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Zhonghai LuView profile

Professor

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +7 more

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

• Mobile Robotics
• Underwater Robotics
• Autonomous Systems
• +4 more

John Folkesson is an Associate Professor at the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology. His research focuses on mobile robotics, underwater autonomous vehicles (AUVs), and Simultaneous Localization and Mapping (SLAM), particularly addressing challenges in dynamic underwater environments. He leads the AUV group within the Swedish Maritime Robotics Centre (SMaRC2.0) and supervises multiple PhD projects, including those funded by Ocean Infinity and Vinnova. Folkesson has pioneered work on sonar-based SLAM, bathymetric mapping, and autonomous underwater navigation without human intervention. He teaches courses such as Probabilistic Graphical Models (DD2420) and Applied Estimation (EL2320). Recent projects include developing neural rendering techniques for sidescan SLAM and automatic launch systems for AUVs in collaboration with Purdue University and SAAB. His research emphasizes long-term autonomy, environmental ambiguity, and sensor data interpretation in unstructured underwater scenarios. Education: PhD in Robotics (2005, KTH Royal Institute of Technology) Recent Funding: 2024 projects include ALARS (Vinnova), WASP WARA-PS, and industrial collaborations. Research Interests Folkesson's work spans underwater robotics, SLAM algorithms, and sensor fusion. Key areas include: Underwater SLAM and sonar modeling Bathymetric reconstruction using neural networks Autonomous decision-making in AUV missions Real-time terrain modeling and localization Articles Trends Recent publications emphasize neural networks for SLAM optimization, sonar data processing, and autonomous underwater systems. Themes include real-time bathymetric mapping, sensor fusion in dynamic environments, and neural rendering techniques for improving navigation accuracy. Folkesson's work bridges theory and practice, with applications in marine robotics and industrial surveys. Advising & Grants PhD supervision: AUV perception (2024), SLAM with Ocean Infinity, event-response AUV systems. Collaborations: Purdue University, SAAB, Ocean Infinity. Course responsibilities: Over 10 advanced robotics and engineering courses at KTH. Labs & Teams Lead of SMaRC2.0, KTH's official research center for maritime robotics. Active in developing AUV systems for long-duration missions, including ice-covered and deep-sea exploration.

Gustav HenterView profile

Assistant Professor

• Deep Generative Models
• Speech Synthesis
• 3D Character Animation
• +3 more

Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.

Jonas BeskowView profile

Professor

• Multimodal Interaction
• Speech Synthesis
• Robotics
• +3 more

Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.

Saghi HajisharifView profile

Researcher

• Visual Machine Learning
• HDR Imaging
• Photo-realistic Rendering
• +5 more

Saghi Hajisharif is a Researcher at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) group. She holds a PhD in Visualization and Media Technology from Linköping University (2020), an MSc in Advanced Computer Graphics (2013), and a BSc in Computer Science from Amirkabir University (2009). Her work focuses on computational imaging, visual machine learning, HDR imaging, and light field technologies. She is a core member of the Computer Graphics and Image Processing research group. Research interests include sparse representation learning for computational imaging, synthetic data ethics, BRDF material modeling, and algorithmic fairness in AI. Her contributions span interdisciplinary projects recognized in IVA’s 100 List (2024), highlighting societal impact potential. She has co-authored influential studies on topics such as FROST-BRDF sampling techniques and metadata standards for GenAI synthetic data. Her articles reflect expertise in computer vision, graphics, and AI ethics, with key contributions to light field imaging, GAN fairness, and material modeling surveys. The IVA’s 100 List recognition underscores her innovative work’s societal relevance. She collaborates across disciplines to advance imaging technologies and ethical AI practices.

Martin MagnussonView profile

Professor

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +5 more

Martin Magnusson is a Professor at the Department of Natural Sciences and Technology, Örebro University, leading the Center for Applied Autonomous Sensor Systems (AASS) and the Robot Navigation and Perception Lab . His research focuses on robotics and artificial intelligence , particularly 3D mapping, localization, radar-based navigation, and human-robot interaction . Email: martin.magnusson@oru.se Phone: +46 19 303870 Location: Room T1222 His work addresses fundamental challenges in achieving robust autonomy through innovations like the 3D Normal Distributions Transform (3D-NDT) and methods for scan registration in dynamic environments. Recent research extends to radar-based navigation and heterogeneous map data integration , with ethical implications regarding military applications of autonomous systems. Key research themes include: Autonomous Perception: Radar and lidar sensor fusion for localization Dynamic Mapping: Flow-aware and quality-assessed environmental models Human-Aware Robotics: Predictive modeling for safe shared-space navigation Professor Magnusson teaches Computer Graphics , connecting academic principles (e.g., ray tracing, light scattering) to applied research in radar simulation models and neural rendering . His research projects span DARKO (agile production robots), NiCE (changing environment navigation), and Radarize (underground autonomous vehicles).

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +4 more

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

Beáta MegyesiView profile

Professor

• Digital Humanities
• Historical Cryptology
• Natural Language Processing
• +3 more

Beáta Megyesi is a Professor of Computational Linguistics at Uppsala University's Department of Linguistics and Philology, currently on leave from August 2023 to December 2025. She holds a PhD in Speech Communication from the Royal Institute of Technology (KTH) and has been a prominent figure in computational linguistics research and education at Uppsala University since at least 2000. Her academic background includes: PhD in Speech Communication, Department of Speech, Music and Hearing, KTH (2002) BA in Computational Linguistics, Department of Linguistics, Stockholm University (2000) Megyesi's research focuses on the intersection of computational methods and humanities, particularly historical cryptology and digital philology. She develops innovative tools that enable humanists and social scientists to obtain quantitative analyses of historical texts, with special emphasis on automatically cracking historical ciphers. Her work bridges linguistic analysis, computer science, and historical research, creating methodologies for processing and analyzing encoded historical documents. She has pioneered approaches to automatic transcription, key structure extraction, and deciphering techniques for historical manuscripts, making significant contributions to both computational linguistics and historical research. Analysis of her recent publications reveals a strong trend toward interdisciplinary research combining historical cryptology with advanced computational methods. Her work demonstrates increasing sophistication in handling historical ciphers through machine learning, image processing, and language modeling techniques. The research spans multiple languages and historical periods, with particular focus on early modern European diplomatic correspondence. Her publications show consistent contributions to both theoretical frameworks and practical tools for historical document analysis, with growing attention to privacy concerns in language learner data. Megyesi has held significant leadership roles including: President of Northern European Association for Language Technology (NEALT, 2020-2021) Head of Department, Department of Linguistics and Philology (2009-2018) Director of English Park Campus, Uppsala University (2017-2018) Member of Swedish Research Council's preparatory group for Linguistics (2021-2023) As an educator, Megyesi has supervised graduate students including Eva Pettersson and Mojgan Seraji, and has taught courses on language technology, digital philology, and computational linguistics at both undergraduate and graduate levels. She has received research funding from Vetenskapsrådet (Swedish Research Council) for multiple projects including DECRYPT (2018-2024) and DECODE (2015-2017), demonstrating sustained research productivity and external recognition of her work's significance. Megyesi leads the DECRYPT project focused on developing methods to automatically crack historical ciphers, working with interdisciplinary teams of linguists, computer scientists, and historians. Her research group has developed specialized tools for transcription of encrypted manuscripts and created important resources like the DECODE Database of Historical Ciphers and Keys. She is an active participant in the international historical cryptology community, frequently organizing and contributing to conferences in this specialized field.

Haibo LiView profile

Professor

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +5 more

Haibo Li is a Full Professor of Media Technology at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and Digital Futures Faculty. His roles include leading the Media Lab in MID, directing Master’s programs in Media Technology, and teaching courses like Generative AI for Media Technology and Video Technology. He holds a Technical Doctorate from Linköping University (1993) and received the Nordic Best PhD Thesis Award (1994) and Docent title in Image Coding (1997). Research focuses on digital media technologies, including image/video compression, human-computer interaction, and AI-driven media systems. He pioneered the Digital Media Lab at Umeå University and co-founded UCIT. His work spans blind image deblurring, thermal comfort sensing, and multimodal emotion recognition. Over 250 publications and six patents highlight contributions to multimedia, AI, and signal processing. Key projects include developing contactless thermal measurement systems for smart buildings, real-time gesture recognition for wearable devices, and advanced neural networks for image restoration. Awards include the Nordic Best Thesis and leadership in EU projects. He chairs conferences and actively participates in MPEG standardization efforts.

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +5 more

Renan Guarese is a Postdoctoral Researcher in Human-Computer Interaction at KTH Royal Institute of Technology , Sweden, focusing on AR/VR applications for pharmaceutical manufacturing through the SMART Industry project with AstraZeneca. He holds a Ph.D. from RMIT (Australia) and M.Sc. from UFRGS (Brazil), with expertise in assistive technologies, situated data visualization, and accessibility. His roles include teaching assistant for courses like Advanced Graphics and Multimodal Interaction, and M.Sc. thesis supervision. He has over 10 years of academic experience across institutions like RMIT, Halmstad University, and UFRGS, with projects ranging from assistive AR for visually impaired individuals to educational AR platforms. Research Interests: Human-Computer Interaction (HCI), Augmented and Virtual Reality (AR/VR), assistive technologies, situated visualization, and accessibility design. He specializes in applications like sonified AR interfaces, industrial training, and empathy-building experiences for marginalized users. Awards: Best Doctoral Thesis (SVR 2024) Finalist, Cross-Reality Systems Competition (IEEE ISMAR 2023) Nomination for Best Paper Award (IEEE VR 2023) Latin American PhD Scholarship (ATN grant) Advising & Grants: Supervising 3 current M.Sc. theses (2025) and co-authoring grants focused on conversational AI in industrial training and predictive maintenance. His work bridges HCI with real-world industry needs, emphasizing accessibility and immersive solutions. Labs/Teams: Active in KTH's Digital Futures initiative and collaborates with AstraZeneca on pharmaceutical AR/VR applications. Engages in international conferences like IEEE VR, ISMAR, and CHI through peer reviewing, workshop organizing, and panel participation.

Mikael FridenfalkView profile

Senior Lecturer

• Artificial Intelligence
• Computer Graphics
• Game Design
• +4 more

Mikael Fridenfalk is a Senior lecturer at the Department of Game Design, Uppsala University, Campus Gotland. He received his PhD in robotics from Lund University in 2003 and completed a postdoctoral research associate position at MIT (2003-2004). Since then, he has been affiliated with Uppsala University Campus Gotland (formerly Gotland University), initially as a lecturer and from 2005 as an associate professor and researcher within computer engineering and AI. With over 40 years of experience in computer programming, primarily using C++, Fridenfalk specializes in artificial intelligence and computer graphics applications. His research spans multiple domains including game design, digital twins, cellular automata, and neural networks. His work demonstrates a consistent focus on the intersection of mathematical principles and practical applications in computer science. Fridenfalk's recent publications (2022-2025) show a shift toward applied research in digital twins for industrial metaverse applications, cloud-edge-terminal collaboration, and game environment design using modular architecture. This reflects an evolution from his earlier theoretical work to more industry-relevant applications while maintaining strong mathematical foundations. Scientific Contributions: Extensive research on cellular automata and modular spaces across numerous publications Development of analytical methods for neural network weight evaluation Innovative applications of L-systems in game environment and music generation Pioneering work on digital twin technology for industrial and marine applications Contributions to photogrammetry techniques for game environments Methods for examination problem distribution in e-learning environments Fridenfalk's work bridges theoretical computer science with practical applications across multiple domains, demonstrating remarkable versatility while maintaining mathematical rigor. His research has been cited in various contexts, including academic papers and Wikipedia references, indicating impact across disciplines.

• Bioinformatics and Computational Biology
• Computer Graphics and Computer Vision
• Medical Imaging

Kimmo Kartasalo is a Senior Research Specialist and Assistant Professor at the Department of Medical Epidemiology and Biostatistics , Karolinska Institutet , Sweden. His research focuses on the intersection of Artificial Intelligence , Bioinformatics , and Medical Imaging , particularly in cancer diagnostics. Current roles: Senior Research Specialist (2024-), Assistant Professor (2024-2030) Supervision: Sol Erika Boman (2023), Kelvin Szolnoky (2022) His work emphasizes deep learning for histopathology analysis, including prostate cancer grading, breast cancer spatial gene expression prediction, and multi-stain whole-slide imaging. He leads projects in 3D tissue reconstruction , virtual reality histology visualization , and AI-assisted diagnostic uncertainty estimation . Collaborations span international institutions and include algorithm benchmarking challenges like PANDA and ACROBAT. Key methodological contributions include physical color calibration for digital pathology scanners, conformal prediction frameworks for diagnostic reliability, and open-source tools like OpenPhi for whole-slide image processing. His publications appear in top journals including Nature Medicine , Cancer Research , and European Urology .

Jacek MalecView profile

Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Robotics
• +3 more

Jacek Malec is a Professor at the Department of Computer Science, Faculty of Engineering, Lund University. He serves as a project manager and office director, actively contributing to research in Artificial Intelligence and Robotics. His work focuses on Knowledge Representation and Reasoning, with applications in Intelligent Collaborative Robotics and Computational Efficiency. Active in ELLIIT (Linköping-Lund IT and Mobile Communication Initiative) Member of Lund University's AI and Digitalization and Natural and Artificial Cognition profile areas Coordinated projects like Mixed-Initiative Interaction for Collaborative Robotics and AI Lund His research spans Computer Science , Robotics , and Artificial Intelligence , with recent publications on Order Batching Optimization and Metropolis Sampling. He has participated in interdisciplinary activities, including symposiums on Synthetic Biology and its role in climate change mitigation. Current projects include Robotics week for schools and infrastructure management for RobotLab LTH . His collaborations extend internationally, with affiliations in fields like Algorithms, Ontology, and Industry 4.0.

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Gaurav RaiView profile

Researcher

• Computer Graphics
• Computer Vision
• Image Processing
• +3 more

Gaurav Rai is a Ph.D. researcher at Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), focusing on computer graphics, computer vision, and GPU computing. He has contributed to real-time animation frameworks and diffusion models for motion transfer. Ph.D. in Computer Science and Engineering (2020–present), IIIT Delhi B.Tech in Computer Science and Engineering (2016–2020), GKV Haridwar His research emphasizes creative algorithms for sketch-based animation and dynamic deformation. Recent work involves diffusion models with temporal consistency and motion-guided frameworks. Gaurav has published in top-tier conferences like SCA, GRAPP, and ICME, with a focus on computer graphics, deep learning, and multimedia. Earlier publications include applications in medical imaging and spam filtering. ACM Student Travel Grant for ICME 2025 CHANAKYA Doctoral Fellowship He has served as a teaching assistant for courses in Computer Graphics, Scientific Computing, and Linear Algebra at IIIT Delhi.