جست‌وجوی استاد و پژوهشگر

Paul Townendمشاهده پروفایل

دانشیار

• Energy-efficient distributed systems
• Sustainable computing
• Cloud computing
• +۷ مورد دیگر

Paul Townend is an Associate Professor at Umeå University , Sweden, with a Docent qualification. He leads the Green Distributed Computing group and serves as Research Leader for the Autonomous Distributed Systems (ADSLab) lab. Scientific Coordinator for Horizon Europe COGNIT Co-Manager & Scientific Advisor for WASP WARA-Ops Member of WASP Graduate School Management Team His research focuses on energy-efficient and sustainable distributed systems , with a current emphasis on Edge-Cloud integration . Additional areas include fault tolerance, provenance, data center optimization, and simulation . He has supervised 5 PhD students and 2 postdocs , with expertise in IoT, machine learning for cloud management, and microservices . Best Paper Awards at SOSE 2013 and ISORC 2012 Principal Investigator for grants totaling over $5.8M , including projects like Adaptive Monitoring of Streaming Data and Energy-Aware Cloud-Edge Management

Petter Ericsonمشاهده پروفایل

پژوهشگر

• Responsible AI
• Graph Theory
• Formal Grammars
• +۴ مورد دیگر

Petter Ericson is a Researcher at the Department of Computer Science , Umeå University , specializing in Responsible AI . His work bridges graph theory , formal grammars , and ethics , with interdisciplinary interests in music and sociotechnical systems . Affiliated with the Responsible AI Research Group , he explores the alignment of AI with human values. Ericson’s research spans formal computational models , including hyperedge replacement grammars and order-preserving DAGs , alongside AI ethics and fairness frameworks . His publications address sociotechnical limits in AI safety, artistic shifts through AI, and graph language learnability. He has contributed to interdisciplinary projects like Concordia , a musical XR instrument for planetary data sonification, and Musereduce , a hierarchical music analysis framework. Ericson applies computational methods to Bayesian music theory and jazz harmony datasets.

• Multi-objective optimization
• Digital twins
• Manufacturing systems
• +۷ مورد دیگر

Bandaru Sunith is an Associate Professor at the University of Skövde's School of Engineering Science, with previous affiliation to The Virtual Systems Research Centre (closed May 2017). His research spans multi-objective optimization, digital twins, and knowledge-driven decision support systems for manufacturing applications. He actively leads the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) research profile and contributes to the ADOPTIVE project focused on vehicle ergonomics optimization. Dr. Sunith's research interests include: Multi-objective optimization and evolutionary algorithms Digital twin frameworks for manufacturing systems Knowledge discovery and visualization for decision support Anomaly detection in industrial processes Factory layout optimization integrating human well-being metrics His recent publications (2023-2025) demonstrate a clear trend toward integrating advanced AI techniques with traditional optimization methods. The research increasingly focuses on practical industrial applications, particularly in automotive manufacturing, with strong emphasis on translating theoretical advances into usable tools. His work bridges the gap between computational optimization and real-world manufacturing challenges, often incorporating human factors considerations. Dr. Sunith has secured significant research funding from Swedish innovation agencies: Virtual Factories with Knowledge-Driven Optimization (VF-KDO) funded by Knowledge Foundation (Grant 2018-0011) Integrated Manufacturing Analytics Platform for Predictive Maintenance (IMAP) funded by Vinnova (Grant 2021-02537) LITMUS project on human-centric sustainable production funded by Knowledge Foundation He has supervised multiple researchers including Henrik Smedberg and Mahesh Kumbhar, resulting in collaborative publications and the development of practical tools like Mimer - a web-based platform for knowledge discovery in multi-criteria decision support. His research group maintains strong industry connections, particularly with automotive manufacturers in Sweden.

Victor Lagerkvistمشاهده پروفایل

دانشیار

• Computational Complexity
• Constraint Satisfaction Problems (CSP)
• Theoretical Computer Science
• +۳ مورد دیگر

Victor Lagerkvist is an Associate Professor at the Department of Computer Science (IDA) , Linköping University, Sweden. He is affiliated with the Theoretical Computer Science Laboratory (TCSLAB) and the Artificial Intelligence and Integrated Computing Systems (AIICS) division. His research focuses on the algebraic method for analyzing computational complexity , particularly in constraint satisfaction problems (CSPs), SAT, and graph homomorphism problems. PhD in Computer Science (2016, Linköping University) Habilitation (2020, Linköping University) His recent work investigates fine-grained complexity , twin-width , and universal algebra to improve algorithms for NP-hard problems. Publications span topics like propositional abduction, Allen's interval algebra, and semiring-based dynamic programming. His scientific awards include the Swedish Research Council Starting Grant (2020) and the 2017 Young Researcher Prize from the Ruth and Nils-Erik Stenbäck Foundation. He supervises PhD students such as Leif Eriksson and serves as a secondary supervisor for others at Linköping University and Université de Lorraine.

• Machine Learning
• Data Mining
• Graph Theory
• +۳ مورد دیگر

Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.

• 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 .

• Graph Data Mining

Tianyi Zhou is a doctoral student and researcher at the Division of Theoretical Computer Science , KTH Royal Institute of Technology, Sweden. Supervised by Professor Aristides Gionis, his work is funded by the ERC project REBOUND . He is also affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP). Research Interests Graph data mining Recent Article Trends His recent publications explore opinion dynamics through timeline algorithms and low-rank updates (2024), as well as heterogeneous information networks in social coding (2021). These works bridge graph theory, social network analysis, and computational modeling. Teaching Roles Assistant for Advanced Algorithms (DD2440) Assistant for Introduction to the Philosophy of Science and Research Methodology for Computer Scientists (DA2210) Assistant for Machine Learning (DD2421) Assistant for Software Engineering Fundamentals (DD2480) Grants Research supported by the ERC project REBOUND .

• Algorithms
• Data Mining
• Graph Mining
• +۲ مورد دیگر

Aristides Gionis is a Professor at KTH Royal Institute of Technology and an adjunct professor at Aalto University, affiliated with the Division of Theoretical Computer Science. His research group comprises postdocs, doctoral students, and research engineers focused on computational social science and data-driven methodologies. His core research spans: Algorithm design and analysis Large-scale data mining techniques Graph structure extraction and pattern discovery Social network dynamics and influence modeling Mitigation of algorithmic bias in digital ecosystems Current projects include the ERC-funded REBOUND initiative examining polarization mechanisms in online media, supported by WASP and EU SoBigData++ grants. His work bridges theoretical computer science with real-world societal challenges in digital platforms. Professor Gionis mentors seven doctoral researchers and two postdocs while examining Master's degree projects across multiple KTH programs. His teaching portfolio includes advanced Machine Learning courses and program-integrating computer science instruction.

Erik Schaffernichtمشاهده پروفایل

مدرس ارشد

• Robotics
• Sensor Systems
• Machine Learning
• +۶ مورد دیگر

Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).