Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm
Heather Congdon Fors is a Senior Lecturer at the Department of Economics, University of Gothenburg. She holds a Ph.D. in Economics from the University of Gothenburg (2006), an M.A. in Economics from the University of Waterloo (1999), and a B.A. (Honours) in Economics from the University of Manitoba (1997). Her research focuses on development economics, education, child labor, and political economy. She has led the research project 'Child labor and school attendance in India' (2014–2018) and serves as a reviewer for journals such as the European Journal of Political Economy and Economic Inquiry. Her teaching expertise includes macroeconomics, statistics, institutional economics, and international economics. She has completed pedagogical training programs including HPE201 to HPE103. Her work explores topics such as the impact of globalization on education, land certification on schooling in Ethiopia, and the persistence of harmful cultural norms like female genital cutting in Africa. She has published extensively in peer-reviewed journals and authored reports on child labor policies, institutional change in post-colonial states, and gender biases in education. Fors has contributed to interdisciplinary projects, including analyses of bureaucrats' use of cost-benefit analysis in environmental policy and the long-term impacts of colonial missions on cultural practices. Her research bridges theoretical frameworks with empirical studies, addressing global challenges such as sustainable development goals and educational inequalities. She is based at Vasagatan 1, Gothenburg, and can be reached at heather.fors@economics.gu.se.
Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.
Lina Bertling Tjernberg is a Professor at the Department of Electrical Engineering, KTH Royal Institute of Technology, and Deputy Head of the School of Electrical Engineering and Computer Science (EECS) with responsibility for research conditions and impact. She served as Director of KTH's Energy Platform during 2018-2024 and holds memberships in IVA (Swedish Royal Academy of Engineering Sciences) and the IEEE Power & Energy Society. Research Focus: Applying mathematics (statistics, optimization, life cycle assessment) to enhance reliability and predictive maintenance in electric power systems, with emphasis on future electricity grids integrating microgrids, battery storage, HVDC, nuclear/pumped/hydro/wind/solar power, hydrogen, and electrified transport. Collaborations: Engaged with Comillas Pontifical University (Madrid), Addis Ababa University, Norwegian University of Science and Technology (NTNU), and IEA Wind. Key Research Trends: Recent articles highlight advancements in microgrid control (2025), SMR nuclear energy integration (2025), AI-driven asset management (2024), hydrogen sector coupling (2024), and renewable forecasting techniques (2024). Awards: 2021 Power Woman of the Year 2022 Energy Power List (Sweden’s top 20 energy influencers) Leadership Roles: Swedish Electromobility Center (SEC) board Chair of Swedish Electrical Standards (SEK Svensk Elstandard) Member, IEEE PES ISGT Europe steering committee
Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.
Joakim Westerlund is a Professor at the Department of Economics at Lund University, Sweden. His research focuses on econometrics, especially panel data econometrics, with expertise in estimation theory, unit root testing, and structural breaks. He teaches econometrics at all academic levels and has supervised numerous bachelor, master, and PhD theses. His work contributes to UN Sustainable Development Goals through methodological advancements in economic analysis. Westerlund has held a Wallenberg Academy Fellowship (2019–2028) and received the Journal of Applied Econometrics Distinguished Author Award in 2018. He collaborates internationally and actively contributes to academic conferences. His research spans theoretical econometrics, empirical applications, and econometric software development. Current PhD supervision includes students working on topics like robustness to structural breaks and human capital analysis. Key research interests include panel unit root tests, interactive effects models, and methodological innovations for handling cross-sectional dependence. His recent work addresses structural breaks in panel data and the New Keynesian Phillips Curve in European economies. He has published widely in top journals such as the Journal of Applied Econometrics and the Oxford Bulletin of Economics and Statistics.
Martin Solberger is an Associate Professor in the Department of Statistics at Uppsala University, where he received his PhD in statistics in 2013 and was promoted to associate professor in 2022. His office is located at Ekonomikum (3rd floor), Kyrkogårdsgatan 10, with postal address Box 513, 751 20 UPPSALA. Dr. Solberger specializes in time series econometrics with particular expertise in macroeconomic forecasting and estimation of latent time series variables including potential GDP and the neutral interest rate. His research demonstrates sophisticated methodological approaches to dynamic factor models, unit root testing, and interest rate analysis, frequently employing Kalman filtering techniques and Bayesian VAR modeling. His publication record reveals a strong focus on Scandinavian economic analysis, particularly examining the natural rate of interest and neutral interest rate dynamics within Swedish and broader Nordic contexts. Recent work shows increasing attention to international spillover effects on domestic monetary policy variables and methodological refinements in panel data econometrics. Through extensive collaboration with researchers including Spånberg, Armelius, and Österholm, Solberger has established himself as a significant contributor to modern time series econometrics methodology and its application to central banking and fiscal policy questions.
Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.
Joakim Odqvist is a Professor and Head of Department at the Structures unit within the Royal Institute of Technology (KTH). He specializes in materials science, with a focus on phase separation in alloys, nanostructure evolution, and the mechanical behavior of advanced materials. His research integrates experimental techniques like small-angle neutron scattering and atom probe tomography with computational modeling to study materials such as stainless steels, cemented carbides, and cast irons. Odqvist teaches courses in ceramic materials, material design, and materials structures, mentoring students at both undergraduate and graduate levels. His work addresses challenges in corrosion resistance, fatigue, and additive manufacturing, contributing to the development of high-performance materials for industrial applications. Research Interests: Phase separation mechanisms, spinodal decomposition in Fe-Cr alloys, nanostructure evolution in duplex stainless steels, diffusion kinetics, and the application of statistical models to hydrogen diffusion. His studies often bridge fundamental material science with practical engineering solutions, emphasizing predictive simulations and material design. Recent Articles: His publications focus on controlling nanostructures in super duplex steels, functional gradient carbides, and statistical models for hydrogen diffusion. Key themes include optimizing heat treatments to mitigate embrittlement, understanding precipitation kinetics in additively manufactured materials, and leveraging phase field modeling to predict material behavior. These studies highlight his role in advancing materials for harsh environments, such as aerospace and energy sectors.
Sara Hägg is a Senior Lecturer at the Karolinska Institutet , affiliated with the Department of Medical Epidemiology and Biostatistics . She is also a Docent in molecular epidemiology. PhD in Computational Biology (Linköping University, 2009) MSc in Molecular Biology (Stockholm University, 2003) BSc in Computer Science (Stockholm University, 2003) Her research focuses on human biological aging , including measurement of aging markers (telomere length, epigenetic clocks, frailty index), causal pathway analysis, and identification of geroprotectors for age-related diseases. She utilizes longitudinal twin studies (SATSA, GENDER, HARMONY), UK Biobank, and Swedish cohorts with methods like Mendelian randomization and genome-wide analyses . Recent articles demonstrate trends in epidemiological aging research , with emphasis on cardiovascular aging , neurological disease interactions , metabolic profiling , and epigenetic clocks . Her work often involves multivariable modeling and cross-cohort validation . Leadership roles include Director of LifeGene Core Facility (2024-) and Founding Board Member of the Nordic Aging Society (2023-). She serves on expert groups for the Swedish Twin Registry and Strategic Research Area in Epidemiology and Biostatistics .
Jonas Faleskog is a Professor in the Department of Materials and Structural Mechanics at KTH Royal Institute of Technology. His research focuses on mathematical modeling of material deformation and failure mechanisms, particularly in metallic and polymeric materials. Key areas include ductile and brittle fracture analysis, fracture mechanics, and computational modeling of material behavior under various stress conditions. He leads a research group collaborating internationally to develop models describing material failure at microscopic scales. Faleskog teaches courses such as Fracture Mechanics (SE2139) and Modeling in FEM (SE2860), emphasizing practical applications of theoretical models. His work spans experimental and numerical methods, addressing challenges in material heterogeneity, porosity effects, and environmental degradation. Notable contributions include advancements in weakest-link modeling for brittle failure, probabilistic fracture models, and strain gradient plasticity analysis. His research bridges material science, applied mechanics, and numerical methods to optimize material utilization in engineering systems like reactor tanks, aircraft, and vehicles. Key collaborations involve international teams exploring microstructural influences on fracture behavior. While no specific awards are listed, his extensive publication record reflects sustained contributions to mechanical and materials engineering.
Anders Karlström is a Professor at KTH Royal Institute of Technology, specializing in Transport Modelling and Economics. His research focuses on sustainable transportation systems, emissions reduction, and energy efficiency. Key interests include activity-based modelling, dynamic discrete choice frameworks, and policy analysis for urban mobility. He has contributed to studies on travel behavior, infrastructure planning, and environmental impacts of transport systems across multiple international cities. His work integrates advanced methodologies such as recursive logit models, spatial regression, and machine learning for predictive analytics. Notable research areas involve evaluating weather variability effects on travel patterns, optimizing traffic state estimation with sensor data, and developing scenario-based models for future employment growth. Karlström collaborates with industries to enhance the competitiveness of sustainable transport solutions globally.
