جستجوی استاد راهنمای دانشگاهی

• Cloud Computing
• Big Data Pipelines
• Machine Learning
• +۴ مورد دیگر

Mihhail Matskin is a Professor at the Royal Institute of Technology (KTH) within the Department of Software Logic & Computer Systems. He specializes in cloud computing, big data pipelines, machine learning, and distributed systems. His work emphasizes optimizing cloud resource allocation, developing graph-based cost models, and advancing AI-driven systems like SQL recommendation engines and relation extraction frameworks. He actively contributes to KTH's educational mission through roles as examiner and course manager in advanced computer science and engineering courses, including Distributed AI, Cloud Storage Optimization, and Big Data Workflow Design. His research spans over two decades, focusing on scalable data management solutions, edge computing applications in healthcare, and semantic analysis of social media. Notable projects include the DataCloud initiative for big data pipeline orchestration and the DEF-PIPE DSL visualization framework. Matskin’s work bridges theoretical advancements with practical implementations, addressing challenges in reproducibility of LLM-based systems, containerized edge computing, and constraint programming for robotics. His academic contributions include pioneering studies on cloud cost modeling, rule-based storage tiering, and contrastive learning for NER tasks. He has advised numerous advanced-level degree projects across computer science, embedded systems, and communication technologies. Matskin’s lab focuses on innovative solutions at the intersection of distributed systems, AI, and data engineering.

György Dánمشاهده پروفایل

استاد

• Applied Game Theory
• Critical Infrastructure Security
• Edge Computing
• +۵ مورد دیگر

György Dán is a Professor in Teletraffic Systems at KTH Royal Institute of Technology, Stockholm, Sweden. He holds a Ph.D. in Telecommunications from KTH (2006) and has extensive academic leadership roles, including Director of Third Cycle Education. His research focuses on networked systems design, with expertise in critical infrastructure security, edge computing, and cyber-physical systems. Dán has supervised over 20 PhD students and led major projects like VR DICE6G and REACT. He has authored/co-authored over 150 publications, receiving awards such as the Ericsson Research Foundation Award (2010-2014) and the 2020 KTH PhD Supervisor of the Year. Education Ph.D. in Telecommunications, KTH Royal Institute of Technology (2006) M.Sc. in Business Administration, Corvinus University of Budapest (2003) M.Sc. in Computer Engineering, Budapest University of Technology and Economics (1999) Research Interests His work spans applied game theory, smart grid security, mobile edge computing, and adversarial machine learning. Current projects include resilient edge computing systems and secure AI for cyber-physical infrastructures. Awards & Recognition Best Paper Awards at SNCNW 2025, IEEE SmartGridComm 2014, and IEEE Infocom 2008 Fulbright Visiting Scholar (University of Illinois, 2012-2013) Invited Professor at EPFL (2014-2015) Labs & Collaborations He leads the Network and Systems Engineering Division at KTH, collaborating with industries like Ericsson and ABB. His group focuses on experimental platforms like the GreenEyes testbed and stateful serverless edge computing.

• Predictive Maintenance
• Mixed Reality Applications
• Human-Robot Collaboration
• +۵ مورد دیگر

Bernard Schmidt is an active researcher at the University of Skövde's School of Engineering Science, specializing in Production and Automation Engineering. His work bridges cutting-edge mixed reality applications with industrial predictive maintenance systems, focusing on sustainable manufacturing solutions. Affiliated with the Virtual Systems Research Centre (closed 2017) and Virtual Engineering Research Environment, he contributes to multiple research profiles including VF-KDO and Virtual Production Development. His research interests center on predictive maintenance methodologies , human-robot collaboration interfaces , and cloud-enhanced manufacturing systems . Schmidt develops mixed reality training environments that reduce production line disruption while improving operator safety. His work integrates double ball-bar measurements with machine learning to create population-based maintenance models that achieve 40% cost reduction compared to traditional approaches. Current projects include ACCURATE (2021-2025) and VF-KDO (2019-2026), funded by Sweden's Knowledge Foundation. Analysis of Schmidt's 15 most recent publications reveals a strong trend toward real-time industrial decision support systems using mixed reality visualization. His work consistently addresses sustainable manufacturing through energy-efficient robotics and predictive maintenance frameworks that leverage cloud computing. Key subfields include virtual sensor integration, collision avoidance algorithms, and multi-objective optimization for robotic cells - demonstrating practical applications in automotive and elevator manufacturing. Research funding highlights include: Knowledge Foundation (KKS) projects: ACCURATE (2021-2025), VF-KDO (2019-2026), Virtual Factory with Knowledge-Driven Optimization (2018-2026) European Commission grant (637107) IPSI Industrial Research School collaboration with Volvo GTO and Volvo Cars Schmidt actively supervises bachelor's degree projects at ASSAR Industrial Innovation Arena in Skövde, with recent work involving Natalia Sempere Maciá and Celia Redondo Verdú. His research integrates with ABB Robotics systems through partnerships with experts like Tommy Y. Svensson. Current laboratory work focuses on HoloLens 2 implementations for robotic cell visualization and safety system development for human-robot collaborative environments.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +۴ مورد دیگر

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +۳ مورد دیگر

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .