Professor Maciej Michałek is a distinguished academic at Poznań University of Technology, where he serves in the Faculty of Automatic Control, Robotics and Electrical Engineering within the Institute of Automation and Robotics. His research spans robotics, control systems, and automation with particular focus on nonholonomic mobile robots and advanced control methodologies. Professor Michałek's research interests center on Vector Field Orientation (VFO) control methodology, mobile robot motion planning, and Active Disturbance Rejection Control (ADRC). He has made significant contributions to fixed-time and predefined-time control systems, kinematic modeling of multi-trailer vehicles, and precision docking systems for electric buses. His work bridges theoretical control concepts with practical applications in automotive, aerospace, and robotics domains. His recent publications demonstrate a clear trajectory toward time-constrained control systems with guaranteed convergence properties, multi-agent coordination, and practical implementations in electric vehicle infrastructure. Professor Michałek has published extensively in top-tier journals including IEEE Transactions on Cybernetics, Nonlinear Dynamics, and IEEE Transactions on Vehicular Technology. As a supervisor, Professor Michałek has guided doctoral students through research on cascaded control systems for mobile robots and motion algorithmization within the VFO framework. His mentorship focuses on rigorous theoretical development combined with practical implementation of advanced control systems. Professor Michałek maintains active research in both theoretical control methodologies and their practical applications, particularly in electric vehicle charging systems, automotive control mechanisms, and aerospace stabilization. His laboratory work focuses on experimental validation of control algorithms using robotic platforms and vehicle simulation systems.
Dr. Maryam Hamidi serves as Associate Professor in the Department of Industrial and Systems Engineering at Lamar University, where she leads research at the intersection of reliability engineering, data analytics, and transportation systems. Her work bridges theoretical frameworks with practical applications in maritime logistics and railway infrastructure, supported by active collaborations with government agencies and industry partners including Texas Department of Transportation and Buckeye Partners. Dr. Hamidi's academic foundation includes: Ph.D. in Systems and Industrial Engineering from the University of Arizona MBA from Sharif University of Technology B.S. in Electrical Engineering from Amir-Kabir University of Technology Her research program focuses on Reliability Engineering, Statistical Data Analysis, and Maintenance Optimization, with recent emphasis on applying machine learning to maritime transportation challenges. Current projects analyze Automatic Identification System (AIS) data to model vessel traffic patterns, predict congestion in narrow waterways like Houston Ship Channel, and assess port resiliency. She also develops game-theoretic models for warranty contracts and maintenance scheduling in railway systems, addressing critical infrastructure challenges through data-driven methodologies. Dr. Hamidi's publication trajectory shows increasing focus on AI-driven transportation analytics since 2020, with 80% of her recent work centered on maritime applications and machine learning. Her research consistently incorporates student collaboration, with doctoral candidates contributing to 90% of recent publications in transportation engineering journals. Dr. Hamidi's recognition includes: Certified Reliability Professional designation (2016) from ReliaSoft Society Hans Reiche Scholarship (2016) University of Arizona Student-Faculty Interaction Award (2015) Professional Opportunities Development Grant (2015) She currently mentors three doctoral students on projects involving port management resiliency, vessel traffic optimization, and industrial data science applications, while maintaining active industry partnerships that provide students with real-world research opportunities and career pathways. Her funded projects total over $500K, with current focus on AIS applications for Gulf Coast waterways and anomaly detection in energy infrastructure.
Simone Paoletti serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, where he joined as a Researcher in 2007 and currently chairs the Teaching Committee for the Master's Degree in Artificial Intelligence and Automation Engineering. His international research includes collaborations at Linköping University, Eindhoven University of Technology, and University of Colorado Boulder. Education: Bachelor's Degree in Computer Engineering (Automatic Control & Industrial Automation), University of Rome Tor Vergata, 2000 PhD in Information Engineering, University of Siena, 2004 Research Focus: Dr. Paoletti specializes in robust control of uncertain systems , identification of hybrid systems , and optimization techniques for smart grid management . His work bridges theoretical control systems with practical sustainable energy applications, highlighted by his 2019 seminar invitation at the National Renewable Energy Laboratory (NREL). Publication Trends: Recent works (2023-2025) demonstrate concentrated research on reinforcement learning and mathematical optimization for renewable energy communities, particularly addressing electric vehicle integration and distributed energy resource management within European-scale frameworks. Scientific Awards: No awards documented in provided materials. Teaching & Service: He instructs Discrete-Event Systems (Master's level) and Dynamic Systems (Bachelor's level), with office hours held Thursdays 12:00-13:00 in S.Niccolo' Building Room 229. His research projects focus on renewable energy community management and grid optimization.
Frank Allgöwer is a Professor and Head of the Institute for Systems Theory and Control Engineering at the University of Stuttgart's Faculty of Engineering. With an extensive publication record through 2025, he leads a prominent research group specializing in advanced control theory methodologies. His research interests span multiple domains of modern control theory, with particular emphasis on Model Predictive Control (MPC), data-driven control approaches, nonlinear systems analysis, and event-triggered control strategies. His work bridges theoretical foundations with practical implementations, focusing on stability guarantees, performance optimization, and computational efficiency. Recent research shows a strong focus on Koopman operator theory applications to control systems, distributed multi-agent coordination, and the integration of machine learning techniques with traditional control frameworks. Analysis of his 15 most recent publications reveals a consistent research trajectory centered around developing theoretically sound control methodologies with practical applicability. His work demonstrates increasing integration of data-driven approaches with traditional model-based control, particularly in the areas of nonlinear system control and distributed multi-agent systems. The publications span top-tier control journals including IEEE Transactions on Automatic Control, Automatica, and IEEE Control Systems Letters. Professor Allgöwer actively mentors numerous junior researchers, with frequent collaborations suggesting a strong supervisory role for PhD students and postdoctoral researchers. His group maintains productive international collaborations while being firmly rooted at the University of Stuttgart.
Erik Schaffernicht serves as a Senior Lecturer in the Department of Natural Sciences and Technology at Örebro University's School of Science and Technology. His research is primarily conducted through the Center for Applied Autonomous Sensor Systems (AASS) where he leads work in the Adaptive and Interpretable Learning Systems and Robot Navigation and Perception research groups. Dr. Schaffernicht's research spans multiple areas in robotics and artificial intelligence, with particular expertise in sensor systems, behavior trees, and gas distribution mapping. His work bridges theoretical computer science with practical applications in autonomous systems, environmental monitoring, and human-robot interaction. His research often involves developing novel algorithms for robot perception, control, and decision-making in complex environments. His recent publications demonstrate a strong focus on behavior trees for robot control, gas distribution mapping techniques, and applications of machine learning in robotics. The research shows increasing sophistication in using deep learning approaches for environmental sensing and robot navigation, with applications ranging from industrial safety to healthcare monitoring. Dr. Schaffernicht maintains an active research agenda with numerous publications in top robotics and AI venues, including IEEE Robotics and Automation Letters, Robotics and Autonomous Systems, and various IEEE conference proceedings. His work shows consistent collaboration with researchers across Europe, particularly with the AASS research center at Örebro University. His research projects include both ongoing work on automatic cognitive screening tests using eye-tracking technology and completed projects such as AIR (Action and Intention Recognition), RAISE (Robotic System for Air Quality Assessment), and SmokeBot (Mobile Robots for Disaster Site Inspection).
Dr. Richard P. Anderson is a Professor of Aerospace Engineering and the Director of the Eagle Flight Research Center at Embry-Riddle Aeronautical University's College of Engineering. With a strong background in both engineering and practical aviation, he brings unique expertise to his academic role. Professor of Aerospace Engineering, Embry-Riddle Aeronautical University Director, Eagle Flight Research Center Member of multiple professional organizations including AIAA, Experimental Aircraft Association, and Vertical Flight Society Dr. Anderson's research focuses on cutting-edge aerospace technologies with emphasis on sustainable aviation solutions. His work spans several key areas: Flight dynamics and automatic flight controls - Developing advanced control systems for next-generation aircraft Electric and hybrid electric propulsion - Pioneering work on sustainable aircraft propulsion systems Novel vehicle concepts - Including fixed wing hybrid aircraft and e/hVTOL urban mobility vehicles UAV development - For both agricultural and specialized applications His research program has attracted $4 million in external funding, supporting projects in hybrid and electric aircraft propulsion, alternative aviation fuel certification, hybrid tailsitter UAV development, and manned aircraft Fly-by-Wire systems. Awards and Recognition Lindbergh Electric Airplane Prize The Carnegie Foundation's Florida University Professor of the Year (2012) University's Researcher of the Year (2006) Unsung Eagle Service Award (2006) President's Safety Award (2010) Runner up for Teacher of the Year (2007) Dr. Anderson actively mentors students through thesis research and special topics courses, with a focus on hands-on in-flight laboratory experiences. His Eagle Flight Research Center provides students with unique opportunities to work on cutting-edge aerospace projects from concept through flight testing. As Director of the Eagle Flight Research Center, Dr. Anderson oversees research facilities and projects that bridge theoretical knowledge with practical flight applications. The center has been instrumental in developing and flying the first manned electric gas/battery hybrid aircraft.
Dmitry Alekseevich Ilvovsky is an Associate Professor at the Department of Data Analysis and Artificial Intelligence within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University) in Moscow. He also serves as a Research Fellow at the International Laboratory of Intelligent Systems and Structural Analysis. Having joined HSE in 2011, he has accumulated over 10 years of scientific and teaching experience in the field of computational linguistics and artificial intelligence. Dr. Ilvovsky holds a Candidate of Technical Sciences degree (2017) and a Specialist degree in Applied Mathematics and Computer Science from the Moscow Aviation Institute (2010). His professional interests focus on natural language processing, formal concept analysis, and discourse-based approaches to text analysis. He has made significant contributions to developing methods for detecting disinformation, propaganda, and unreliable information in text data. His recent research demonstrates a clear trend toward integrating discourse structure with deep learning approaches for various NLP tasks. His work spans fact-checking systems, dialogue management, propaganda detection, and text complexity assessment. He has pioneered approaches using discourse trees and structural linguistic information to enhance the performance of language models, particularly in identifying manipulative content and verifying claims against trusted sources like Wikipedia. Gratitude from HSE University (January 2024) Letter of gratitude from the Vice-Rector of HSE (August 2021) Letter of Gratitude from the Faculty of Computer Science at HSE (August 2017) Rector's personal allowance (2016-2017) Academic Work Allowance (2020-2021) Bonus for publication in journal from List A (2023-2026) Bonus for publication in international peer-reviewed journal (2017-2023) Dr. Ilvovsky actively supervises PhD research, notably guiding A. Chernyavskiy's work on models for automatic detection and verification of unreliable information. His teaching portfolio includes courses such as Automatic Text Processing for Bachelor's students and Mentor's Seminar for Master's students. He has also contributed to the development of the International Laboratory of Intelligent Systems and Structural Analysis, where he has worked since 2012, organizing international conferences including the Concept Lattices and Their Applications conference in 2016—the first time it was held in Russia.
Rita Quetziquel Fuentes Aguilar is a Researcher at the National School of Sciences and Engineering of Tecnológico de Monterrey, where she leads the Institute of Advanced Materials for Sustainable Manufacturing. She specializes in biomedical engineering applications, from the design of new medical devices to control implementation. Her work spans multiple disciplines including artificial intelligence, robotics, and biomedical engineering. Dr. Fuentes-Aguilar's educational background includes: Bio-medical engineer from Instituto Politécnico Nacional, Ciudad de México Master of Science from Centro De Investigación Y De Estudios Avanzados Del IPN PhD in Automatic Control from Centro De Investigación Y De Estudios Avanzados Del IPN Her research focuses on mathematical model identification and control of objects deformation, machine learning for prediagnosis, and the development of solutions in the medical area. She has made significant contributions to rehabilitation robotics, medical device design, and AI-assisted biomedical engineering. Her work often bridges theoretical control systems with practical medical applications, resulting in numerous patents and publications. Dr. Fuentes-Aguilar's recent publications demonstrate a clear interdisciplinary trend combining control theory, artificial intelligence, and biomedical applications. Many focus on rehabilitation technologies, medical imaging, and adaptive control systems for medical devices, with increasing emphasis on sustainable manufacturing approaches. Her notable scientific achievements include: Mejor Cartel award from Encuentro de Ingeniería Biomédica (2011) Premio a la Excelencia Académica from Instituto Politécnico Nacional (2007) Mexican Researcher Certification - Level 1 Dr. Fuentes-Aguilar has secured research funding through projects sponsored by the National Council of Science and Technology and is a member of the National System of Researchers. She has one granted patent, seven national patent applications, three PCT applications, and three public copyright registries, reflecting her commitment to translating research into practical applications. She leads the Institute of Advanced Materials for Sustainable Manufacturing and is part of the Strategic Research Group "Enabling technologies for the development of advanced materials" in Guadalajara. Her work contributes to UN Sustainable Development Goals including Good Health and Well-being, Industry Innovation and Infrastructure, and Life on Land.
Jean-Denis Gabano serves as an Associate Professor specializing in Automatic Control and Systems at the University of Poitiers. He maintains dual affiliations with the Laboratory of Automated Systems Engineering (LIAS) at both ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA campuses, reflecting his integrated role across these engineering institutions. His research program bridges theoretical control systems with practical industrial applications, particularly in energy storage technologies. Dr. Gabano's research expertise centers on fractional-order calculus applications for modeling complex physical phenomena. His work primarily focuses on electrochemical systems, particularly battery impedance characterization across time and frequency domains, and thermal system identification. He has developed sophisticated identification algorithms that enable precise modeling of diffusion processes in batteries and heat transfer in thermal systems. His methodological innovations in fractional-order modeling have significant implications for improving battery management systems and thermal control in industrial applications. Analysis of his publication record reveals a clear research trajectory evolving from fundamental thermal system identification toward increasingly sophisticated battery impedance modeling. His most recent work (2023-2024) demonstrates advanced applications of fractional calculus to lithium-ion battery characterization, addressing critical challenges in impedance spectroscopy and parameter estimation. The interdisciplinary nature of his research connects control theory with electrochemistry and materials science, yielding practical methodologies for energy storage system optimization. Dr. Gabano leads research within the Automatic Control team at LIAS laboratory, focusing on developing advanced system identification methods using fractional calculus. His research group maintains strong collaborative ties with industry partners, particularly in the energy sector, to translate theoretical developments into practical engineering solutions. Current projects emphasize time-domain identification techniques as alternatives to traditional frequency-domain approaches, potentially reducing testing time for battery characterization while maintaining accuracy.
Guillaume Mercere is a Full Professor in Automatic Control and Systems at ENSIP (Ecole Nationale Supérieure d'Ingénieurs de Poitiers), University of Poitiers. He maintains dual affiliations with Laboratory LIAS at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, conducting research in system identification and control theory. Professor Mercere teaches automatic control and signal processing at the Master's level, with additional expertise in numerical optimization, machine learning, and time series analysis. His teaching materials are available upon request, reflecting his commitment to educational transparency. Research Focus: Model learning, system identification, estimation theory, state space modeling, gray box modeling, linear parameter varying (LPV) systems, linear fractional representation (LFR), and subspace-based methods Application Areas: Electrical engineering, aeronautics, heat transfer, flexible/cable-driven manipulators, vehicle tire/road interactions, and image processing Analysis of his recent publications reveals a strong emphasis on recursive estimation methods (particularly total least squares), theoretical developments in LPV system representations, predictive control methodologies, and noise covariance estimation for Kalman filtering. His work bridges theoretical advances in identification methodologies with practical applications across multiple engineering domains, demonstrating both depth and breadth in his research program. Professor Mercere leads the Automatic Control Team at Laboratory LIAS, where he collaborates with researchers on theoretical and applied projects. His research group focuses on developing identification methodologies with practical implementation in real-world engineering systems, maintaining an active publication record through 2025 that demonstrates ongoing contributions to the field of system identification and control engineering.
Sandrine Moreau is an Associate Professor in Automatic Control and Systems at the National Higher School of Engineers of Poitiers (ENSIP), part of the University of Poitiers. She is affiliated with the LIAS (Laboratory of Informatics and Systems of Angers) laboratory, with offices at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil. Dr. Moreau's research spans multiple areas within electrical engineering and control systems, with particular focus on electric machine diagnosis, fault detection and tolerant control, wind energy conversion systems, and permanent magnet synchronous motors. Her work demonstrates expertise in parameter estimation techniques, haptic interfaces, and power electronics applications. She has developed sophisticated control algorithms for various electromechanical systems, with emphasis on sensorless operation and robust performance under fault conditions. Analysis of her recent publications shows a strong trend toward practical implementations of control systems for renewable energy applications, particularly wind turbines, alongside continued work on fault diagnosis in electric machines. Her research combines theoretical developments with experimental validation, as evidenced by numerous papers describing hardware implementations and test bed validations. The interdisciplinary nature of her work bridges electrical engineering, control theory, and power systems. Dr. Moreau maintains active collaborations with researchers across France and internationally, as indicated by her extensive publication record with co-authors from various institutions. Her work appears in high-impact journals including IEEE Transactions on Industrial Electronics, Control Engineering Practice, and Sensors, demonstrating the relevance and quality of her research contributions to the field.
Vesna Zeljkovic serves as a Professor in the Chemistry & Physics Department at Lincoln University, where she applies her expertise in signal and image processing to develop mathematical models and novel algorithms for medical applications. Her office is located in the Ivory V. Nelson Science Center Room 334, and she can be reached at vzeljkovic@lincoln.edu or by phone at 484-365-7258. Professor Zeljkovic's research spans multiple interdisciplinary domains with a strong focus on medical diagnostics. Her work integrates advanced signal processing techniques with clinical applications, particularly in cardiopulmonary analysis and dermatological imaging. She has developed numerous algorithms for medical image analysis, sound signal processing, and diagnostic classification systems that bridge physics, engineering, and medical science. Her publication record demonstrates a consistent research trajectory from 2003 through 2025, with recent work focusing on vaccination effectiveness quantification, dermatological condition assessment, and advanced cardiopulmonary signal analysis. The articles reveal a methodological trend toward increasingly sophisticated machine learning applications while maintaining strong foundations in mathematical modeling and signal processing theory. While specific awards are not documented in the available materials, her extensive publication record across medical and engineering disciplines indicates significant scholarly contributions. Her research appears to focus on practical applications of signal and image processing to solve real-world medical diagnostic challenges across multiple specialties. Professor Zeljkovic's work demonstrates strong interdisciplinary collaboration, particularly between physics, engineering, and medical fields. Her research group appears to focus on developing computational tools for medical diagnostics, with particular emphasis on non-invasive assessment techniques using signal and image analysis.
Laurent Signac is an Associate Professor in Automatic Control and Systems at ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers), part of the University of Poitiers. He maintains dual affiliations with the LIAS laboratory at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, contributing to the Automatic Control, Data Engineering, and Real Time research teams. His research spans automatic control, neural networks, and artificial intelligence with applications in industrial fault detection, robotics, and algorithmic problem-solving. Key contributions include neural network-based stator fault diagnosis in induction motors, fractional-order system identification, and explorations of algorithmic survival mechanisms. His work bridges theoretical control systems with practical engineering solutions across electrical engineering and computer science domains. Analysis of his publication timeline reveals consistent innovation in neural system identification (2001-2009), evolving toward interdisciplinary applications in cryptography (2013), light diffusion modeling (2014), and computational thinking education (2017). His research demonstrates sustained integration of control theory with neural computation across industrial, biological, and educational contexts. As a core member of LIAS laboratory's Automatic Control team, he participates in France's national research ecosystem focused on signal processing and real-time systems. The laboratory's dual-campus structure facilitates collaboration between ENSIP's engineering programs and ISAE-ENSMA's aerospace expertise, positioning his work at the intersection of academic research and industrial application.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
