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.
Cristian Rojas is a Professor of Automatic Control at KTH Royal Institute of Technology, specializing in system identification. His research bridges control theory, statistics, and machine learning to develop data-driven methods for analyzing and controlling dynamical systems. He holds an MS in Electronics Engineering from Universidad Técnica Federico Santa María (Chile) and a PhD in Electrical Engineering from the University of Newcastle (Australia). Research focuses on efficient utilization of data for self-learning systems, including topics like continuous-time system identification, robust control, and statistical estimation. Notable contributions include work on subspace identification, input design for sparse systems, and algorithms for H-infinity norm estimation. His methodologies emphasize practical applications in industrial automation, smart infrastructure, and autonomous systems. Recent publications highlight advancements in inverse filtering, decentralized learning systems, and the theoretical underpinnings of data-driven control. He collaborates widely on projects involving Bayesian methods, adversarial systems, and privacy-protected decision-making frameworks. Rojas' work often addresses challenges such as undersampling effects, model consistency, and computational efficiency in real-world control scenarios. His academic contributions include organizing academic ceremonies at KTH and mentoring researchers in the Department of Automatic Control. Current research explores intersections between machine learning interpretability and control theory, with applications to explainable AI in engineering systems.
Muhammad Umar B Niazi is a Marie-Curie Postdoctoral Fellow at both KTH Royal Institute of Technology and Massachusetts Institute of Technology , working under the guidance of Karl H. Johansson and Munther Dahleh respectively. Holding a Ph.D. in Automatic Control Engineering from Université Grenoble Alpes, Niazi's research spans secure monitoring of cyber-physical systems and dynamic incentive design for sociotechnical systems . Education : Ph.D. (Grenoble INP, 2021); M.Sc. & B.Sc. (Bilkent University, COMSATS) Research Directions : Secure estimation against cyberattacks in transportation networks and epidemic models Physics-informed learning for observer design in nonlinear systems Eco-driving incentives using Stackelberg game theory Aggregated monitoring of large-scale systems Scientific Contributions : 2025 publications on distributed observers and reachability analysis 2024 work on incentive mechanisms and sensor fault detection 2023 developments in physics-informed epidemic control 2022-2019 foundational work on network observability and opinion dynamics Awards : Marie-Curie Postdoctoral Fellowship (2022-2025) Best Student Paper Finalist, European Control Conference 2019 Niazi's interdisciplinary work combines control theory , game theory , and machine learning to address resilience in transportation, epidemic monitoring, and social network dynamics. His methods integrate theoretical rigor with practical implementations through tools like SUMO simulations and physics-informed neural networks.
Changfu Zou is an Associate Professor in Control Engineering at Chalmers University of Technology, affiliated with the Automatic Control Research Unit. He specializes in modeling and automatic control of energy storage systems, particularly lithium-ion batteries, with significant industry collaborations including Volvo Cars, Volvo Trucks, Polestar, Scania, and CEVT AB. PhD in Automation and Control Engineering from the University of Melbourne Visiting Researcher at University of California, Berkeley (2015–2016) His research integrates machine learning with electrochemical models to enhance battery performance in electric vehicles and energy systems. Key projects include predictive control for fast charging, digital twin development, and grid-integrated EV charging optimization. Recent publications focus on multi-phase battery modeling , thermal management , and health-aware charging algorithms . He supervises projects funded by the European Commission, Swedish Research Council, and Knut and Alice Wallenberg Foundation. Awards IEEE Vehicular Technology Society Best Vehicular Electronics Paper Award IEEE Transactions on Transportation Electrification Prize Paper Award IEEE VTS Climate Challenge Award Nordic Energy Challenge Award As Associate Editor for journals like IEEE Transactions on Vehicular Technology and iScience , he contributes to editorial oversight in transportation and energy systems.
Xiaolei Bian is a Researcher in the Automatic Control unit at Chalmers University of Technology, specializing in advanced battery management systems. He joined Chalmers in 2022 after completing his PhD at KTH Royal Institute of Technology, bringing expertise at the intersection of control engineering and electrochemistry. Dr. Bian earned his PhD in chemical engineering from KTH Royal Institute of Technology, Sweden, in 2021. His educational background combines deep knowledge of electrochemical processes with engineering principles, positioning him uniquely to address complex challenges in battery technology. His primary research focuses on advanced battery management systems, with particular emphasis on smart sensor integration, fast charging technologies, battery aging mechanisms, and electrochemical modeling. Dr. Bian's work addresses critical challenges in electric vehicle technology and energy storage systems, aiming to improve battery performance, longevity, and safety through innovative monitoring and control strategies. His research bridges theoretical modeling with practical implementation, developing methods that can operate effectively with limited data in real-world applications. Analysis of Dr. Bian's publication record reveals a strong trend toward developing practical battery state estimation techniques that work with partial or random charging data. His work increasingly integrates machine learning with traditional electrochemical models, creating hybrid approaches that balance accuracy with computational efficiency. A significant portion of his research targets real-time implementation constraints, making his methods particularly valuable for automotive applications where computational resources are limited. Serves on the advisory panel for the journal Batteries Organized the 2024 IEEE International Conference on Prognostics and Health Management (ICPHM) in Stockholm Contributed to the 49th Annual Conference of the IEEE Industrial Electronics Society in Singapore Helped organize the inaugural IET Energy Storage Conference in Glasgow Active reviewer for over 15 top-tier journals and multiple international conferences As a project co-PI or PI, Dr. Bian has secured significant research funding from the Swedish Energy Agency, the European Commission, and Chalmers Areas of Advance. His current projects focus on monitoring microscale inhomogeneity in lithium-ion batteries, developing physics-informed learning approaches for predictive maintenance of e-powertrains, and creating faster charging solutions with extended battery lifetime through advanced degradation monitoring using low-cost sensors. These projects involve collaboration with researchers across multiple disciplines including materials science, electrical engineering, and industrial automation. Dr. Bian works within Chalmers' Automatic Control research environment, collaborating closely with researchers in Materials Physics and other departments. His research group focuses on translating theoretical advances in battery state estimation into practical implementations for electric vehicles and energy storage applications, with strong industry connections that ensure relevance to real-world challenges.
Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.
Zoran Sjanic is an Adjunct Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY), working in the field of Automatic Control. His research primarily focuses on sensor fusion, visual-inertial navigation, and robotics perception systems. His research interests include: Visual-Inertial SLAM and Odometry Dense Optical Flow using Deep Learning Multi-sensor Image-based Navigation State Estimation and Filtering Robotic Perception and Autonomous Navigation Collaborative Environment Mapping The recent publications indicate a strong trend in integrating deep learning with classical estimation frameworks for improved robustness in navigation systems, particularly in GPS-denied environments. His work bridges computer vision, control theory, and robotics. Scientific contributions include advancements in optical flow evaluation, sliding window estimation, and collaborative qualitative mapping. Notable collaborations include researchers such as Gustaf Hendeby, Martin Skoglund, and Patrick Doherty. He has not listed any formal advisees or awards in the provided material. No information about grants or advising activities is available. There is no mention of lab or research team leadership in the current text.
