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.
Ola Carlson is a Professor in Sustainable Electric Power Production at Chalmers University of Technology. He specializes in electrical systems for renewable power production and hybrid electric vehicles. Since 2022, he serves as a senior advisor to the Swedish Wind Centre, focusing on island operation with Chalmers wind turbine and battery systems. Research Interests His research spans renewable power systems, wind energy integration, grid stability, and microgrid optimization. Key projects include modeling Nordic transmission systems, analyzing wind turbine bearing currents, and developing maintenance schedules for aging components. Article Trends Recent publications emphasize wind turbine design, microgrid stochastic optimization, and dynamic state estimation for transmission protection. Topics cover machine learning applications in forecasting, fault handling, and battery degradation impacts on energy systems. Projects & Collaborations RESIST - Energy islanding for resilient systems (2026–2027) COSPACT - Nordic-Baltic co-simulation platform (2020–2023) Fossil Free Energy Districts (2016–2019) Collaborations with ABB, Swedish Energy Agency, and European Commission Labs & Teams Works with Power Grids and Components at Chalmers, leading projects like 'Detecting and eliminating bearing currents' (2018–2023) funded by the Swedish Energy Agency. Involved in Chalmers Campus as a testbed for intelligent grids.
Nikolaos Kolomvakis is a researcher in the Division of Communication Systems at KTH Royal Institute of Technology in Sweden. He is also a visiting researcher at Ericsson AB in Stockholm. Previously, from 2017 to 2023, he held positions as Systems Engineer and Senior Researcher at Ericsson. His research focuses on wireless communications and signal processing, particularly on developing baseband physical-layer algorithms for distributed/cell-free massive MIMO, holographic MIMO, and large intelligent surfaces. Education: Ph.D. in wireless communications from Chalmers University of Technology , supervised by Prof. Mats Viberg with co-supervision from Prof. Thomas Eriksson and Prof. Michail Matthaiou M.Sc. in information technology & electrical engineering from ETH Zurich (2012) Research Interests: Wireless communications Signal processing Distributed/cell-free massive MIMO Holographic MIMO Large intelligent surfaces Publications: Recent work includes analyzing nonlinear distortion in large arrays and active reconfigurable intelligent surfaces (2025) Exploring spatial frequencies in near-field communications (2025) Investigating 6G performance through gigantic MIMO (2025)
Tobias Oechtering is a Professor at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. His research focuses on information theory, privacy-preserving technologies, statistical signal processing, machine learning, and smart grid systems. He has held academic positions at KTH since 2008, advancing from Post-Doctoral Researcher to Assistant Professor (2010–2013), Associate Professor (2013–2018), and Professor (2018-present). He has supervised over 20 PhD students and contributed to numerous postdoctoral programs. Research Interests: - Network information theory and physical-layer security - Privacy mechanisms with provable guarantees - Distributed statistical inference and sensor calibration - Reinforcement learning and privacy-aware machine learning - Smart grid privacy and energy management - Wireless communication algorithms and signal processing - Networked control systems and stability analysis He currently supervises 7 PhD students and hosts 3 postdocs. His work has led to over 150 peer-reviewed publications, with recent contributions in privacy-preserving smart grid strategies, adversarial inference control, and information-theoretic security. He has served as editor for IEEE Transactions on Information Forensics and Security and held leadership roles in KTH's Digitalisation Research Platform.
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.
Robert Muscarella is a Senior Lecturer at the Department of Ecology and Genetics, Plant Ecology and Evolution, Uppsala University. His work bridges plant ecology, climate change impacts, and tropical forest dynamics. Research interests include Climate change effects on forest structure Functional trait analysis Species distribution modeling Post-disturbance forest recovery Ecohydrology of tropical systems Remote sensing applications in ecology Recent publications highlight trends in tree mortality , hurricane impacts , drought responses , and functional trait variability across tropical ecosystems. Methodological contributions include open-access species distribution modeling tools.
Björn Hagströmer is Professor of Finance and Director of Studies for the Postgraduate Programme at Stockholm Business School , Stockholm University. He earned his PhD from Aston Business School in 2010 and has been with SBS since then. His primary teaching responsibility is the course Financial Market Structure . Research Focus : Hagströmer specializes in financial market microstructure , analyzing price formation, liquidity supply, and trading mechanisms. His work examines closing call auctions, high-frequency trading dynamics, bid-ask spread components, and information revelation in decentralized markets. Current projects include studies on market fragmentation , gold futures microstructure , and equity market information dissemination Recent publications explore volatility extensions in auctions, unbiased spread estimation, and network-based information flow analysis Awards : Recipient of the 2017 De la Vega Prize for research on effective spread overestimation. His work appears in leading journals including Journal of Financial Economics and Journal of Finance . Academic Leadership : Co-organizer of the Microstructure Exchange online seminar series and the 2017 Conference on the Econometrics of Financial Markets celebrating the 20th anniversary of Campbell-Lo-MacKinlay's influential work.
Markus Jäntti serves as Professor at Stockholm University's Institute for Social Research (SOFI), where his research centers on income and wealth distribution, poverty dynamics, and socio-economic mobility through comparative international lenses. He specializes in quantifying family background's influence on economic outcomes and leads pivotal projects including MapIneq (life-course inequality trends), PrecaNord (Nordic precarious work analysis), and TITA (austerity-era inequality assessment). His research portfolio spans income distribution , wealth inequality , intergenerational mobility , and comparative social policy , employing advanced econometric techniques and cross-national datasets like the Luxembourg Income Study. Work within SOFI's Labor Market Economics (AME) group extends to education, health, taxation, and gender equality, reflecting labor economics' interdisciplinary nature. Analysis of his 2020-2025 publications reveals dual emphases: methodological innovation (e.g., grouped-data inequality measurement) and empirical exploration of family-background effects, labor policies, and social transfers. His influential 2020 framework synthesizing four mobility approaches underscores conceptual rigor, while recent Nordic-focused studies address contemporary challenges like migrant labor exploitation. Scientific awards: No awards were documented in source materials. Major grant-funded projects include: MapIneq : Tracking intergenerational, educational, labor market, and health inequality drivers across lifespans PrecaNord : Multi-level analysis of precarious/informal work in Finland, Norway, and Sweden TITA : Consortium study of austerity's impacts on financial, health, and opportunity inequalities As core faculty in SOFI's AME group, he contributes to research spanning labor market outcomes (wages, employment), social transfers, crime, and political economy—operating within Stockholm University's broader social policy ecosystem while maintaining independent research leadership.
Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.
Joakim Jaldén is a Professor at the Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He holds a Ph.D. in Electrical Engineering from KTH (2007) and completed post-doctoral studies at Vienna University of Technology (2007-2009). With affiliations at Stanford University and ETH Zürich, his academic journey reflects global expertise. 2002: M.Sc. in Electrical Engineering, KTH 2007: Ph.D. in Electrical Engineering, KTH 2007-2009: Post-Doctoral Researcher, Vienna University of Technology Jaldén's research spans Signal Processing , Wireless Communications , and Biomedical Data Analysis . He pioneered MIMO communications and later developed ELISpot/FluoroSpot analysis algorithms commercialized by Mabtech AB. His work on cell migration tracking (IEEE ISBI 2012) and distributed optimization (ECO-PANDA method) demonstrates interdisciplinary impact. Key publication trends include Hidden Markov Models for DNA sequencing, Reinforcement Learning in communication systems, and Low-Complexity Beamforming for MU-MIMO networks. His 2024 work on mmWave MIMO beam coherence showcases continued leadership in wireless channel modeling. Scientific recognition includes: IEEE Signal Processing Society 2006 Young Author Best Paper Award Ingvar Carlsson Career Award 2009 (Swedish Foundation for Strategic Research) IEEE ISBI 2012 Best Paper Award Bitplane Awards (2013-2015) for cell tracking challenges As Program Director of KTH's 5-year Electrical Engineering Degree Program (CELTE) since 2016 and Vice-Chair of EECS Faculty Board , Jaldén leads academic initiatives. His collaborations with industry (e.g., Mabtech AB) and roles as examiner for advanced courses in communication systems highlight his educational impact.
Emil Björnson is a Professor of Wireless Communications and Head of the Communication Systems Department at KTH Royal Institute of Technology since 2024. He received his Master of Science in Engineering Mathematics from Lund University (2007) and PhD in Telecommunications from KTH (2011). After postdoctoral work at SUPELEC, France (2012-2014), he held faculty positions at Linköping University (2014-2021) before returning to KTH in 2020. Research Focus: MIMO communications, reconfigurable intelligent surfaces, radio resource allocation, machine learning for communications, and energy efficiency Editorial Roles: Editor for multiple IEEE transactions and magazines His research has significantly advanced wireless communication technologies, particularly in Massive MIMO and cell-free systems. He has authored four textbooks, including Massive MIMO Networks (2017) and Introduction to Multiple Antenna Communications and Reconfigurable Surfaces (2024). Scientific awards include: IEEE Fellow Clarivate Highly Cited Researcher Wallenberg Academy Fellow Digital Futures Fellow Multiple IEEE and EURASIP awards (2014-2024)
Peiyuan Chen is an Associate Professor at the Department of Electric Power Engineering, Chalmers University of Technology. He holds a B.Eng. from Zhejiang University (2004), an M.Sc. from Chalmers (2006), and a Ph.D. from Aalborg University (2010). His research focuses on power system operation and planning with wind power integration, emphasizing time series modeling, statistical analysis, and optimization. He contributes to projects on grid-forming converters, inertia estimation, frequency control, and renewable energy system stability. Research Interests: • Power Systems and Renewable Integration • Grid-Forming Converters and Stability Analysis • Time Series Modeling and Statistical Methods • Machine Learning for Energy Applications • Frequency Control and Synthetic Inertia Recent Publication Trends include studies on deep learning for heating load classification, wind turbine type optimization, fault ride-through capabilities, and inertia estimation in converter-dominated grids. His work bridges theoretical power system analysis with practical implementations in Nordic and European energy networks. Projects (2017-2024) include grants from the Swedish Energy Agency, Swedish Research Council (VR), and collaborations with institutions in Sweden, China, and Italy. Key areas: grid strength metrics, multiport converter applications, and citizen energy communities.
Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.
Giovanni Forchini is a Professor at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, Sweden. His research focuses on econometrics, panel data analysis, and their applications in health economics and epidemiological modeling. He holds the title of Docent, a Swedish academic qualification reflecting advanced expertise. His work bridges theoretical econometrics with practical policy analysis, particularly in pandemic preparedness and healthcare optimization. Research Themes: Econometric methodologies for panel data and structural equation models Quantifying pandemic impacts on healthcare systems and economies Optimization of resource allocation during public health crises Key Contributions: Developed the DAEDALUS model for integrated economic-epidemiological policy simulations Analyzed SARS-CoV-2 transmission dynamics and vaccine impact in multiple countries Pioneered statistical methods for handling multifactor structures in panel data Awards & Grants: USBSE Pedagogical Prize 2020 Funding from Forte (Swedish Research Council for Health, Working Life and Welfare) and Handelsbanken Teaching & Mentorship: Coordinates Master’s theses in Economics at USBSE Teaches advanced courses like Econometrics 1 & 2 and Analysis of Financial Data
Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.
