جست‌وجوی استاد و پژوهشگر

Maria Brandénمشاهده پروفایل

استاد

• Segregation Dynamics
• Social Inequality
• Machine Learning
• +۴ مورد دیگر

Maria Brandén is a Professor and Head of Division at Linköping University, affiliated with the Department of Management and Engineering (IEI) and the Institute for Analytical Sociology (IAS). She has been part of IAS since its founding in 2014 and became its director in January 2021. Her primary role involves leading research and fostering interdisciplinary collaborations, particularly in computational social science. She is based at Campus Norrköping and can be reached at maria.branden@liu.se. Education: Maria Brandén holds a PhD in Sociological Demography from Stockholm University (2014), which focused on couple decisions related to residential mobility. She has since expanded her research into broader societal issues. Research Interests: Her work examines how segregation (school, housing, workplace) impacts individuals’ life chances, leveraging Sweden’s extensive register data. Key areas include neighborhood effects, socio-economic inequalities in health outcomes (e.g., during the pandemic), and computational methods such as machine learning to analyze complex social dynamics. She emphasizes understanding heterogeneous effects and long-term societal impacts of segregation. Grants & Projects: As Principal Investigator (PI), she secured SEK 13 million from the Swedish Research Council (2020–2023) to combine machine learning with register data for studying social inequality. Additional projects funded by Forte and the Swedish Research Council address school and housing segregation. She also leads the Institute’s strategic growth and international collaborations. Labs/Teams: Director of IAS, which focuses on agent-based simulations, social network analysis, and text mining. She co-founded the SweCSS (Swedish Centre for Computational Social Science) and oversees its Visiting Fellows Program to bridge social and computational sciences.

Henrik Erikssonمشاهده پروفایل

استاد

• Artificial Intelligence
• Epidemiology
• Data Science
• +۳ مورد دیگر

Henrik Eriksson is a Professor and Head of the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on epidemiological modeling, societal resilience, and semantic web technologies. He has contributed to projects analyzing infectious disease transmission dynamics and pandemic response strategies, leveraging data science and computational methods. Key affiliations include the IDA divisions of Artificial Intelligence and Integrated Computer Systems (AIICS), and his work intersects with public health informatics, urban resilience, and high-performance computing. Recent publications address real-time forecasting of disease outbreaks and guidelines for resilient urban systems. He leads interdisciplinary initiatives at LiU, fostering collaborations in AI-driven solutions for societal challenges.

Chengxi Liمشاهده پروفایل

پژوهشگر

• Distributed Learning
• Federated Learning
• Signal Processing
• +۳ مورد دیگر

Chengxi Li is a Postdoctoral Researcher at the Division of Information Science and Engineering (ISE) within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm. He works under Prof. Mikael Skoglund and Prof. Ming Xiao, with a visiting position at École Polytechnique Fédérale de Lausanne (EPFL) under Prof. Rachid Guerraoui. His research focuses on distributed learning, federated learning, signal processing, and information theory, with applications in sensor networks and wireless communication systems. Li holds a Ph.D. in Electronic Engineering from Tsinghua University (2022) and a B.S. in Information and Communication Engineering from the University of Electronic Science and Technology of China (2018). He has also completed visiting studies at the University of Wollongong and City University of Hong Kong. His work has been recognized through awards such as the Beijing Municipal Outstanding Doctoral Dissertation (2023) and the Excellent Doctoral Dissertation from the China Education Society of Electronics (2023). His research interests emphasize developing theoretical foundations and algorithms for distributed systems, including federated learning frameworks, secure signal processing, and efficient communication strategies in decentralized networks. Key contributions include innovations in gradient coding for straggler mitigation, robust federated learning under label quality disparities, and distributed detection in sensor networks with secrecy constraints. Li has secured grants as Principal Investigator, including the MSCA Postdoctoral Fellowship (2024–2026) and the Digital Futures Postdoc Fellowship (2023–2025). He mentors Master student Aiyang Yu and actively contributes to academic service, including reviewing for top journals like IEEE Transactions on Signal Processing and organizing workshops on secure federated learning. His work bridges foundational research and practical applications, with publications in prestigious venues such as IEEE Transactions on Signal Processing, IEEE Transactions on Communications, and IEEE Internet of Things Journal. Current projects explore communication-efficient learning algorithms and secure distributed systems for next-generation networks.

Sajad Daei Omshiمشاهده پروفایل

پژوهشگر

• Integrated Sensing and Communications
• Wireless Communications
• Signal Processing
• +۳ مورد دیگر

Sajad Daei Omshi is a Postdoctoral researcher at KTH Royal Institute of Technology, working in the Division of Information Science and Engineering under Professors Mickael Skoglund and Gabor Fodor. His research focuses on Integrated Sensing and Communications (ISAC), wireless signal processing, and radar-communication system design. Previously, he held a postdoc position at EURECOM under Professor Marios Kountouris, focusing on semantic and goal-oriented communications. Education: PhD in Electrical and Electronics Engineering from Iran University of Science and Technology (2015–2019). Research areas include mathematical optimization, compressed sensing, super-resolution techniques, MIMO communications, and channel estimation. His work addresses challenges in 6G networks, millimeter-wave systems, and radar-communication integration. Key research contributions span ISAC frameworks, blind signal recovery, and distributed computing. Notable achievements include the 2020 IEEE Iran Section Outstanding PhD Thesis Award. He collaborates actively with institutions like Farzad Parvaresh's Lab at KTH and has published over 50 articles in top conferences/journals. Awards: Outstanding PhD thesis award of IEEE Iran Section (2020) Advising/Grants: No specific advising information available, though his postdoc roles imply mentorship in collaborative projects. His research is supported by grants focusing on ISAC and 6G technologies. Labs/Teams: Member of Farzad Parvaresh's Lab at KTH, contributing to signal processing and communication systems research.

• Fluid Dynamics
• Scientific Machine Learning
• Multi-scale Modelling
• +۶ مورد دیگر

Marco Laudato is a Researcher at KTH Royal Institute of Technology, serving as Principal Investigator leading independent research initiatives and affiliated with FLOW. He holds a PhD in Mathematics and Models from the University of L'Aquila (2021) and completed postdoctoral studies at KTH's Department of Engineering Mechanics (2021–2024). His work aligns with UN Sustainable Development Goal #3, focusing on thrombotic event prediction and respiratory flow optimization through multi-scale modeling, neural networks, and fluid-structure interaction. Research interests include Fluid Dynamics, Scientific Machine Learning, Aeroacoustics, and Biofluid Dynamics. Notable achievements include the 2024 Göran Gustafsson Young Scientist Award and a 2022 Swedish Research Council grant. Dr. Laudato teaches courses such as Data-driven Methods in Engineering Mechanics and Fluid Mechanics, emphasizing both theoretical and applied mechanics education. Education: PhD in Mathematics and Models (University of L'Aquila, 2021) Affiliations: FLOW Research Group at KTH Key Projects: Neural operator modeling for platelet mechanics, collapsible tube dynamics, and metamaterials research His publications span experimental and computational studies in metamaterials, aeroacoustics, and biomedical fluid dynamics, with a strong emphasis on machine learning integration.

Dejan Kosticمشاهده پروفایل

استاد

• Distributed Systems
• Computer Networks
• Operating Systems
• +۶ مورد دیگر

Dejan Kostic is a Chair Professor of Internetworking at KTH Royal Institute of Technology, leading the Networked Systems Laboratory. He is affiliated with RISE Research Institutes of Sweden and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on distributed systems, computer networks, operating systems, and machine learning applications in networking. Education: Ph.D. in Computer Science, Duke University M.S. in Computer Science, University of Texas at Dallas B.S. in Computer Engineering, University of Belgrade Research Interests: His work spans distributed systems , high-performance networking , smartNIC offload , AI-driven systems , and network function virtualization (NFV) . Recent projects include energy-efficient AI inferencing (Wallenberg Scholar Project) and scalable federated learning for healthcare applications. Key contributions include the PacketMill framework, FAJITA stateful processing, and the LineFS distributed file system. Publications & Awards: ERC Starting (2010) and Consolidator (2018) Awards Wallenberg Scholar (2024) Best Paper at SOSP 2021 and Community Award at NSDI 2022 Advising & Grants: Supervised over 15 doctoral students, including notable alumni like Hamid Ghasemirahni and Alireza Farshin. Leads projects funded by ERC, WASP, and Swedish Research Council (VR). Active in grant review panels for ERC and Swedish Foundations. Labs & Teams: Heads the Network Systems Laboratory (NSLab) at KTH, collaborating with Ericsson and RISE on industrial projects. Key initiatives include the Networked Systems for Machine Learning course and the Connected Intelligence unit at RISE.

Ioana-Oriana Berceaمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Data Structures
• Randomized Algorithms
• +۶ مورد دیگر

Ioana-Oriana Bercea is an Assistant Professor at the Division of Theoretical Computer Science (EECS), KTH Royal Institute of Technology, and a member of the Digital Futures Faculty. She holds a PhD in Computer Science from the University of Maryland, with prior postdoctoral research at the IT University of Copenhagen and Tel Aviv University. Her research focuses on Theoretical Computer Science, including Data Structures (e.g., Bloom filters), Randomized Algorithms (e.g., hashing), and Computational Geometry (e.g., TSP). Education : PhD in Computer Science, University of Maryland (advisor: Samir Khuller) Master's in Computer Science, University of Maryland (advisor: Aravind Srinivasan) Bachelor of Science in Mathematics (Honors) and Computer Science, University of Chicago Research Interests : She explores scalable data management (e.g., Bloom filters), algorithm design for clustering and hashing, and geometric optimization problems. Her work emphasizes theoretical foundations with practical applications. Key Achievements : VR Starting Grant (2024) for the project "DataTech" Best Artifact Award at ACM SIGMOD 2023 for the InfiniFilter paper Advising & Grants : Advises PhD student Jonas Østergaard Klausen (joint with Mikkel Thorup and Jacob Holm) Lead researcher on grants exploring data storage and algorithmic efficiency Labs & Teams : Active in KTH's Theoretical Computer Science group and collaborates with the Digital Futures initiative to advance cross-disciplinary digital technologies.

Johan Boyeمشاهده پروفایل

دانشیار

• Natural Language Processing
• Spoken Dialogue Systems
• Multimodal Dialogue Systems
• +۶ مورد دیگر

Johan Boye is an Associate Professor at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, part of the Speech, Music, and Hearing department. He leads the SWE-QUEST project, developing automated question generation for Swedish language education, funded by Vinnova and Digital Futures. His research focuses on dialogue systems, natural language processing, and spatial language understanding, with applications in education, robotics, and human-computer interaction. Education background includes a docent title (Swedish associate professor) and extensive academic experience. He has supervised over 20 master’s and PhD theses in NLP and AI, with recent topics including large language models, coreference resolution, and multimodal retrieval systems. Teaching responsibilities include courses on language technology, search engines, and speech processing. His work spans cross-disciplinary collaborations with Stockholm University and RISE Research Institutes in the Digital Futures initiative, addressing societal challenges through digital innovation. Key projects include the SpaceBook EU project (2010–2014) exploring pedestrian navigation via dialogue systems and the NICE game (2005) for child-computer interaction. Research outputs include over 50 peer-reviewed publications in journals like the Journal of Natural Language Engineering and conferences such as ACL and SIGDIAL. He maintains active roles in academic service, including editorial work and program committees for NLP conferences. His lab develops tools like Textinator for NLP evaluation and UDon2 for dependency tree manipulation, emphasizing open-source contributions to the research community.

Kasun De Zoysaمشاهده پروفایل

مدرس ارشد

• Computer Security
• Cryptography
• Digital Forensics
• +۴ مورد دیگر

Dr. Kasun De Zoysa is a Senior Lecturer at the University of Colombo School of Computing (UCSC), specializing in Computer Security and Cryptography. He holds a B.Sc. (First Class Honours) in Computer Science from the University of Colombo (1993–1997) and a Ph.D. in Computer Security from Stockholm University (1999–2004). His academic roles include coordinating the M.Sc. in Information Security program and advising the Center for Digital Forensic. Research Focus: Secure multi-party transactions, public key cryptography, web security, sensor networks, digital forensics, and ICT4D. Teaching: Undergraduate courses like Programming, Information System Security; postgraduate courses including Network Security, Digital Forensics, and Mobile Application Security. Notable projects include the TikiriDB sensor network framework, mobile ATM systems for developing countries, and wildlife monitoring via infrasound. He has published extensively on sensor networks, cybersecurity, and digital forensics in venues like IEEE MASS, REALWSN, and ICTer.

Erik Flores-Garcíaمشاهده پروفایل

استادیار

• Data-Driven Production Logistics
• Machine Learning
• Digital Twin Technology
• +۳ مورد دیگر

Erik Flores-García is an Assistant Professor at the Digital Futures Faculty of KTH Royal Institute of Technology , collaborating with Stockholm University and RISE Research Institutes of Sweden. His research focuses on data-driven production logistics, machine learning applications in industrial systems, and digital twin technologies to enhance manufacturing efficiency and sustainability. Located at Osquars Backe 5 in Stockholm, his work bridges academic research with industrial challenges. His research interests include optimizing logistics systems through AI, integrating human-centric vision systems, and developing frameworks for smart production environments. Key themes include energy efficiency, explainable AI, and socio-technical implications of automation in biopharmaceutical and automotive industries. Recent publications highlight advancements in deep reinforcement learning for warehouse optimization, digital twin-based maintenance planning, and frameworks for industrial IoT integration. His work emphasizes both technological innovation and societal impact, addressing challenges like real-time data transmission and ethical considerations in automation. No scientific awards are listed, but his contributions reflect a focus on industry-academia collaboration through co-production models and problem-based education initiatives. His research also explores simulation-based methodologies for facility layout design and decision-making in production system innovations.

Martin Jacobssonمشاهده پروفایل

دانشیار

• Machine Learning
• Biomedical Engineering
• IoT and Wearable Technologies
• +۵ مورد دیگر

Martin Jacobsson is an Associate Professor at the School of Computer Science and Communication (CBH) at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He co-founded the Digital Futures research center, which focuses on cross-disciplinary digital technologies addressing societal challenges such as healthcare innovation, IoT systems, and wireless communication. His work emphasizes practical applications with high industrial relevance and strategic importance through collaborations with Stockholm University and RISE Research Institutes of Sweden. His research interests span machine learning in clinical decision support, biomedical data compression, wearable sensor networks, and low-power wireless communication protocols. Notable projects include drone-based motion capture systems for sports analytics, embedded emergency systems, and predictive models for surgical outcomes using ML techniques. Publications highlight contributions to healthcare informatics (e.g., trauma care improvement), IoT device optimization (e.g., battery-free wearables), and wireless network protocols (e.g., LoRaWAN interference analysis). His work bridges theoretical advancements with real-world implementations in healthcare, sports, and industrial IoT. He is actively involved in the Digital Futures community, engaging through LinkedIn, YouTube, and academic conferences. The center is located at Osquars Backe 5 in Stockholm, with contact via info@digitalfutures.kth.se.

Jan Kronqvistمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Convex MINLP
• +۷ مورد دیگر

Jan Kronqvist is an Assistant Professor at KTH Royal Institute of Technology and a faculty member of Digital Futures, a cross-disciplinary research center focused on digital technologies. His research emphasizes optimization methodologies, including convex MINLP, machine learning integration, and algorithm design for complex systems. He actively contributes to projects like SMART (Predictive Maintenance), SimOPT (Process Design), ADAPCOS (Undersea Swarm Optimization), and cybersecurity in power systems. Key research areas include global optimization for clustering problems, robustness in neural networks, GPU-accelerated machine learning, and ensemble model optimization. He has developed algorithms like the Extended Cutting Plane and SHOT solver for convex MINLP, with applications in chemical engineering, power systems, and control systems. His work bridges theoretical optimization and practical industrial challenges, addressing topics such as cyberattack resilience, multimodal mobility systems, and ensemble learning verification. Projects often involve collaboration with industry partners to translate mathematical models into real-world solutions. Jan has advised initiatives like the SEC scholar program for Calvin Tsay and contributed to research on radiation therapy scheduling and distillation process optimization. His research outputs span over 40 peer-reviewed articles, focusing on advancing optimization algorithms and their applications across engineering and data science domains.

Rodrigo Morenoمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Rodrigo Moreno is an Associate Professor at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) at KTH Royal Institute of Technology, and a Co-Principal Investigator (Co-PI) in two major research projects: 'Characterization of the mechanical tissue properties of the brain in the developing brain with magnetic resonance elastography' and 'Advanced Magnetic Resonance Elastography for the Brain.' He is also affiliated with Digital Futures, a cross-disciplinary research center focused on developing digital technologies to address societal challenges. His primary research interests include biomedical imaging techniques, biomechanical properties of tissues, and the application of artificial intelligence (AI) in healthcare. Dr. Moreno’s work spans neurodegenerative diseases like Alzheimer’s and Parkinson’s, as well as advancements in medical imaging modalities such as MRI, CT, and ultrasound. His recent projects emphasize precision medicine using AI-powered longitudinal MRI analysis and the mechanical characterization of brain tissue in developmental and disease contexts. He leads collaborations in computational methods like tensor voting and deep learning for medical image processing, segmentation, and generative modeling of biological structures. His contributions include developing algorithms for tractography filtering, synthetic data generation, and robust segmentation of medical images. Dr. Moreno’s research has produced over 50 peer-reviewed articles since 2014, with a focus on advancing imaging technologies and their clinical applications. His work bridges computational engineering, biomedical research, and digital innovation, aiming to improve diagnostics and personalized treatment strategies for complex diseases.

Chiara Rossittoمشاهده پروفایل

دانشیار

• Human-Computer Interaction (HCI)
• Grassroots Technologies
• Community-Led Initiatives
• +۷ مورد دیگر

Chiara Rossitto is an Associate Professor of Human-Computer Interaction at Stockholm University’s Department of Computer and Systems Sciences (DSV), affiliated with the Digital Futures Faculty’s Rich and Healthy Life working group. She holds a PhD in HCI from KTH Royal Institute of Technology and a Master’s in Communication Science from the University of Siena. Her research focuses on grassroots technologies, community-led initiatives, and sustainable socio-technical systems. Key themes include food sharing, scaling challenges in grassroots networks, and the role of artists in performance-led design. She actively contributes to conferences like CHI and ECSCW, co-authoring influential papers on topics such as queuing mechanisms in activism and telepresence technologies in theater. Rossitto has received the David Martin Best Paper Award at ECSCW 2018. She coordinates the Interaction Design program at Stockholm University and leads collaborations, including a 2018 grant with the University of Tokyo on mixed-reality design. Her work bridges theory and practice, emphasizing ethical and socially just technological solutions.

Rolf Stadlerمشاهده پروفایل

استاد

• Network Management
• Cloud Analytics
• Resource Management
• +۳ مورد دیگر

Rolf Stadler is a Professor and Head of the Division of Network and Systems Engineering at KTH Royal Institute of Technology in Stockholm. He holds affiliations with Digital Futures Faculty and is Co-PI of the DEMOCRITUS project on decision-making in critical infrastructures. His research focuses on network and system management, including real-time monitoring, resource allocation, and data-driven engineering. Education: M.S. in Mathematics, University of Zurich Ph.D. in Computer Science, University of Zurich His research interests span advanced monitoring techniques, network analytics, and cybersecurity. Notable collaborations include Ericsson Research, Cisco Systems, and KDDI Labs. He leads the Laboratory for Communication Networks and co-founded the ACCESS Linneus Center. Dr. Stadler serves as Editor-in-Chief of IEEE Transactions on Network and Service Management. Research Trends: Recent work emphasizes adaptive security policies, online learning for intrusion detection, and scalable frameworks for dynamic network environments. His publications highlight innovations in game-theoretic security, distributed resource management, and real-time performance prediction. Awards: Applied Networking Research Prize (2014) Dan Stokesberry Award (2015) Teaching & Grants: Acts as examiner and course responsible for multiple graduate-level programs in computer science and electrical engineering. His grants include funding for projects like DEMOCRITUS and service mesh optimization initiatives. Labs & Teams: Active in the Laboratory for Communication Networks and the interdisciplinary ACCESS Linneus Center, focusing on complex network systems.