جست‌وجوی استاد و پژوهشگر

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.

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

Xuyang Wuمشاهده پروفایل

دانشیار

• Distributed Optimization
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Xuyang Wu is an Associate Professor at the School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology (SUSTech), China. Previously, he served as an Assistant Professor at SUSTech and held a postdoctoral position at KTH Royal Institute of Technology in Sweden under Professors Mikael Johansson and Sindri Magnússon. He completed his PhD in Communication and Information Systems at the University of Chinese Academy of Sciences (2015–2020) and a B.S. in Applied Mathematics from Northwestern Polytechnical University (2011–2015). His research focuses on distributed optimization, federated learning, and large-scale machine learning algorithms, emphasizing theoretical guarantees and practical implementations. Notable projects include safe resource allocation in power networks and asynchronous algorithms for distributed systems. He has published extensively in top-tier journals like IEEE Transactions on Automatic Control and conferences such as ICML and ICLR. Dr. Wu has received recognition including a best student paper finalist award at IEEE ICCA 2019. His work bridges theoretical advancements with real-world applications in smart networks and cyber-physical systems.

Deepika Yadavمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Global Health
• Women’s Health
• +۳ مورد دیگر

Deepika Yadav is a Research Fellow at Stockholm University's Department of Computer and Systems Sciences (DSV), affiliated with the Stockholm Technology & Interaction Research (STIR) group. She leads the Digital Futures Postdoc Fellowship project 'Relational Aspects of Care in Intimate Digital Health Technologies' (2021–2023), focusing on designing technologies to address gaps in women’s healthcare. Her research bridges Human-Computer Interaction (HCI) and global development, emphasizing underrepresented groups and resource-constrained settings. Her work explores sociotechnical challenges in digital health technologies, particularly around intimate care such as fertility tracking, lactation support, and workplace health infrastructure. She employs innovative methods like Soma Design to enhance somatic literacy and co-design inclusive solutions. Collaborations span academic institutions including KTH Royal Institute of Technology and RISE Research Institutes of Sweden through the Digital Futures initiative. Key themes include trust in algorithmic health services, interpersonal care dynamics, and workplace adaptations for bodily needs. Her projects emphasize participatory design with marginalized communities, aiming to improve healthcare access and equity through technology. Recent publications focus on digital contraception, gig-worker health, and maternal support systems in India. Deepika’s research contributes to both academic discourse and practical innovations, fostering ethical tech development that prioritizes human well-being and societal impact. She has advised on collaborative learning platforms for community health workers and pioneered mobile health solutions in rural India.

Jiaojiao Zhangمشاهده پروفایل

پژوهشگر ارشد

• Distributed Optimization
• Decentralized Machine Learning
• Algorithm Design
• +۴ مورد دیگر

Jiaojiao Zhang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, focusing on decentralized machine learning algorithms with convergence guarantees. Her research addresses second-order, online, and robust methods for distributed optimization, emphasizing applications in privacy-preserving technologies and smart systems. Education: B.E. in Automation (Harbin Engineering University, 2015), M.Sc. in Control Theory (University of Science and Technology of China, 2018), Ph.D. in Operations Research (CUHK, 2022). Award: Hong Kong PhD Fellowship Scheme (2018). Supervisors: Mikael Johansson (KTH EECS) and Joakim Jaldén (KTH). Her work bridges distributed optimization and algorithm design, targeting challenges in federated learning, wireless sensor networks, and multi-core processor training. Projects like 'Decentralized Learning' aim to enhance cooperation and trust in digital futures through robust, efficient algorithms. Key areas: privacy-preserving federated learning, Byzantine-robust systems, and dynamic privacy allocation. Publications emphasize convergence analysis, communication efficiency, and adversarial resilience in decentralized settings. She actively contributes to Digital Futures, a joint initiative of KTH, Stockholm University, and RISE.

• Computational Neuroscience
• Neural Network Modeling
• Locomotion Control
• +۳ مورد دیگر

Alexander Kozlov is a Researcher at KTH Royal Institute of Technology's Division of Computational Science and Technology. His work focuses on computational neuroscience, particularly modeling neural networks and locomotor control systems. He has contributed to understanding spinal cord networks, striatal connectivity, and neuromodulation mechanisms through in silico studies. Kozlov teaches courses in machine learning, artificial neural networks, and mathematical modeling of biological systems. His research integrates AI frameworks with biological data, emphasizing GPU-accelerated simulations and large-scale microcircuit analysis. Key contributions include studies on Parkinson's disease impact on neural connectivity and the role of sensory feedback in locomotion. He collaborates with the Science for Life Laboratory in Stockholm and maintains active roles in both peer-reviewed and conference publications. Education background and affiliations are not explicitly detailed in the provided text, but his extensive publication record reflects a deep engagement with interdisciplinary neuroscience and computational biology. Awards or grants are not listed here.

Nicolas Pielawskiمشاهده پروفایل

پژوهشگر ارشد

• AI
• Healthcare
• Statistics
• +۲ مورد دیگر

Nicolas Pielawski is a Research Fellow at Uppsala University's Department of Information Technology, Division of Systems and Control, where he develops AI solutions for emergency medicine. His work bridges Bayesian statistics, information theory, and healthcare applications to enhance decision-making systems. Research interests focus on AI implementation in clinical settings, uncertainty quantification, trustworthy AI systems, and mathematical modeling of complex problems. Key areas include Bayesian inference, neural network interpretability, and multimodal learning for biomedical data. Publications demonstrate consistent focus on deep learning innovations, particularly in medical image processing and natural language model optimization. Recent work emphasizes computational efficiency, interpretability, and applications in spatial omics and emergency healthcare contexts.

Philip Harrisonمشاهده پروفایل

پژوهشگر

• Machine Learning
• Pharmaceutical Science
• Biodiversity Analysis
• +۳ مورد دیگر

Philip Harrison is a Visiting Researcher at the Department of Pharmaceutical Biosciences, Uppsala University. He holds a PhD in marine biology (2006, focusing on grey seal population dynamics) and is pursuing a second PhD in pharmaceutical science, developing machine learning methods for microscopy image analysis within the HASTE project. His research spans biodiversity modeling, computational biology, and pharmaceutical applications of AI. Education : PhD in Marine Biology, 2006 (Topic: Population Dynamics of Grey Seals) Current PhD Candidate in Pharmaceutical Science (Focus: Machine Learning in Microscopy Analysis) Research Interests : Harrison’s work bridges ecology and computational bioscience. Early research modeled wildlife populations and biodiversity trends. Current projects leverage deep learning for drug discovery, cellular imaging analysis, and lipid nanoparticle drug delivery. He collaborates on the HASTE project, advancing AI-driven scientific pipelines. Publications : Recent work includes peer-reviewed articles on mechanism-of-action prediction via image data fusion, Boolean protein interaction methods, and deep learning in cytometry. His publications emphasize interdisciplinary approaches, combining AI with pharmaceutical and ecological challenges. Advising/Grants : No formal student advising or grant details provided in sources. His research is embedded in collaborative projects like HASTE, focusing on cloud-native data pipelines and AI applications. Labs/Teams : Part of the Pharmaceutical Bioinformatics group at Uppsala, contributing to cross-disciplinary teams in computational bioscience and drug discovery.

Vincenzo Gulisanoمشاهده پروفایل

دانشیار

• Distributed Systems
• Data Streaming
• Concurrent Data Structures
• +۷ مورد دیگر

Vincenzo Gulisano is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology. He holds a Ph.D. from the Technical University of Madrid and master's and bachelor's degrees from the University of Trieste. His research focuses on distributed systems, data streaming, and concurrent data structures with applications in cyber-physical systems such as Advanced Metering Infrastructures (AMI) and DDoS detection. He leads the Master Program in Computer Systems and Networks (MPCSN) and teaches courses like Operating Systems (EDA092/DIT400). His work emphasizes scalable and fault-tolerant stream processing frameworks like STRETCH and Erebus, which address challenges in real-time analytics and elastic resource management. Gulisano collaborates on projects integrating machine learning (e.g., IP-LSH-DBSCAN for parallel clustering) and cybersecurity (e.g., Metis for AMI intrusion detection). He has published widely in top-tier conferences like VLDB, Euro-Par, and IEEE TPDS, with contributions to stream synchronization, scheduling policies, and energy-sharing communities. Gulisano’s research bridges theoretical foundations and practical implementations, addressing scalability, determinism, and explainability in data-driven systems. His work often involves interdisciplinary collaborations, combining algorithms, hardware, and middleware design for efficient data processing in edge and cloud environments.

• Robot Control
• Robotic Manipulation
• Human-Robot Interaction
• +۱۲ مورد دیگر

Yiannis Karayiannidis is an Associate Professor in the Department of Automatic Control at Lund University's Faculty of Engineering (LTH). He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). A portion of his research team is based at Chalmers University of Technology, specifically within the Division of Systems and Control (SYSCON). He actively contributes to the robotics and control community through editorial roles and leadership in professional organizations. His research focuses on advanced robotic control systems, particularly in human-centered environments. Key areas include robotic manipulation, dual-arm coordination, force and grasping control, physical human-robot interaction, robot learning, navigation, reactive planning, multi-agent systems, and nonlinear and adaptive control. These interests are reflected in both his teaching and research output. The recent publication trends highlight a strong focus on autonomous navigation in dynamic environments, robust force control, multi-agent coordination, and learning-based manipulation. His work integrates model-based control with data-driven methods, particularly in complex and uncertain settings. Topics such as obstacle avoidance, tunnel-following MPC, friction modeling, and semantic feature estimation demonstrate technical depth and real-world applicability. Prof. Karayiannidis holds significant academic service roles: Associate Editor, IEEE Robotics and Automation Letters Associate Editor, IEEE/ASME Transactions on Mechatronics Associate Editor, International Conference on Robotics and Automation (ICRA) Associate Editor, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Associate Editor, European Control Conference Treasurer, IEEE Robotics Chapter in Sweden He has supervised numerous PhD and master's students and is actively involved in research projects funded by national and strategic initiatives. His editorial and supervisory roles underscore his leadership in the robotics and control community. He also contributes to infrastructure development as a manager of RobotLab LTH. He is involved in several key research labs and teams: RobotLab LTH – a shared robotics research infrastructure where he serves as a manager Division of Systems and Control (SYSCON), Chalmers University of Technology – hosts part of his research team Center for Construction Robotics – collaborative network involving Lund University researchers WASP and ELLIIT research networks – strategic national platforms for AI and robotics