جست‌وجوی استاد و پژوهشگر

Zhonghai Luمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +۷ مورد دیگر

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

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

• Power System Control
• Smart Grids
• Communication Systems
• +۱۱ مورد دیگر

Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Panagiotis Papapetrouمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Time Series Analysis
• +۵ مورد دیگر

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

Maja Elmgrenمشاهده پروفایل

مدرس ارشد

• Chemistry Education Research
• Physics Education Research
• Higher Education
• +۸ مورد دیگر

Maja Elmgren is a Senior Lecturer in Physical Chemistry and Educational Developer at Uppsala University's Department of Chemistry and Faculty of Science and Technology. Since 2009, she has directed the Council for Educational Development (TUR), focusing on academic leadership, chemistry education, and scholarly teaching. Her research spans physical chemistry, chemistry/physics education, and higher education development. Research Interests: Leadership in higher education and doctoral assessment Educational expertise in academia Student learning in thermodynamics and kinetics Chemistry education through international cooperation Quantum mechanics conceptualization Thermal phenomena visualization via infrared cameras Article Trends: Recent publications highlight interdisciplinary learning (chemistry-mathematics), assessment reform in doctoral education, student-centered pedagogy, and technological tools in science education. Subfields include mathematical modeling, entropy understanding, and VR-funded projects on science teacher roles. Scientific Awards: Medal for Merit from Uppsala Science and Technology Student Union (2011) Distinguished Teaching Award from Uppsala University (2001) Academic Roles: Director, Council for Educational Development (TUR) since 2009 Author of "Academic Teaching" (2018) and "Universitetspedagogik" (2016) International collaborations with IUPAC and VR-funded projects Specializes in entropy/thermodynamics education and infrared camera applications

• Artificial Intelligence
• Machine Learning
• Computer Science
• +۶ مورد دیگر

Fredrik Heintz is a professor at Linköping University's Department of Computer and Information Science within the Faculty of Science & Engineering. His research bridges artificial intelligence, education, and healthcare, focusing on AI literacy, synthetic data generation, and autonomous systems. Key affiliations: Linköping University (Faculty of Science & Engineering, Department of Computer and Information Science) Research Interests: Heintz's work spans multiple domains: Developing frameworks for AI literacy in K-12 education Creating fair synthetic healthcare data using GANs and bias-transforming techniques Advancing autonomous 3D exploration algorithms for dynamic environments Benchmarking tools for fairness, utility, and explainability in AI models Stream reasoning for real-time data analytics and knowledge extraction Evaluating ethical implications of AI in teacher education Scientific Contributions: His publications highlight collaborations with international researchers and significant grants from the Swedish Research Council, Knut and Alice Wallenberg Foundation, and VINNOVA. Notable projects include TransFusion for time-series generation, Bt-GAN for fair healthcare data, and DAEP for dynamic exploration planning. Funded by Wallenberg AI, Autonomous Systems and Software Program (WASP) ELLIIT Excellence Center at Linköping-Lund Mistra Geopolitics research program

Tobias Dalbergمشاهده پروفایل

مدرس ارشد

• sociology of education
• social stratification
• higher education policy
• +۳ مورد دیگر

Tobias Dalberg is a Senior Lecturer at Uppsala University's Department of Pedagogy, Didactics and Educational Sociology, with research focusing on sociology of education, social stratification, and higher education policy. His work examines gender differentiation in course selection, curricular blind spots, and historical transformations in academic disciplines. Contact: tobias.dalberg@edu.uu.se | Personal Website Research: Utilizes graph theory, natural language processing, and geometric data analysis for educational pathway studies Current research areas include: Gendered patterns in major selection and course enrollment Educational policy analysis (1950-2020) Academic field institutionalization processes Disciplinary legitimacy struggles (1935-1980) Recent publications (2021-2024) reveal trends in: Higher education marketization and gender segregation Curriculum structure impacts on student trajectories Disciplinary evolution in social sciences and humanities Assessment consistency in educational evaluation As member of international research collaborations, Dalberg works with networks studying educational stratification and transnational academic markets. His methodological contributions include applications of network analysis for studying epidemic spread in educational contexts.

Vladimir Vlassovمشاهده پروفایل

استاد

• Machine Learning
• Data-intensive computing
• Cloud computing
• +۷ مورد دیگر

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

Olaf Hartigمشاهده پروفایل

دانشیار

• Database Systems
• Semantic Web
• Data Management
• +۵ مورد دیگر

Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding

• Business Process Management
• Process Mining
• Object-Centric Process Mining
• +۶ مورد دیگر

Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.

Markus H. Flierlمشاهده پروفایل

دانشیار

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

Alexandros Sopasakisمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۹ مورد دیگر

Alexandros Sopasakis is a Senior Lecturer at the Department of Mathematics, Faculty of Engineering (LTH), Lund University. He is affiliated with multiple research initiatives including eSSENCE (the e-Science Collaboration), ELLIIT (Linköping-Lund IT and mobile communication initiative), and LU Profile Areas in Natural and Artificial Cognition. Senior Lecturer in Mathematics Principal Investigator for eSSENCE Active in LU Profile Areas: Climate, Health, and AI Research Interests: His work bridges machine learning, dynamical systems, and applied mathematics, focusing on: Stochastic modeling of complex systems Attention-based forecasting (Transformers) Diffusion models for synthetic data generation Graph neural networks in transportation and agriculture Anomaly detection in signal processing Climate modeling under non-Gaussian noise Scientific Awards: Recipient of three patents in: 5G/6G mobility devices Beam management measurement optimization Traffic network forecasting

• Intelligent Transportation Systems
• Traffic Flow Modeling
• Linear Parameter Varying Systems
• +۷ مورد دیگر

Professor Balázs Adam Kulcsár is a faculty member in the Automatic Control research group at the School of Electrical Engineering and Computer Science, Chalmers University of Technology. With 104 publications and involvement in 34 research projects, he is a prominent researcher in intelligent transportation systems. His work spans multiple domains within transportation engineering and control theory, with significant contributions to traffic flow modeling, electric vehicle routing, and advanced control systems. Professor Kulcsár's research primarily focuses on intelligent transportation systems design, traffic flow modeling for control, Linear Parameter Varying systems, and failure diagnostics. His work demonstrates a strong integration of control theory with practical transportation challenges, particularly in the context of electric mobility and sustainable transportation. Recent research shows a growing emphasis on machine learning applications for transportation optimization, electric vehicle infrastructure, and urban traffic management. Analysis of his recent publications reveals a clear trajectory toward sustainable transportation solutions, with electric vehicle charging infrastructure, fleet management, and public transit optimization as dominant themes. His work increasingly incorporates machine learning techniques, particularly graph neural networks and reinforcement learning, to address complex transportation challenges. The research demonstrates strong interdisciplinary collaboration across engineering disciplines, with a focus on practical implementation of theoretical advances. Professor Kulcsár leads and participates in numerous research projects focused on future transportation systems, including projects on electric mobility, traffic optimization, and intelligent transportation infrastructure. His research group collaborates extensively with industry partners like Volvo and Heart Aerospace, as well as with other academic institutions. Current projects include Rethinking the Sustainability of V2G, Quantum computing for future mobility solutions, and Digital Twin for Energy Prediction. His research group maintains strong connections with transportation industry stakeholders and contributes to major initiatives such as the Transport Area on Advance project, which aims to achieve leading competence in future green, safe, and efficient transport systems. The team operates at the intersection of theoretical control systems and practical transportation applications, with particular expertise in modeling complex traffic phenomena and developing implementable control solutions.