جست‌وجوی استاد و پژوهشگر

Sina Sheikholeslamiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Distributed Systems
• +۳ مورد دیگر

Sina Sheikholeslami is a Researcher at the Division of Energy Systems, Department of Energy Technology at KTH Royal Institute of Technology. He works on leveraging AI for sustainability and climate action, focusing on projects like Beyond 2030 and OnStove. His affiliations include the KTH Climate Action Centre and Vinuesa Lab. He holds a PhD in Distributed Computing from KTH (2025), advised by Vladimir Vlassov, Amir Payberah, and Jim Dowling, with prior M.Sc. studies at Eindhoven University of Technology and KTH through the EIT Digital Master School. He also completed a B.Sc. in Computer Software Engineering at Amirkabir University of Technology. Research interests include distributed systems, machine learning, deep learning, and their applications in sustainable development. Notable work includes developing frameworks like AutoAblation for ablation studies and Importance-aware DPT for dataset partitioning, which earned the Best Artefact Award at DAIS 2023. His recent work explores using LLMs for ablation studies and weight initialization techniques from hyperparameter trials. Academic leadership roles include serving on the KTH PhD Chapter’s Board, EECS PhD Student Council, and committees such as the School Assembly and Third-Cycle Education Council. He is Sweden’s Local Representative for the EIT Digital Alumni Foundation. His teaching roles include assistant and teacher in courses like Data Mining and Data-Intensive Computing. He supervises multiple students, including those exploring topics like scalable model training with Ray and feature stores in Hopsworks. His research spans environmental monitoring (e.g., ExtremeEarth), public transit systems (DUGET), and interdisciplinary applications of ML in wood science and urban planning.

Pierre Nyquistمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +۷ مورد دیگر

Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC). Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020). Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools. Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM. Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۵ مورد دیگر

Dr. Gaoyang Dai is an Assistant Professor at Uppsala University's Department of Information Technology, specializing in Computer Systems research. His work focuses on real-time systems, embedded computing, and scheduling algorithms for complex task models. Research interests include: Deterministic timing analysis in distributed systems Priority inversion handling in multicore environments Non-preemptive node scheduling for DAG tasks Cyber-physical system design paradigms Deep learning applications in wireless networks Recent publications demonstrate expertise in IEEE Transactions and DATE conference proceedings, with particular emphasis on sporadic DAG task scheduling and resource sharing protocols. Collaborative work includes development of the TIMES-Pro toolchain for CPS implementation.

• Parallel Computing
• Programming Models
• System Software
• +۳ مورد دیگر

Mats Brorsson is a Professor at the Division of Software and Computer Systems , KTH Royal Institute of Technology. His research spans multiple areas of computer architecture and parallel computing, with a focus on system software, energy-aware architectures, and performance debugging tools. He is actively involved in projects like the PaPP ARTEMIS collaboration and coordinates the KTH-SICS Scalable Computing Systems initiative. Research Interests : Mats Brorsson's work primarily addresses parallel computing , task-based programming models (e.g., OpenMP), and energy-efficient computer architectures . He has made significant contributions to NUMA system optimization , work-stealing schedulers , and runtime systems for high-performance computing. Professional Activities : Mats Brorsson serves as coordinator for the PaPP ARTEMIS project and is a member of the KTH-SICS Collaboration in Scalable Computing Systems. His publications reflect deep engagement with task scheduling , cache coherence protocols , and adaptive resource management for parallel systems.

Ahmed Rezineمشاهده پروفایل

مدرس ارشد

• Computer Science
• Software Verification
• Machine Learning
• +۷ مورد دیگر

Ahmed Rezine is a Senior Lecturer and Associate Professor at the Department of Computer Science (IDA) , specifically within the Software and Systems (SAS) division at Linköping University . His research focuses on software verification , machine learning , and cybersecurity , particularly in the context of deep neural networks and embedded systems . Research Trends: His recent publications, such as VNN: Verification-Friendly Neural Networks (2024) and Trojans in Instruction Sets (2024), highlight his work at the intersection of AI robustness and hardware security . Earlier studies (2023-2017) emphasize formal verification of concurrent systems and GPU architectures , reflecting a consistent focus on parameterized systems and security analysis . Colleagues & Collaborations: Rezine collaborates with researchers in the Software and Systems group, part of the Wallenberg Autonomous Systems Program (WASP) . His work intersects with AI IDA and Embedded Systems (ESLAB) , focusing on autonomous systems and real-time processing .

Danupon Na Nongkaiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Graph Algorithms
• Dynamic Computing
• +۶ مورد دیگر

Dr. Danupon Na Nongkai is an Associate Professor in the Division of Theoretical Computer Science at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science. His research focuses on theoretical computer science, with specialization in graph algorithms for dynamic and distributed environments. Supported by prestigious grants including the Swedish Research Council's VR Young Researcher Grant (2015) and the European Research Council's Starting Grant (2016), his work spans approximation algorithms, communication complexity, game theory, verification, theoretical databases, quantum algorithms, and social network analysis. PhD in Algorithms, Combinatorics, and Optimization (ACO) from Georgia Tech (2011) Co-winner of Principles of Distributed Computing Doctoral Dissertation Award (2013) Research Interests: Dr. Na Nongkai investigates fundamental problems in graph theory and algorithm design, particularly in dynamic, distributed, and quantum computing paradigms. His research connects theoretical computer science with practical applications in network processing, optimization, and complexity analysis. Article Trends: Recent publications demonstrate expertise in near-linear time algorithms for shortest paths, cut problems, and connectivity in diverse computational models. Key themes include cross-paradigm optimization (dynamic/static, distributed/parallel, quantum), expander decomposition applications, and submodular function analysis. Scientific Recognition: FOCS 2022 Best Paper Award ERC Starting Grant recipient VR Young Researcher Grant PODC Doctoral Dissertation Award Advising & Grants: As a principal investigator, Dr. Na Nongkai has mentored numerous researchers through collaborative publications with 85 co-authors. His group's funding includes competitive national and European grants for cutting-edge algorithm research.

• Welding Mechanics
• Fatigue and Fracture Avoidance
• Fitness For Service
• +۵ مورد دیگر

Per Lindström Lussi is a Research Professor specializing in welding mechanics and structural integrity at Linnaeus University's Faculty of Technology, Department of Mechanical Engineering. With a doctoral degree in Computational Welding Mechanics from University West (2015), he focuses on Fatigue and Fracture Avoidance (FFA) and Fitness For Service (FFS) in marine and offshore structures. Education: Doctoral Thesis in Computational Welding Mechanics (2015), University West Licentiate of Naval Architecture and Ocean Engineering (2005), Chalmers University Master of Science in Marine Engineering (2001), Chalmers University Diploma of Commercial Management & Organization in Nautical Science (1999), Chalmers University Bachelor of Science in Marine Engineering (1991), Kalmar Maritime Academy International Welding Engineer certificate (2001), IIW Dr. Lindström Lussi's research focuses on welding mechanics, residual stress analysis, and structural integrity assessment of marine and offshore structures. His work bridges theoretical computational models with practical engineering applications, particularly in fatigue and fracture mechanics. He has extensive experience in both academic research and industrial applications, having worked with major organizations including Lloyd's Register, DNV GL, and Westinghouse Electric. His recent publications demonstrate a strong focus on computational welding mechanics, with particular emphasis on residual stress prediction, fracture mechanics, and additive manufacturing applications in marine contexts. The research shows progression from fundamental welding process modeling to advanced applications in structural integrity assessment and seaworthiness analysis. Professional Affiliations: Swedish delegate in IIW Working Unit C-X "Structural performances of welded joint - Fracture avoidance" (since 2006) Committee member, International Ship and Offshore Structures Congress (ISSC) Dr. Lindström Lussi leads research in the Welding Mechanics Laboratory and is active in the Smart Industry Group. His current projects include seaworthiness assessment of WAAM-manufactured marine propellers, FEA of residual stresses in welded structures, and investigation of NORM contamination in LNG fuel systems.

• Embedded Systems
• Real-time Systems
• Distributed Real-time Systems
• +۸ مورد دیگر

Hazem Ali is a Senior Lecturer at Halmstad University's School of Information Technology. He holds a Ph.D. in Electrical and Computer Engineering from Faculdade de Engenharia da Universidade do Porto (FEUP) and an M.Sc. in Computer Science and Engineering from Halmstad University. His research focuses on embedded systems, real-time systems, and dataflow programming models. He has expertise in hardware/software co-design, parallel computing, and optimization of real-time applications. Education: Ph.D. in Electrical and Computer Engineering (FEUP, Portugal) M.Sc. in Computer Science and Engineering (Halmstad University, Sweden) Recent publications highlight his work in cybersecurity for autonomous vehicles, GPU acceleration of MIMO systems, and optimization of dataflow models. His projects include ELLIIT B02 (Beyond 5G Wireless) and CyberInfra (Cybersecure Traffic Infrastructure). Proficiency in tools includes MATLAB, C/C++, Java, VHDL, and dataflow languages like CAL and Sigma-C, with extensive international experience in Sweden, Portugal, and Egypt.

• Computer Architecture
• Deep Learning Accelerators
• Heterogeneous Computing
• +۴ مورد دیگر

Pedro Petersen Moura Trancoso is a Full Professor at the Department of Computer Engineering , Chalmers University of Technology, Sweden. His research focuses on deep learning accelerators , heterogeneous computing , energy-efficient architectures , and memory system optimization for IoT and edge devices. Key projects: AutoPIM (autonomous vehicle accelerators), VEDLIoT (efficient AIoT), eProcessor (European processor ecosystem), PRIME (PIM systems) Collaborations: European Commission, Swedish Research Council, Swedish Foundation for Strategic Research Research Trends : Hybrid CNN/GPU/FPGA Acceleration On-Chip/Scratchpad Memory Optimization Adaptive Resource Allocation for Energy Efficiency Hardware-Software Co-Design for AIoT Publications demonstrate leadership in deep learning hardware , heterogeneous memory systems , and edge computing architectures . Key journals: IEEE ISPASS, ACM Computing Frontiers, DATE Conference.

Rodney Edvinssonمشاهده پروفایل

استاد

• Economic History
• International Trade
• Monetary Systems
• +۶ مورد دیگر

Rodney Edvinsson is a Professor at the Department of Economic History and International Relations at Stockholm University. His research spans multiple centuries of economic data, focusing on: Historical GDP reconstruction Monetary system evolution Demographic and climatic impacts on economies Gender economics Pre-industrial agricultural output Research Highlights: Edvinsson has pioneered long-term economic analyses through projects like: Constructing a 700-year Consumer Price Index for Sweden Analyzing Swedish GDP 1620-1800 using harvest data Studying gender labor dynamics during Sweden's 'housewife era' (1930-1970) Investigating copper standard monetary systems in early modern Sweden Publication Trends: His 15 most recent articles reveal sustained expertise in: Historical price indices Monetary policy continuity Demographic-economic interactions Gender role economics Climate impact modeling Pre-industrial market systems Key Contributions: Edvinsson's work has transformed historical economic analysis through: Creation of multi-century datasets Integration of climatic variables into economic models Re-evaluation of pre-industrial productivity Challenging traditional mercantilist narratives Developing time-use methodologies Exposing structural breaks in economic growth patterns

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

• Theoretical Computer Science
• Distributed Computing
• Approximation Algorithms
• +۴ مورد دیگر

Mohit Daga is a Researcher at KTH Royal Institute of Technology, Sweden, specializing in theoretical computer science, distributed computing, and algorithmic research. He holds an MS in Computer Science from IIT Madras (2018). His current role involves research engineering and supervising master thesis projects across Machine Learning, AI, and GPU Programming. Education: MS in Computer Science, Indian Institute of Technology Madras (2018) Research Interests: His work focuses on distributed algorithms, approximation methods, and dynamic systems. He has explored applications in network analysis, optimization, and medical imaging. Recent projects include developing consensus protocols for DAG-based systems and analyzing multi-shot broadcast stability in distributed networks. Awards: Google Summer of Code Scholarship (2020) Advising & Grants: Supervised 9 master’s theses since 2020. Research internships include Cystar Labs (IIT Madras), IMDEA Networks Lab, and Xerox Research. His work on resource allocation at Xerox led to a patented solution. Labs/Teams: Contributed to projects at KTH’s Department of Computer Science and collaborated with IMDEA Networks and Cystar Labs. Part of the team developing the AciSpace CubeSat payload integration system.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +۵ مورد دیگر

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

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.

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.