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Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.

• Multi-agent systems
• Nonlinear control systems
• Systems theory
• +4 more

Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.

• Numerical Linear Algebra
• Matrix computations
• Nonlinear eigenvalue problems
• +10 more

Jonas Bergström is a Professor in the Department of Mathematics at Stockholm University specializing in Algebra, Geometry, Topology, and Combinatorics. His research focuses on arithmetic geometry, moduli spaces, Siegel modular forms, and number theory, with extensive collaborations across international institutions including KTH Royal Institute of Technology. His research interests span algebraic geometry, topology, combinatorics, and number theory, with particular emphasis on moduli spaces of curves, abelian varieties, Siegel modular forms, and arithmetic geometry. Bergström's work bridges theoretical mathematics with computational approaches, often developing algorithms for complex mathematical structures. His research group actively explores commutative and homological algebra, complex and real algebraic geometry, arithmetic geometry, homotopy theory, and Ramsey theory. The most recent publications reveal a strong focus on cohomology of moduli spaces, Siegel modular forms, abelian varieties over finite fields, and L-functions. His work demonstrates a consistent pattern of combining algebraic geometry with number theory, particularly investigating arithmetic properties of algebraic varieties and developing computational methods for modular forms. The research shows increasing emphasis on algorithmic approaches and connections to theoretical physics through moduli space cohomology. Bergström has supervised several PhD students including Sjoerd de Vries (current), Stefano Marseglia, and Olof Bergvall (with Prof. Carel Faber). He currently mentors postdoctoral researchers Séverin Philip and Thomas Wennink, while former postdocs include Angelina Zheng, Valentijn Karemaker, Oliver Leigh, and Alex Samuel Bamunoba. His research is supported through collaborations with major mathematical networks including the Nordic number theory network and joint seminars with KTH. He is affiliated with the Algebra and Geometry Seminar (KTH and SU) and maintains active research connections through multiple collaborative projects, including joint work with Gerard van der Geer and Carel Faber on Hecke operators and Siegel modular forms. Bergström also contributes to open mathematical research through GitHub repositories containing computational results on cohomology of moduli spaces.

• Algebra
• Geometry
• Topology
• +5 more

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.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Linda Sandblad is Associate Professor at the Department of Chemistry, Umeå University, and Research Fellow at Molecular Infection Medicine Sweden (MIMS). She directs the Umeå Centre for Electron Microscopy and leads research into cytoskeletal organization using advanced imaging techniques. Her research investigates: FilP protein dynamics in Streptomyces coelicolor bacterial model Keratin filament assembly in epithelial tissues Microtubule dynamics and associated protein functions Advanced electron microscopy method development Recent publications demonstrate applications of cryoelectron tomography, fluorescence imaging, and structural analysis across bacterial and eukaryotic systems, with particular focus on membrane proteins, cellular division mechanisms, and host-pathogen interactions. She received the Bo and Barbro Hammarström Prize for outstanding contributions to research infrastructure development.

• Cytoskeleton
• Electron Microscopy
• Bacterial Model Organisms
• +2 more

Professor Ahmed Hemani is a faculty member at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He holds the role of PI for the project 'New Chip Architectures for Industrial Vision' and leads research in reconfigurable computing, memristor-based systems, and hardware acceleration for AI and edge computing. His work bridges theoretical computer science with practical VLSI design and embedded systems development. He actively contributes to cross-disciplinary initiatives at Digital Futures, a joint center with Stockholm University and RISE Research Institutes of Sweden focused on digital innovation. His research emphasizes scalable FPGA/HPC architectures, low-power neuromorphic systems, and optimization techniques for custom silicon solutions. Current projects include a Lego-inspired edge AI framework and memristor-driven MIMO acceleration. Teaching responsibilities span advanced courses in SOC design, digital system verification, and embedded systems. He supervises advanced-level degree projects across computer engineering and ICT innovation specializations, emphasizing hands-on hardware-software co-design methodologies. Recent publications highlight innovations in memristor applications, FPGA-based acceleration, and reconfigurable architectures for neural networks and bioinformatics. His work addresses challenges in dark silicon utilization, energy-efficient computation, and high-performance embedded systems.

• Computer Engineering
• Embedded Systems
• Hardware Acceleration
• +5 more

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +2 more

Greta Lindwall is a Lecturer at the Royal Institute of Technology (KTH), specializing in materials science and additive manufacturing. She teaches courses such as Metallic Materials, Powder Metallurgy, and Materials in Design and Product Development. Her roles include examiner, course coordinator, and teacher for several advanced programs. Her research focuses on solidification processes in additive manufacturing, particularly using high-energy X-ray techniques and synchrotron imaging to study material behavior during electron beam and laser powder bed fusion. She has contributed to understanding microstructural evolution in steels, grain refinement mechanisms, and process optimization for materials like tool steels and stainless steel. Her work integrates experimental methods with computational modeling, including CALPHAD-based approaches for phase equilibria and microstructural prediction. Key research areas include real-time tracking of solidification, smoke mechanisms in electron beam processes, and the development of novel alloys for AM applications. Lindwall collaborates with initiatives like the LIGHTer Academy and has published extensively on topics such as phase transformations, material properties under AM conditions, and process monitoring technologies. Her contributions bridge fundamental materials science with advanced manufacturing techniques, aiming to improve material performance and process reliability.

• Materials Science
• Metallurgy
• Additive Manufacturing
• +3 more

Andrea Fossati is a DDLS Fellow and Principal Investigator at Karolinska Institutet, leading the Fossati Lab at SciLifeLab. His research focuses on bacterial defense systems and phage biology, utilizing interaction proteomics and machine learning to develop novel strategies against antibiotic-resistant bacteria by disabling bacterial immunity during phage-bacterial warfare. Dr. Fossati's work centers on discovering bacterial defense mechanisms and phage counter-defense systems through advanced proteomic techniques like DIP-MS and quantitative interaction mapping. His lab integrates systems biology and machine learning to analyze host-pathogen interactions, with emphasis on jumbo phage infection mechanisms and lipid compartment formation. This research directly targets the enhancement of phage therapy efficacy by tilting the evolutionary balance toward phages. His publication trends reveal deep specialization in virology and microbiology, with dominant themes in bacterial immunity, phage-bacteria interactions, and proteomic methodology development. Over 80% of recent work involves jumbo phages and defense system characterization, while computational frameworks like PCprophet demonstrate cross-disciplinary integration of machine learning. Scientific awards: DDLS Fellow Dr. Fossati actively mentors the next generation of scientists, currently supervising two PhD students and collaborating with three postdoctoral researchers. His DDLS fellowship provides substantial research funding enabling cutting-edge instrumentation for proteomic analysis and high-throughput screening of phage-bacterial interactions, with recent grants focusing on quantitative interaction mapping and defense system discovery. The Fossati Lab operates within SciLifeLab's national infrastructure, maintaining close collaborations with leading proteomics and virology groups across Europe. The team specializes in next-generation interaction proteomics, developing novel methodologies for complex deconvolution while maintaining strong computational biology capabilities for data analysis and machine learning applications in host-pathogen systems.

• Bacterial Defense Systems
• Phage Biology
• Virology
• +5 more

Pär Olsson is a Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on computational and experimental studies of radiation damage in nuclear materials, with particular emphasis on understanding defect dynamics, microstructural evolution under irradiation, and the behavior of advanced nuclear fuels. He leads courses such as Multiscale Modeling of Nuclear Materials and Radiation Damage Physics, emphasizing both theoretical and practical aspects of materials science in nuclear contexts. His research interests include the development of advanced accident-tolerant fuels (e.g., UN-UO2 composites), the computational modeling of radiation effects in metals like Fe and W, and the interplay between solute atoms and defects in irradiated materials. His work combines first-principles calculations, kinetic Monte Carlo simulations, and experimental validation to address challenges in nuclear energy systems. Notable contributions include studies on fission product solubility in nuclear fuels, oxygen’s role in tungsten defect evolution, and the thermal performance of UN-based composites. His team’s projects, such as the M4F initiative (Multiscale Modelling for Fusion and Fission Materials), aim to bridge atomistic and continuum-scale models for material design. Pär Olsson collaborates extensively with international institutions and contributes to nuclear materials education through course coordination and supervision of graduate studies in physics and materials science.

• Nuclear Materials
• Radiation Damage Physics
• Computational Materials Science
• +4 more

Professor Stefano Romeo leads the Human Translational Genetics group at Karolinska Institutet's Department of Medicine, Huddinge, where his research bridges genetics, metabolism, and clinical medicine to address metabolic diseases. His work focuses on uncovering genetic and molecular mechanisms underlying liver diseases, diabetes, and cardiovascular conditions through innovative translational approaches. Professor Romeo's research has significantly advanced our understanding of metabolic dysfunction-associated steatotic liver disease (MASLD), identifying key genetic variants including PNPLA3 and MBOAT7 genes. He pioneered the development of multilineage 3D in vitro models for fatty liver disease and discovered a protective genetic variant in the PSD3 gene. His landmark achievement is the identification of two distinct MASLD types with different cardiometabolic risk profiles using compartmentalized polygenic risk scores, which has major implications for targeted treatment approaches. In cardiovascular research, Professor Romeo has developed machine learning algorithms for diagnosing familial hypercholesterolemia and elucidated the role of lipoprotein(a) as an independent cardiovascular risk factor. His integrated approach combines genomics, bioinformatics, molecular biology, and clinical investigations to translate discoveries into practical applications. Research Focus Areas: Genetic basis of metabolic liver diseases Cardiometabolic risk stratification 3D disease modeling and therapeutic testing Polygenic risk scoring for precision medicine Molecular pathways in lipid metabolism Professor Romeo's work has resulted in numerous high-impact publications in journals including Nature Medicine and Journal of Hepatology, demonstrating his leadership in translating genetic insights into improved disease prediction, prevention, and therapeutic outcomes for patients with metabolic disorders.

• Genetics
• Metabolism
• Liver Diseases
• +5 more

Seyedsaeed Razavikia is a Ph.D. candidate at the KTH Royal Institute of Technology , affiliated with the School of Electrical Engineering and Computer Science and the Division of Network and Systems Engineering . His work is supervised by Carlo Fischione , with co-supervision by Mairton Barros , and funded by the WASP project . B.Sc. and M.Sc. in Electrical Engineering from Iran University of Science and Technology and Sharif University of Technology , respectively. Visiting researcher at Imperial College London with Deniz Gündüz in the Information Processing and Communications Lab . Research interests span machine learning over networks , optimization , statistical signal processing , and communication theory . His work focuses on over-the-air computation , federated learning , and low-rank matrix recovery . Publications highlight advancements in digital communication , networked machine learning , and signal processing , with applications in massive connectivity and blind demixing . Awards : IEEE Sweden VT-COM-IT Best Student Journal Paper Award 2024 IEEE Sweden VT-COM-IT Top-5 Student Conference Paper Award 2024 Hans Werthén Foundation Visiting Abroad Scholarship Wallenberg AI, Autonomous Systems and Software Program Ph.D. Admission Collaborations include institutions like Imperial College London and Ericsson Research , with contributions to patents on digital channel computation .

• Optimization
• Statistical Signal Processing
• Communication Systems
• +3 more

Yiannis Karayiannidis is a Senior Researcher (equivalent to Associate Professor/Research) with the Division of Systems and Control (SYSCON), Department of Electrical Engineering at Chalmers University of Technology. He maintains a significant affiliation with the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology, demonstrating his cross-institutional impact in the Swedish robotics community. Dr. Karayiannidis earned his Diploma in Engineering in 2004, followed by a Ph.D. in Engineering in 2009, and achieved Docent status in 2017. His academic journey has focused on robotics and control systems, establishing him as a leading researcher in these fields. His primary research interests span robot control, robotic manipulation in human-centered environments, dual arm manipulation, force control, robotic assembly, control of physical human-robot interaction, multi-agent robotic systems, adaptive control and nonlinear control systems. Dr. Karayiannidis has made significant contributions to the understanding of deformable object manipulation, contact-rich robotic tasks, and human-robot collaboration. His work bridges theoretical control systems with practical robotic applications, particularly in scenarios requiring precise physical interaction. Analysis of his recent publications reveals a strong focus on advanced manipulation techniques, particularly for deformable linear objects, and human-robot collaborative tasks. His research increasingly incorporates machine learning approaches, especially reinforcement learning, to address complex manipulation challenges. There is also a clear emphasis on practical applications in industrial settings, with several projects related to robotic assembly and cable routing. Dr. Karayiannidis serves as Associate Editor for the IEEE Robotics and Automation Letters, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), and the European Control Conference. He is also the treasurer of the IEEE Robotics Chapter in Sweden and a WASP-affiliated researcher. He has served as Principal Investigator for multiple research projects including DARMA and DARMA_bridge (funded by WASP), CHROMA (funded by VR), and the H2020 SARAFun project. His current projects include "Learning & Understanding Human-Centered Robotic Manipulation Strategies" (2020-2025), "Computer Vision and Machine Learning for Robot Systems" (2019-2021), and "ViMCoR" (2019-2021) in collaboration with Volvo Group. Dr. Karayiannidis is actively involved in the robotics research community through his editorial roles and project leadership. His work connects theoretical control systems with practical robotic applications, particularly in industrial and human-robot collaborative settings.

• robot control
• robotic manipulation
• dual arm manipulation
• +7 more

Jens B Nielsen is a Professor of Quantitative Systems Biology at Chalmers University of Technology , Sweden, where he leads a research group of over 60 people. He previously served as full Professor and founded the Center for Microbial Biotechnology at Danish Technical University (DTU) , and as Chief Science Officer of the Novo Nordisk Foundation Center for Biosustainability (2013-2018). Current CEO of BioInnovation Institute (Denmark) since 2019 Member of National Academy of Engineering (USA), National Academy of Science (USA), and multiple European academies His research spans systems biology , metabolic engineering , and industrial biotechnology , with over 700 publications and 50 patents. Key innovations include CRISPR-based yeast engineering , enzyme-constrained metabolic models , and microbiome-host interaction frameworks . He has founded several biotech companies and supervised 100+ PhD students. Recent publications highlight work on: Antimicrobial resistance dynamics in microbiomes CRISPRi/a for yeast tolerance mechanisms Metabolism-driven enzyme evolution over millennia Precision modeling of gut microbiota Scientific Awards : Nature Mentor Award for academic leadership ENI Award for energy innovation Novozymes Prize for biotech impact Gold Medal from Royal Swedish Academy of Engineering Sciences He has received continuous recognition as a Highly Cited Researcher (2015-2018) and serves on editorial boards of leading journals like Nature Communications and Trends in Biotechnology .

• Systems Biology
• Biochemical Engineering
• Metabolic Engineering
• +9 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more