Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.
Per Krusell is Professor of Economics at Stockholm University and holds the Torsten and Ragnar Söderberg Chair at the Institute for International Economic Studies (IIES). He is a Fellow of the Econometric Society and served as President of the European Economic Association in 2020. He is currently the Secretary of the Prize Committee for the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. Education: PhD in Economics, University of Minnesota Research Interests: Per Krusell’s research spans macroeconomics , inequality , climate economics , labor markets , and political economy . He has made foundational contributions to understanding incomplete markets, wealth distribution, optimal taxation, and the macroeconomic effects of climate change. His work often integrates dynamic stochastic general equilibrium (DSGE) models with heterogeneous agents to study long-run outcomes and policy impacts. Publications Trend: His recent publications (2022–2025) reflect a strong focus on climate policy , integrated assessment modeling , and macro-labor dynamics . Key themes include carbon taxation, labor supply responses to productivity growth, fiscal multipliers in heterogeneous-agent settings, and the macroeconomic consequences of vaccination and epidemic control. Awards & Honors: Fellow of the Econometric Society Torsten and Ragnar Söderberg Chair in Economics President, European Economic Association (2020) Secretary, Nobel Prize Committee in Economic Sciences Advising & Grants: While specific student names are not listed, his leadership roles in global economic associations and policy committees indicate extensive academic advising and grant involvement in macroeconomic policy and climate economics. Labs & Teams: He is affiliated with the Institute for International Economic Studies (IIES) at Stockholm University, a leading research institute in macroeconomics and international economics.
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
Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.
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
Carlos Errando Herranz serves as an Assistant Professor in the Quantum and Computer Engineering Division at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) and as a Principal Investigator at QuTech. His research focuses on developing scalable quantum photonic integrated circuits using semiconductor fabrication processes compatible with existing infrastructure for quantum internet applications. He received Bachelor's and Master's degrees from Universitat Politècnica de València (2013) and a PhD in Micro and Nanosystems from KTH Royal Institute of Technology (2018), followed by postdoctoral positions at KTH and MIT as a Marie Curie fellow. His lab investigates quantum photonics, integrated photonics, and color centers with emphasis on diamond tin-vacancy systems and silicon-based quantum emitters. Recent publications demonstrate strong expertise in tuning quantum emitters via strain engineering, heterogeneous integration of spin-photon interfaces, and MEMS-enabled reconfigurable photonics. Key advancements include cavity-enhanced quantum memories, superconducting detector integration, and spectral control of solid-state emitters for quantum networks. Dr. Herranz advises seven graduate students including PhD candidates Vicky Dominguez Tubio, Arjan Mejas, Matteo Pirro, Christian Primavera, Jan Riegelmeyer, and Elena Volkova, along with Master student Bram Zijlstra. His team comprises postdocs Lin Jin and Pat Laferriere, and interns Elsa Herranz Valiente and Ernest Staffetti Cruañas. The Errando Herranz Lab operates within QuTech's Quantum Internet Division at Delft University, maintaining specialized facilities for nanofabrication and optical characterization of quantum photonic devices. Current research directions include developing CMOS-compatible quantum memories operating at telecom wavelengths and scalable architectures for quantum repeaters.
Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Yang Liu is a tenured Associate Professor at the Department of Management and Engineering , Linköping University, Sweden, and an Adjunct Professor at the University of Oulu, Finland. His expertise spans smart manufacturing, clean energy transition, and Industry 4.0 applications. He holds an M.Sc. and D.Sc. from the University of Vaasa, Finland. Research & Awards: Liu's work focuses on sustainable systems, decision support systems, and AI-driven energy efficiency. He has authored over 140 Web of Science publications, including top 0.1% ESI Hot Papers. He is ranked among the world's top 2% scientists (Stanford-Elsevier) and leads globally in 'big data analytics in manufacturing' and 'Industry 4.0-driven circular economy' research. Leadership & Projects: He leads projects like FlexSUS (EU Horizon 2020) and PERSEUS, developing tools for smart urban energy planning and 15-minute city models. He serves as Editor-in-Chief of Cleaner Engineering and Technology and Guest Editor for multiple journals. His research emphasizes bridging data science with sustainability challenges in manufacturing and energy systems. Key Achievements: Top-ranked in global citations, ESI Highly Cited Papers, and industry-driven sustainability frameworks. Grants: Leads EU-funded projects and collaborates with Siemens Energy on energy transition solutions. Labs & Teams: Part of the Environmental Technology and Management (MILJÖ) division and Unit for Product Service Innovation (MILJOPSI) at Linköping.
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.
Magnus Dahlstedt is Professor of Social Work at Linköping University's Department of Culture and Society (IKOS) since 2019, previously holding the same position at ISV (Institution for Social and Welfare Studies) from 2015-2019. He has been responsible for the research environment and doctoral education in Social Work at Linköping University from 2016 to 2023. His academic journey began with a PhD in Ethnicity from Linköping University in 2005, followed by becoming a docent (associate professor) in 2009 and a biträdande professor (associate professor) in 2014 before achieving full professorship in 2015. Dahlstedt's research centers on citizenship formation in contemporary society shaped by welfare policy transformations and international migration. His work examines how citizenship is concretely formed in segregated urban environments, focusing particularly on the gap between formal rights and substantive living conditions. He investigates social pedagogical efforts across public policy, education, and the non-profit sector, with special attention to vulnerable groups including young people with foreign backgrounds in urban peripheries. His methodological approach is interdisciplinary, grounded in critical power analysis, discourse analysis, and ideological critique, often involving cross-disciplinary research collaborations. His recent publications reveal consistent thematic focus on migration, social inclusion, and urban segregation, with particular emphasis on how sports can serve as tools for social purposes in marginalized communities. The research shows increasing attention to longitudinal studies of migrants' trajectories and the role of different educational contexts (high school, folk high schools, study associations) in integration processes. Dahlstedt's work demonstrates strong policy relevance, with findings frequently presented in government agency reports and informing public debate on segregation and inclusion. Researcher at Center for Community Strategies Research (2018-2021) Member of Mimerrådet, National Program for Folk Education Research (since 2014) Department Head at Remeso, Linköping University (2012-2015) Dahlstedt actively contributes to public discourse through media appearances, public lectures, and alternative writing projects including the anthology 'Brandtal' (2023) and 'Hårdare kan ingen vara' (2023), while preparing 'En professors dagbok' for publication in fall 2024. His research is funded by multiple agencies including Vetenskapsrådet, Norrköping Research and Development Fund, and the Swedish Crime Prevention Council.
Lars Engstrand is a Professor in infection control at Karolinska Institutet, heading the Translational Microbiome Research and Pandemic Preparedness group within the Department of Microbiology, Tumor and Cell Biology. His research spans multiple disciplines with a focus on translational microbiome research and pandemic preparedness. Engstrand's research interests center on understanding the human microbiome's role in health and disease, particularly in women's reproductive health and gastroenterology. His group has established multiple large-scale clinical studies including SweMaMi (Swedish Maternal Microbiome project), BASIC (associations between microbiota and preterm birth), VaMiGyn (Vaginal Microbiota in Gynaecological health), and several others investigating the vaginal microbiome's relationship to pregnancy outcomes, HPV infection, and cervical cancer. His team also conducts significant research on gut microbiome in inflammatory bowel disease and colorectal conditions through projects like PopCol and KOLBIBAKT. Analysis of Engstrand's most recent publications reveals a strong focus on the relationship between microbiome composition and women's health outcomes. His research consistently examines how vaginal, gut, and oral microbiomes influence pregnancy complications, preterm birth, HPV infection, and mental health during pregnancy. The work often employs large cohort studies with comprehensive sampling strategies across multiple body sites and time points, providing robust data for understanding microbiome dynamics in health and disease. Engstrand leads the Microbiome Exploration and Development for Intervention (MEDI) initiative and the National Pandemic Center (NPC) at Karolinska Institutet. The NPC conducted large-scale sequencing during the COVID-19 pandemic and has amassed over 1.5 million samples. His research group includes multiple specialized teams focusing on culturomics, bioinformatics, labcore operations, women's health research, and pandemic preparedness.
Johan Eklöf is a Professor at Stockholm University within the Department of Ecology, Environment and Plant Sciences . His research emphasizes marine ecology , focusing on interactions between marine biodiversity, environmental conditions (including climate), and societal impacts . He teaches courses such as Management of Aquatic Resources in the Tropics , Marine Ecology for the Biogeo Program , and Ecology II . Research Focus: Causal relationships between biodiversity and ecosystem resilience Foundation species as drivers of ecosystem services Optimizing resource management under climate change Projects: Principal Investigator for FORCE (2023-present), a transdisciplinary Baltic Sea recovery project Contributor to NordSalt (climate impacts on Nordic salt marshes) and PlantFish (Baltic Sea vegetation-fish interactions) Scientific Leadership : Supervises 2 current PhD students and co-supervises 6 additional PhD/postdocs Formerly mentored 4 PhD graduates and >40 MSc students Environmental Context: His work spans Baltic Sea and Western Indian Ocean ecosystems, using seagrass beds , mussel beds , and coastal benthic systems as primary models. Recent publications highlight climate change impacts on trophic cascades , habitat connectivity , and policy frameworks for marine conservation .
Mattias Heldner is Professor of Phonetics and Head of Department at the Department of Linguistics, Stockholm University. He also serves as Director of the Phonetics Laboratory where he has developed advanced recording facilities for breathing movements and voice quality dynamics in conversation. Dr. Heldner received his PhD in Phonetics from Umeå University in 2002, completed a postdoc at TeliaSonera Sweden in 2005, became Docent in Speech Communication at KTH Royal Institute of Technology in 2007, and was appointed Professor in Phonetics at Stockholm University in 2011. His research focuses on communicative behavior relevant for turn-taking in conversation and signaling of prosodic functions such as prominence and boundaries. His work spans multiple dimensions of speech communication, including: Respiratory patterns and their role in conversation flow Voice quality dynamics as turn-taking cues Prosodic functions in spontaneous speech Physiological constraints in verbal communication Acoustic properties of child speech development Neural correlates of lexical stress Dr. Heldner's recent publications demonstrate how respiratory signals provide crucial information for predicting speech activity and managing turn-taking. His research combines physiological measurements with acoustic analysis to uncover hidden aspects of conversational organization, particularly the relationship between breathing patterns and speech timing. As an educator, Dr. Heldner has taught phonetics courses for Speech and Language Pathology students at Karolinska Institutet, University of Gothenburg, and Umeå University. He currently supervises one PhD student and has successfully supervised two PhD theses to completion. Dr. Heldner has held several Swedish research grants and served as one of the Technical Program Chairs for Interspeech 2017 in Stockholm. His Phonetics Laboratory has developed innovative systems like RespTrack for measuring respiratory movements during speech with high experimental control.
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.
