Yu Xia is a Post Doc at the Department of Chemistry, Stockholm University, Sweden. He is affiliated with the Tom Willhammar Research Group, focusing on advanced electron microscopy and diffraction techniques for structural characterization of materials. PhD (2019–2023) from a joint program between the University of Birmingham (UK) and the Southern University of Science and Technology (China). Research emphasizes fabrication of metallic nanoparticles with non-equilibrium structures and shapes using gas-phase condensation and thermal shock methods. Specializes in scanning transmission electron microscopy (STEM), in-situ heating experiments, and electron energy loss spectroscopy (EELS) for nanoparticle analysis. Current work prioritizes 4DSTEM imaging for electron beam-sensitive materials and Python-based post-processing of electron microscopy datasets. Yu Xia's research spans Materials Science , Nanotechnology , and Electrocatalysis , with applications in photocatalytic hydrogen evolution , graphene composites , and advanced electron microscopy techniques . His work often integrates computational image processing with structural characterization to optimize material properties. Publications highlight innovations in heterostructure engineering , metallic alloy catalysts , and electron beam-sensitive material imaging . No scientific awards are explicitly mentioned in the provided text. Yu Xia's technical expertise includes Python scripting for image analysis, in-situ electron microscopy , and multifunctional graphene-based materials .
Niklas Hedin is a Professor and Head of the Department of Chemistry at Stockholm University . His research group specializes in developing advanced materials for environmental and energy applications, with a particular focus on CO₂ capture technologies , green material synthesis , and biochar-based solutions for pollution mitigation and sustainable resource utilization. Professor at Department of Chemistry Head of Department Stockholm University affiliation The research spans from fundamental molecular spectroscopy studies to industrial-scale applications . Key projects include the use of activated limestone for Baltic Sea eutrophication control, colloidal porous liquids for energy-efficient carbon capture, and engineered biochars for dual environmental remediation and agricultural applications. Recent publications highlight 2025 breakthroughs in aminated cellulose aerogels , graphene oxide composites for direct air capture , and ultrasound-assisted hydrogen peroxide synthesis . These works demonstrate Hedin's commitment to multiscale material engineering combining experimental validation with computational modeling. The group includes several PhD students and postdoctoral researchers working on specialized aspects of material synthesis and environmental application. His team actively collaborates with industrial partners and government agencies to translate laboratory findings into real-world solutions for sustainable chemistry and climate change mitigation .
Massimo Bongiorno is an Assistant Professor in Electrical Engineering at Chalmers University of Technology. He holds a Master’s degree in Electronics Engineering from the University of Palermo (2002) and earned his Licentiate and PhD from Chalmers University. His research focuses on power electronics applications in power systems, particularly grid-forming converter systems, power quality, and renewable energy integration. MSc in Electronics Engineering (University of Palermo, 2002) Licentiate and PhD (Chalmers University of Technology) Research interests include: Power electronics in power systems Grid-forming converter stability Renewable energy integration Modular multilevel converter design Small-signal and large-signal stability analysis Energy storage system applications Recent publications highlight trends in: Converter control strategies for grid stability Dynamic modeling of power electronics systems Applications in offshore wind and hydro microgrids Impedance analysis and resonance mitigation Advanced fault ride-through techniques Multi-terminal HVDC grid control
Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.
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.
Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.
Powder Metallurgy (MH2100) and has expertise in computational materials science. His research emphasizes predictive modeling of material behavior, including precipitation kinetics, sintering processes, and coating interactions. Notable areas include phase field modeling of discontinuous precipitation, spinodal decomposition in Fe-Cr alloys, and high-entropy alloy design. His studies bridge experimental data with computational tools like the YAPFI phase-field framework. Key themes in his publications span cemented carbides, Co-based entropic alloys, and tool wear mechanisms. He combines CALPHAD thermodynamic modeling with first-principles calculations to address challenges in materials processing and corrosion resistance. His work often addresses industrial applications, such as optimizing machining tools and additive-manufactured superalloys.
Anna Gautier is an Assistant Professor in the Department of Computer Science at Chalmers University of Technology, affiliated with the Division of Data Science and AI. Previously, she was a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology (2023–2025), focusing on mechanism design for multi-robot systems. Her research emphasizes planning under uncertainty, multi-agent systems, and human-robot interaction. She holds a PhD from the University of Oxford (2023), an MSc from the London School of Economics, and dual undergraduate degrees from Washington University in St. Louis. Education Background: PhD in Computer Science, University of Oxford (2023) MSc in Applied Mathematics, London School of Economics BA in Mathematics and BS in Computer Science, Washington University in St. Louis Research Interests: Dr. Gautier explores planning algorithms for multi-agent systems, particularly in uncertain environments. She designs mechanisms to coordinate robots and humans, leveraging game theory and formal methods. Her work addresses challenges like resource allocation, risk-aware decision-making, and trust in autonomous systems. Recent projects include contingency planning for autonomous vehicles and auction-based resource distribution. Professional Activities: She co-chairs the ECAI 2025 Demonstration Track and teaches the course Safe Robot Planning and Control at KTH. Her projects include collaborations with WASP-Nest (PerCorSo) and TECoSA on trustworthy autonomy. She actively publishes in top venues like AAMAS and AAAI. Labs and Teams: Affiliated with Chalmers' Data Science and AI division, she leads research in multi-agent systems and human-AI collaboration.
Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.
Mattias Brunström serves as Assistant Professor of Cardiology and Associate Professor of Epidemiology at Umeå University's Faculty of Medicine within the Department of Public Health and Clinical Medicine, Section of Cardiology. He is concurrently a resident physician at Norrlands University Hospital and holds leadership roles as chairman of Sweden's national hypertension working group and scientific secretary of the Swedish Society for Hypertension, Stroke and Vascular Medicine, with active participation in the European and International Societies of Hypertension. His academic foundation includes a 2018 PhD thesis examining blood pressure-lowering treatment effects across different blood pressure levels through systematic reviews and meta-analyses of randomized clinical trials. This doctoral work established his expertise in evidence-based cardiovascular therapeutics and epidemiological methodology. Dr. Brunström's research program centers on cardiovascular disease risk factors, with specialized focus on hypertension pathophysiology and aortic diseases. His group investigates how adolescent blood pressure levels predict future cardiovascular events, examining interactions with obesity, physical fitness, and diabetes to improve risk stratification. They also analyze differential effects of antihypertensive drug classes on cardiovascular outcomes and study risk factors for aortic dissection/rupture to optimize preventive surgical interventions. This work addresses critical gaps in managing the world's leading cause of death, where uncontrolled hypertension contributes to 10 million annual fatalities despite effective treatments. Analysis of his 2024-2025 publications reveals dominant themes in hypertension guideline development, treatment threshold controversies, and cardiovascular risk assessment. His work frequently challenges conventional approaches (e.g., questioning excessive treatment of 'elevated' blood pressure in elderly patients) while advancing evidence for lifestyle interventions and beta-blocker utility. Methodologically, his research leverages large cohort studies (including 1.4 million enlistee data), systematic reviews, and international collaborations through societies like ESH and ISH to translate epidemiological findings into clinical practice. Dr. Brunström leads multiple funded research initiatives including 'Remission of type 2 diabetes through eHealth' (2022-2028) and 'VIPviza' (2013-2027), directing a multidisciplinary team that bridges clinical cardiology, epidemiology, and public health. His advisory role extends to national guideline committees and international hypertension societies where he shapes clinical practice through evidence synthesis and position papers. Based at Norrlands University Hospital's Cardiology Section, his research group operates within Umeå University's strong cardiovascular research ecosystem, maintaining active collaborations with the Swedish National Diabetes Register and international consortia. Their work emphasizes real-world applicability, examining topics like bedtime dosing of antihypertensives and self-report diagnostic tools to overcome barriers in hypertension control where only 25% of affected individuals achieve target blood pressure levels.
Angela Sasic Kalagasidis is a Professor and Department Head at Chalmers University of Technology , leading the Building Physics research group. She serves as a board member of the Moisture Center at Lund University of Technology , contributing to interdisciplinary research in building science. Building Physics Heat and Mass Transfer Energy Efficiency Moisture Safety Indoor VOC Emissions Climate Change Adaptation Her recent publications focus on aerogel-based materials for insulation, urban heat island mitigation , and thermal energy storage systems . Key methodologies include CFD simulations , field testing , and life cycle assessment frameworks . Research trends show emphasis on: Advanced computational tools for hygrothermal analysis Integration of phase change materials in building systems Climate resilience in building envelopes Optimization of ventilation and moisture control
Saleh Javadi is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. He is actively engaged in research and teaching within the field of systems engineering. His educational background includes: B.Sc. in Electrical-Control Engineering from Amirkabir University of Technology (2009) M.Sc. in Electrical, Electronic and Systems Engineering from The National University of Malaysia (2013) Ph.D. in Systems Engineering from Blekinge Institute of Technology (BTH) (2021) Saleh Javadi's research focuses on signal processing, machine learning, and computer vision , with applications spanning remote sensing, intelligent transportation systems, and AI-driven industrial optimization. His work bridges theoretical advancements with practical implementations, particularly in SAR imagery analysis, drone-based agricultural monitoring, and traffic surveillance systems. His recent publications demonstrate a strong focus on remote sensing technologies, particularly Synthetic Aperture Radar (SAR) image processing and analysis. There's a clear trend toward applying machine learning techniques to solve complex problems in aerial and satellite imagery, traffic monitoring, and agricultural applications. His research shows interdisciplinary connections between computer vision, signal processing, and practical engineering applications. Saleh Javadi has received significant recognition for his innovative work: Innovator of the Year award (SKAPA – Innovation Prize in Memory of Alfred Nobel) in Blekinge for innovative efforts in optimizing and reducing energy consumption in industries by using artificial intelligence ÅForsk Entrepreneur's prize at the Swedish Innovation Council Day – Swedish Incubators & Science Park's annual conference in May 2019 Dr. Javadi is involved in practical applications of his research through projects such as "Artificiell intelligens AI kan reducera ogräsfrön i utsäde" (ongoing) and "Bekämpa Renkavle med hjälp av drönare och Artificiell Intelligens (AI)" (completed). His work demonstrates a strong commitment to translating academic research into real-world solutions that address industrial and environmental challenges. His research appears to be conducted within a collaborative framework, working with colleagues on drone technology, SAR image analysis, and AI applications across multiple domains including agriculture, maritime monitoring, and transportation systems.
Carl-Mikael Zetterling is a Professor and Head of Department at Kungliga Tekniska Högskolan (KTH) in Stockholm, Sweden, affiliated with the School of Electrical Engineering and Computer Science (ICT) and the Electronics and Embedded Systems department. His research focuses on process technology and device design for high-temperature, high-power silicon carbide (SiC) electronics, expanding into SiC-based analog and integrated circuits. He has authored over 300 publications, including books on SiC process technology and plagiarism prevention. Dr. Zetterling has held leadership roles such as Vice Dean of the School of ICT (2013–2017) and teacher representative on KTH's faculty board. He has collaborated internationally at Stanford University, Kyoto University, and Kyoto Institute of Technology. His work addresses applications in extreme environments, including Venus exploration and fusion reactor monitoring, with a focus on radiation tolerance and thermal resilience. The 15 most recent publications highlight trends in wide bandgap semiconductors, gamma irradiation effects on SiC devices, and high-temperature integrated circuits. His articles span structural health monitoring with machine learning, novel SiC diode designs, and radiation-hardened electronics. Key contributions include advancements in self-aligned contacts, trench MOSFETs, and compact modeling for extreme conditions. While no formal awards are listed, his roles in technical program committees (TMS Electronic Materials Conference, IEEE SISC Conference) and editorial work demonstrate significant academic service. He teaches courses ranging from digital design to high-temperature electronics, overseeing degree projects in embedded systems, communication, and nanotechnology.
Erik Prytz is a Senior Associate Professor in Cognitive Science at the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on applying human factors principles to improve safety-critical systems, particularly in emergency response domains such as first aid, disaster medicine, and prehospital care. He holds a PhD in Human Factors Psychology and has served in roles including Director of the Forum Securitatis graduate school and Program Chair for the Cognitive Science BSc program. Education: PhD in Human Factors Psychology (Old Dominion University, 2014), MSc in Cognitive Science (LiU, 2010). Research Interests: Simulation-based training, stress and mental workload, emergency responder teamwork, and human-system interaction in crisis scenarios. His work emphasizes interdisciplinary collaboration, combining cognitive science, computer science, and medicine to enhance emergency response systems. Recent projects explore driver behavior toward emergency vehicles, ad-hoc responder group dynamics, and optimal placement of bleeding control kits in public spaces. He contributes to initiatives like the Center for Advanced Research in Emergency Response (CARER) and the Forum Securitatis graduate school. Erik’s teaching includes courses on human factors, distributed cognition, and emergency response systems. He actively participates in curriculum development and quality assurance committees within the Faculty of Arts and Sciences.
Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.
