal Processing , Wireless Communications , and Biomedical Data Analysis . He pioneered MIMO communications and later developed ELISpot/FluoroSpot analysis algorithms commercialized by Mabtech AB. His work on cell migration tracking (IEEE ISBI 2012) and distributed optimization (ECO-PANDA method) demonstrates interdisciplinary impact. Key publication trends include Hidden Markov Models for DNA sequencing, Reinforcement Learning in communication systems, and Low-Complexity Beamforming for MU-MIMO networks. His 2024 work on mmWave MIMO beam coherence showcases continued leadership in wireless channel modeling. Scientific recognition includes: IEEE Signal Processing Society 2006 Young Author Best Paper Award Ingvar Carlsson Career Award 2009 (Swedish Foundation for Strategic Research) IEEE ISBI 2012 Best Paper Award Bitplane Awards (2013-2015) for cell tracking challenges As Program Director of KTH's 5-year Electrical Engineering Degree Program (CELTE) since 2016 and Vice-Chair of EECS Faculty Board , Jaldén leads academic initiatives. His collaborations with industry (e.g., Mabtech AB) and roles as examiner for advanced courses in communication systems highlight his educational impact.

• Game Theory
• Control Theory
• Cybersecurity
• +۵ مورد دیگر

Kim Hammar is a postdoctoral researcher at KTH Royal Institute of Technology, with affiliations at the University of Melbourne (2025-2028) and Imperial College London. He works under Prof. Tansu Alpcan and Prof. Emil Lupu, focusing on the intersection of game theory, control theory, and large-scale systems for networking and security applications. Previously, he completed his Ph.D. at KTH under Prof. Rolf Stadler and Prof. Pontus Johnson. His research spans cybersecurity, networked systems, and adaptive control mechanisms. Key contributions include applying optimal stopping reinforcement learning conjectural online learning causal modeling to intrusion response and network security. His 2025-2024 publications highlight advancements in automated security through game-theoretic and control-theoretic approaches, with a focus on dynamic environments. Kim received the VR International Postdoctoral Fellowship in 2025. He has served as an assistant for Computer Networks (EP111U) Computer Systems (EP121U) at KTH.

• Program Verification
• Proof Engineering
• Formal Methods
• +۴ مورد دیگر

Karl Palmskog is a Lecturer at KTH Royal Institute of Technology in the Division of Theoretical Computer Science and the STEP research group. His work focuses on program verification and proof engineering, with particular emphasis on developing techniques and tools based on proof assistants for constructing functionally correct and secure software systems. Palmskog received his Ph.D. in Computer Science in 2014 from KTH, advised by Mads Dam, and his M.Sc. in Computer Science and Engineering from KTH in 2007. Prior to his current position, he was a postdoc at The University of Texas at Austin and University of Illinois at Urbana-Champaign. His research interests span programming languages, software engineering, and formal verification, with a particular focus on developing techniques and tools based on proof assistants. He is an avid user of the Coq proof assistant for both proving and programming, often complemented by OCaml, and also utilizes HOL4 and other ML family dialects. His work bridges theoretical foundations with practical applications, particularly in the domains of blockchain systems, distributed systems, and automotive software verification. Analysis of his recent publications reveals a strong focus on Coq-based verification, with significant contributions to proof engineering tools and methodologies. His work includes developing tools for regression proving, change impact analysis, mutation testing for Coq projects, and lemma name suggestion using deep learning. There's also a growing trend toward applying formal methods to real-world systems like blockchain protocols and automotive software. Palmskog has been involved in several research projects, including Coq-community Proof Engineering and Distributed Components. His past projects include Trustfull (SSF), Model-based Event Driven Scalable Programming for the Mobile Cloud (NSF), Highly Adaptable and Trustworthy Software (EU FP7), and 4WARD Future Internet (EU FP7). As an educator, Palmskog has served as examiner, course responsible, teacher, and assistant for various courses including Algorithms, Data Structures and Complexity; Degree Projects; Game Theory; Parallel and Distributed Computing; and Programming Paradigms. His work on Chip, a Coq formalization of change impact analysis, demonstrates his commitment to creating practical, certified tools that bridge formal methods with software engineering practice.

• Medical Imaging
• Cardiovascular Imaging
• Cardiology
• +۳ مورد دیگر

Markus Henningsson serves as an Adjunct University Lecturer at Linköping University's Department of Health, Medicine and Care (HMV) and Department of Diagnostics and Specialist Medicine (DISP). His academic work centers on advanced medical imaging techniques with particular emphasis on cardiovascular applications. His research interests span Medical Imaging , Cardiovascular Imaging , and Artificial Intelligence in Medicine . Dr. Henningsson specializes in developing and applying novel MRI techniques for cardiac analysis, particularly focusing on left atrial structure, epicardial adipose tissue, and fibrosis segmentation. His work bridges engineering methodologies with clinical cardiology applications. Analysis of his recent publications reveals a strong trend toward integrating machine learning with medical imaging, particularly using deep learning architectures like geometric UNet for tissue segmentation. His research consistently addresses clinically relevant questions in atrial fibrillation and cardiovascular disease assessment through advanced imaging biomarkers. While no formal scientific awards are documented in the available information, his work appears in reputable journals including Scientific Reports , Physics in Medicine and Biology , and Magnetic Resonance in Medicine . His collaborative approach is evident through numerous co-authorships with researchers across cardiovascular sciences. Dr. Henningsson's work appears connected to Linköping University's Cardiovascular Sciences Unit, which conducts research in cardiovascular physiology, cardiology, and related surgical disciplines. His contributions support the department's focus on both basic and clinical cardiovascular research with direct patient care applications.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

Vladimir Vlassovمشاهده پروفایل

استاد

• Machine Learning
• Data-intensive computing
• Cloud computing
• +۷ مورد دیگر

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

• Cloud Computing
• Distributed Computing
• Parallel Computing
• +۱۰ مورد دیگر

Professor Javid Taheri is a leading academic at Karlstad University (2019–present), previously serving as Associate Professor (2015–2019) and Senior Lecturer (2015). His research focuses on cloud computing, edge computing, distributed systems, and AI-driven networking. He holds a Ph.D. in Information Technologies from The University of Sydney (2007) and an M.Sc./B.Sc. in Electrical Engineering from Sharif University of Technology (2000/1998). Research interests include cloud-edge continuum systems , resource optimization , 5G/6G networking , and AI for IoT . Notable contributions include frameworks like PerfSim (microservice performance simulation) and MultiScaler (auto-scaling for cloud applications). Publications highlight innovations in edge computing optimization, security for distributed systems, and machine learning for resource management. He has co-authored over 150 papers across top venues like IEEE Transactions and ACM conferences. Academic leadership includes roles as conference chair (IC2E 2023) and editorial work for journals on cloud and edge computing.

Kevin Kammمشاهده پروفایل

دانشیار

• Financial Mathematics
• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• +۸ مورد دیگر

Kevin Kamm is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research spans stochastic analysis , financial mathematics , commodities , and machine learning , with a focus on optimal strategies and SPDEs. He has developed innovative approaches using Deep Learning and stochastic Magnus expansion for financial models. Education : Mathematics studies at Technische Universität Berlin; Ph.D. in financial mathematics at the University of Bologna under the ABC-EU-XVA project. Research : Specializes in negative interest rate frameworks, rating triggers for XVA adjustments, and HPC applications in SPDEs. Collaborates on aquaculture valuation models incorporating biological and feeding cost risks. Publications : 15+ recent works cover CIR model extensions, XVA calibration, SPDE numerical methods, and machine learning applications in financial mathematics and commodities. Teaching : Instructs courses in Financial Mathematics and Stochastic Differential Equations , supervising Master's theses in areas like stress testing and mortgage-backed securities. Affiliations : Member of Umeå University's Mathematical Finance and Economics and Mathematical Modeling and Analysis research groups.

Martin Servinمشاهده پروفایل

دانشیار

• Digital Physics
• Computational Modelling
• Robotics
• +۳ مورد دیگر

Martin Servin is an Associate Professor at the Department of Physics, Umeå University, and leads the Digital Physics research group within the UMIT Research Lab. His work focuses on computational modeling and simulation of granular materials, robots, and vehicles, with applications in AI-based control and perception. He holds a doctoral degree from Umeå University (2003) and has pioneered research in real-time physics simulation, particularly in the context of autonomous machinery and off-road robotics. Research Interests : Digital physics, granular materials simulation, autonomous systems, reinforcement learning, and simulation-to-reality transfer. His group develops advanced simulation tools for industries like forestry, mining, and construction. Key Projects : Mistra Digital Forest (2019–2026) AILUR (Digital Twin for AI-controlled Lunar Robotics) XSCAVE (Explainable, Safe Control for Heavy Machinery) Publications emphasize simulation methodologies, AI integration, and real-world validation across robotics, vehicle dynamics, and granular mechanics. Notable contributions include work on wheel loader dynamics, deep reinforcement learning for control systems, and terrain modeling. Awards include the Spin-off award for industry-grade physics in Unreal Engine (2018) , recognizing his role in Algoryx Simulations, a spin-off company commercializing his research. Labs/Teams : UMIT Research Lab, Digital Physics Group, and collaborations with Algoryx Simulations.

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.