جست‌وجوی استاد و پژوهشگر

Jörgen Blomvallمشاهده پروفایل

دانشیار

• Financial Engineering
• Stochastic Programming
• Quantitative Finance
• +۳ مورد دیگر

Jörgen Blomvall is an Associate Professor at Linköping University's Department of Management and Engineering (IEI). His research focuses on optimal financial decision-making, accurate financial measurement, and stochastic optimization models applied to financial markets. He develops methods to enhance measurement accuracy for quantities like forward rates, default intensities, and local volatilities, which are critical for equity, interest, credit, and derivative markets. Blomvall's work integrates stochastic programming and dynamic programming to address portfolio optimization, risk management, and transaction cost reduction. He is affiliated with the Operations Management and Finance research group, exploring resource optimization in manufacturing and service industries. His key research areas include financial engineering, stochastic programming, and quantitative risk management. Recent work emphasizes improving dividend estimation from intraday quotes, reducing transaction costs in hedging strategies, and analyzing stage complexity in stochastic programming for portfolio choice. Blomvall's methodologies have advanced the modeling of systematic risks and optimal investment decisions under real-world market constraints. Blomvall has contributed extensively to the development of optimization-based frameworks for performance attribution and yield curve estimation. His articles frequently address practical applications of stochastic models in financial markets, balancing theoretical rigor with computational feasibility. Despite his prolific output, no specific academic awards or student advisees are documented in the provided texts. He is active in the Production Economics (PEK) research group at Linköping University.

• 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.

Björn Forsbergمشاهده پروفایل

استادیار

• Structural Bioinformatics
• Cryo-Electron Microscopy (Cryo-EM)
• Computational Biology
• +۷ مورد دیگر

Björn Forsberg is an Assistant Professor in the Department of Physics, Chemistry and Biology (IFM) at Linköping University, where he leads a research group in structural bioinformatics. He is affiliated with SciLifeLab Linköping and the National Supercomputer Centre (NSC), and is part of the Wallenberg National Program for Data-Driven Life Science (DDLS), supported by the Knut and Alice Wallenberg Foundation. His work bridges computational science and molecular biology, focusing on the development of novel methods for analyzing cryo-electron microscopy (cryo-EM) data. His research centers on understanding molecular life through data-driven approaches. He develops computational tools to analyze ensemble cryo-EM data, using spatial filtering and local correlation metrics to identify sources of structural variation and attribute them to biological mechanisms. His lab leverages high-performance computing resources, including the Berzelius supercomputer, to process large-scale datasets and improve the resolution and interpretability of molecular models. This work has broad implications for understanding diseases like Alzheimer's and for drug discovery. The recent publications highlight a strong trend in advancing cryo-EM methodology, particularly through software development (e.g., RELION) and the integration of machine learning and GPU computing. His work spans fungal metabolism, ion channel dynamics, chloroplast ribosomes, and foundational algorithmic improvements in 3D reconstruction. The research combines structural biology, bioinformatics, and computational physics to extract biological meaning from complex data. Scientific Awards and Recognition: Selected for the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) Supported by the Knut and Alice Wallenberg Foundation through DDLS and the PALS (Program for Academic Leaders in Life Science) network Björn Forsberg is actively building his research team, advising PhD and master’s students, and seeking postdoctoral researchers with backgrounds in computational biology, computer science, or related fields. His lab collaborates with leading experts in structural biology and uses national research infrastructures to push the boundaries of data-driven life science. He has no listed formal grants yet, but his program is funded through major foundation support. His research is conducted at the intersection of the Department of Physics, Chemistry and Biology (IFM), SciLifeLab Linköping, and the National Supercomputer Centre (NSC), forming a multidisciplinary environment for innovation in computational structural biology.

• 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.

• Nuclear Engineering
• Monte Carlo Simulations
• Neutron Transport
• +۶ مورد دیگر

Jan Dufek is an Associate Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on advancing numerical methods for Monte Carlo neutron transport simulations, with applications in nuclear reactor design and safety. Key areas include coupled simulations of thermal-hydraulic feedback, Monte Carlo burnup methods, fission source convergence acceleration, and fission matrix-based techniques. He also develops deterministic nodal nuclear data models using polynomial regression to handle multi-dimensional state variables efficiently. He teaches courses such as Monte Carlo Methods and Simulations in Nuclear Technology and Nuclear Reactor Physics , serving as an examiner and course responsible. His work contributes to projects like the McSAFE initiative, aiming to enhance high-performance Monte Carlo methods for reactor safety. Collaborations include studies on deep learning for nuclear fuel composition prediction and transient analysis using hybrid stochastic-deterministic approaches. His research bridges computational efficiency with practical reactor engineering challenges. Dr. Dufek is affiliated with the NRT (Nuclear Reactor Technology) group at KTH, fostering interdisciplinary advancements in reactor physics and computational methodologies.

Evelyn Otero Solaمشاهده پروفایل

دانشیار

• Sustainable Aviation
• Air Traffic Management
• Emissions Reduction
• +۵ مورد دیگر

Dr. Evelyn Otero Sola is an Associate Professor in Aeronautical Engineering at KTH Royal Institute of Technology, serving as Vice-Director of the Centre for Sustainable Aviation (CSA) and a member of the Executive Board of the Environmentally Compatible Air Transport System (ECATS) International Association. Her research focuses on sustainable aviation, particularly optimizing air traffic management (ATM) to reduce emissions (CO2 and non-CO2) and noise, with projects involving sustainable aviation fuels (SAF), hydrogen, AI, and drones. She leads the doctoral course FSD3831 Future Sustainable Aviation and the master’s course SD2830 Aircraft Performance and Air Traffic Management , emphasizing interdisciplinary approaches to sustainability. Key collaborations include the Swedish Aerospace Research Center (SARC) and initiatives like TREVOL, TRACE, CIDER, and KLIMAFLY. Her work bridges research, education, and industry to drive sustainable aviation solutions globally. Education details are not explicitly provided, but her academic contributions include over 15 publications since 2009, spanning CFD optimization, climate modeling, and aviation environmental impact. She advises 11 doctoral students in the first iteration of her course and plans to expand access internationally. Her initiatives aim to create a skilled workforce for sustainable aviation through holistic education and cross-sector networks.

• molecular processes in flow and wetting
• sampling of conformational transitions in biomolecules
• parallel algorithms for molecular dynamics simulation

Berk Hess is a Professor at the Royal Institute of Technology (KTH) in Stockholm. His research focuses on molecular processes in flow and wetting, conformational transitions in biomolecules, and parallel algorithms for molecular dynamics simulations. He coordinates and teaches advanced courses in molecular dynamics, biotechnology modeling, and simulation of biological systems. His work contributes to computational biophysics and high-performance computing in life sciences.

• 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.

Patrick Normanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Molecular Response Theory
• Computational Spectroscopy
• +۵ مورد دیگر

Patrick Norman is a Professor and Head of the Division of Theoretical Chemistry and Biology at KTH Royal Institute of Technology, Stockholm, Sweden. He also serves as Director of the PDC Center for High Performance Computing. His research focuses on advanced time-dependent molecular response theory methods, with applications in optical and X-ray spectroscopy. Coordinates VeloxChem and Gator HPC software projects Leads EU-EIC project OMICSENS (2023–2026) for lung cancer biosensors Principal Investigator in EU-ITN COSINE (2018–2021) and KAW network CoTXS (2014–2019) His theoretical work includes resonance convergent response theory via Liouville equations and Ehrenfest theorem, implemented in quantum chemistry software for linear/nonlinear spectroscopy. He co-authored the graduate textbook Principles and Practices of Molecular Properties (Wiley, 2018) and organizes international PhD schools on molecular response properties. Recent publications emphasize fragment state optimization, solvation effects, chiral luminescent materials, and amyloid ligand binding simulations. His work bridges quantum chemistry, HPC, and biomedical applications through tools like Gator and VeloxChem .

Björn Lisperمشاهده پروفایل

استاد

• Computer Science
• Software Engineering
• Real-Time Systems
• +۵ مورد دیگر

Björn Lisper is a Professor at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Computer Science and Software Engineering. His research focuses on formal methods, real-time systems, embedded software, static analysis, and high-level synthesis. He has contributed to advancements in WCET analysis, machine learning applications in testing, and compiler optimization techniques. His work emphasizes practical industrial applications, particularly in automotive and multi-core systems. Research Interests: Real-Time Systems & WCET Analysis Static Program Analysis High-Level Synthesis for FPGAs Machine Learning in Software Testing Formal Methods & Verification Embedded Systems Design Publications span topics like neural network accelerators, automated testing frameworks, and compiler optimizations, reflecting a blend of theoretical and applied computer science. His work is characterized by collaboration between academia and industry to bridge gaps in embedded software predictability and performance.

• 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.

Henrik Erikssonمشاهده پروفایل

استاد

• Artificial Intelligence
• Epidemiology
• Data Science
• +۳ مورد دیگر

Henrik Eriksson is a Professor and Head of the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on epidemiological modeling, societal resilience, and semantic web technologies. He has contributed to projects analyzing infectious disease transmission dynamics and pandemic response strategies, leveraging data science and computational methods. Key affiliations include the IDA divisions of Artificial Intelligence and Integrated Computer Systems (AIICS), and his work intersects with public health informatics, urban resilience, and high-performance computing. Recent publications address real-time forecasting of disease outbreaks and guidelines for resilient urban systems. He leads interdisciplinary initiatives at LiU, fostering collaborations in AI-driven solutions for societal challenges.

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.