جست‌وجوی استاد و پژوهشگر

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.

Ioana-Oriana Berceaمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Data Structures
• Randomized Algorithms
• +۶ مورد دیگر

Ioana-Oriana Bercea is an Assistant Professor at the Division of Theoretical Computer Science (EECS), KTH Royal Institute of Technology, and a member of the Digital Futures Faculty. She holds a PhD in Computer Science from the University of Maryland, with prior postdoctoral research at the IT University of Copenhagen and Tel Aviv University. Her research focuses on Theoretical Computer Science, including Data Structures (e.g., Bloom filters), Randomized Algorithms (e.g., hashing), and Computational Geometry (e.g., TSP). Education : PhD in Computer Science, University of Maryland (advisor: Samir Khuller) Master's in Computer Science, University of Maryland (advisor: Aravind Srinivasan) Bachelor of Science in Mathematics (Honors) and Computer Science, University of Chicago Research Interests : She explores scalable data management (e.g., Bloom filters), algorithm design for clustering and hashing, and geometric optimization problems. Her work emphasizes theoretical foundations with practical applications. Key Achievements : VR Starting Grant (2024) for the project "DataTech" Best Artifact Award at ACM SIGMOD 2023 for the InfiniFilter paper Advising & Grants : Advises PhD student Jonas Østergaard Klausen (joint with Mikkel Thorup and Jacob Holm) Lead researcher on grants exploring data storage and algorithmic efficiency Labs & Teams : Active in KTH's Theoretical Computer Science group and collaborates with the Digital Futures initiative to advance cross-disciplinary digital technologies.

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.

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.

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.

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.

• Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +۳ مورد دیگر

Jemima M. Tabeart is an Assistant Professor at TU Eindhoven, affiliated with the Centre for Analysis, Scientific Computing and Applications . Her research focuses on numerical linear algebra, data assimilation, and covariance matrices. She actively contributes to outreach and scientific crafting, and advises students on final projects in computational science. Education: PhD (2016–2019): University of Reading (supervised by Prof. Sarah Dance and others) MRes (2015–2016): Mathematics of Planet Earth CDT (Imperial College London/Reading) MMath (2011–2015): University of Bath with study abroad at Université Grenoble Alpes Research Interests: Large-scale numerical linear algebra and its applications Development of efficient preconditioners for 4D-Var and data assimilation systems Treatment of covariance matrices in computational models Interdisciplinary applications in environmental and meteorological modeling Teaching & Leadership: Organizing a 2025 Research Semester Programme at CWI on numerical analysis and machine learning Teaching courses on calculus, linear algebra, and optimization at TU Eindhoven Co-lecturing the MasterMath course on Advanced Numerical Linear Algebra Professional Activities: Member of the 4TU.AMI SRI in Bridging Numerical Analysis and Machine Learning Principal investigator in a network funded by the INI Network Support for Mathematical Sciences Advocate for low-carbon travel, documenting her journeys on her blog

• Embedded Systems
• FPGA Design
• Fault Tolerance
• +۵ مورد دیگر

Johnny Öberg is an Associate Professor at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology. He specializes in embedded systems, FPGA design, and fault-tolerant hardware architectures. His research focuses on radiation effects in electronics, machine learning acceleration, and network-on-chip (NoC) systems. He teaches and examines courses such as Computer Systems Architecture (IS2202), Embedded Hardware Design in ASIC and FPGA (IL2225), and Embedded Systems Design Project (IL2232). His work often bridges theory and practice, emphasizing real-world applications in aerospace, automotive, and IoT domains. Key research trends include improving reliability in SRAM-FPGAs through statistical fault injection, developing hardware-accelerated machine learning frameworks, and optimizing NoC architectures for predictable performance in mixed-criticality systems. Recent projects include the SAFEPOWER architecture for energy-efficient systems and collaborations on structural health monitoring using Lamb wave analysis. No scientific awards are explicitly mentioned in the provided texts. Johnny has advised on multiple degree projects but no specific student names are listed. His contributions include foundational work in GALS (Globally Asynchronous, Locally Synchronous) communication bridges and protocol grammars for low-power implementations. Labs/teams: Active involvement in the ICES (Innovative Centre for Embedded Systems) and the Suaineadh project for space-deployable structures. Collaborates on interdisciplinary initiatives like the ABB NoC and Panacea NoC prototypes.

Stefan Pålssonمشاهده پروفایل

مدرس

• University Pedagogy
• Higher Education Teaching
• Computational Science Education
• +۷ مورد دیگر

Stefan Pålsson is a Lecturer at the Department of Information Technology; Computational Science, Uppsala University, within the Faculty of Science and Technology. He serves as director of studies and is deeply engaged in university pedagogy, contributing through teaching, educational development, and leadership in pedagogical initiatives. His research and professional work focus on enhancing teaching quality in higher education, particularly in computational science and engineering disciplines. Key interests include active learning, gender-aware curriculum reform, and institutional support for pedagogical excellence. The recent publications reflect a strong trend in educational research, emphasizing faculty development, compulsory training for teachers, and systemic reforms in engineering education. The work spans journals and conference proceedings in education and engineering education, with a clear focus on practical, scalable pedagogical interventions. Scientific Awards: Excellent teacher, Uppsala University since 2013 Stefan Pålsson provides pedagogical support at the faculty level through TUR (the Faculty of Science and Technology's University Pedagogical Council) and the Unit for University Pedagogy. He advises on teaching practices and curriculum development, though no formal PhD or Master’s students are listed. No grants are mentioned in the provided text. He is actively involved in institutional educational development teams, including TUR and the Unit for University Pedagogy, where he contributes to shaping teaching standards and supporting academic staff in adopting evidence-based teaching methods.

Magnus Långمشاهده پروفایل

استادیار

• Formal Methods
• Concurrency Theory
• Software Verification
• +۲ مورد دیگر

Magnus Lång is an Assistant Professor with doctoral duties at Uppsala University's Department of Information Technology, Division of Computer Systems. His research focuses on formal methods, concurrency theory, and software verification. Research Interests: Specializes in stateless model checking for concurrent programs. Explores dynamic partial order reduction techniques. Investigates graph-based algorithms for parallel verification. Develops optimized methods for sequential consistency. Contributes to event-driven multi-threaded program analysis. Contact: Email: magnus.lang@it.uu.se Postal Address: Box 337, 751 05 Uppsala

• Computational Linguistics
• Natural Language Processing
• Terminology Management
• +۳ مورد دیگر

Jody Foo is a Lecturer at the Department of Computer and Information Science, Linköping University. They are affiliated with the Natural Language Processing Lab (NLPLAB) and the Human-Centered Systems (HCS) research group. Their work bridges computational methods with terminology management. Education: Licenciate degree in Computational Linguistics; Magistersexamen (~MSc) in Cognitive Science. Research Focus Jody’s research centers on computational terminology management , involving: Monolingual and bilingual automatic term extraction Term clustering and semantic mirroring Terminology harmonization for standardization Integration of machine learning and language models Applications in patent analysis and SMT (Statistical Machine Translation) Publication Trends Their publications (2003–2014) span computational linguistics, NLP, and terminology engineering. Notable areas include SVM-based term detection, semantic mirroring for patent terminology, and corpus augmentation for machine translation. Collaborative works with Merkel, Andersson, and others highlight interdisciplinary approaches. Contact Email: jody.foo@liu.se | Office: Room E 3F:482, Linköping

Ehsan Miandjiمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

David Bromanمشاهده پروفایل

استاد

• Programming Languages and Compilers
• Real-Time Systems and Cyber-Physical Systems
• Probabilistic Machine Learning
• +۲ مورد دیگر

David Broman is a Professor at KTH Royal Institute of Technology in Sweden, leading the Division of Software and Computer Systems (SCS) and serving as Associate Director Faculty for Digital Futures. He holds a Ph.D. in Computer Science from Linköping University (2010), with visiting roles at UC Berkeley (2012–2016) and Stanford University (2023–2024). His research spans programming languages, real-time systems, and probabilistic machine learning, emphasizing secure and efficient compilation techniques. Education: Ph.D. in Computer Science (Linköping University, 2010) Visiting Roles: UC Berkeley (2012–2014 part-time), Stanford University (2023–2024) Research Interests: David focuses on advancing programming language theory, compilers, and probabilistic methods. Key areas include transformer-based compilation, cyber-physical systems, and formal verification. His work bridges theory and practice, addressing challenges in software reliability and efficiency. Awards: Notable recognitions include the Best ETAPS Paper Award (2023), Distinguished Artifact Award (ESOP 2022), and Teacher of the Year (KTH 2017). His contributions also include grants from the Swedish Foundation for Strategic Research and Wallenberg AI, Autonomous Systems and Software Program (WASP). Advising & Grants: David mentors graduate students (e.g., Gizem Caylak, Viktor Palmkvist) and leads projects funded by WASP, focusing on next-generation compilation techniques. He also contributed to tools like Miking, Timed C, and FlexPRET. Labs & Teams: His team develops frameworks for probabilistic programming and real-time systems, emphasizing collaboration with industry and academia through initiatives like Digital Futures at KTH.

August Ernstssonمشاهده پروفایل

مدرس

• Parallel Programming
• Heterogeneous Computing Systems
• High-Performance Computing
• +۳ مورد دیگر

August Ernstsson is a Lecturer at Linköping University’s Department of Computer and Information Science (IDA). He is affiliated with the Graduate School in Computer Science (CUGS) and research groups Programming Environments (PELAB) and Software and Systems (SAS). His research focuses on parallel programming models, heterogeneous computing systems, and optimizing software for FPGA and GPU architectures. Notable contributions include frameworks for high development productivity (EXA2PRO) and deterministic parallel algorithms. Ernstsson’s work emphasizes performance portability, compiler design, and abstraction layers for heterogeneous systems. He collaborates with international teams on projects like pattern-based programming abstractions and FPGA-accelerated systems.