Professor Dariusz Horla is a faculty member at the Faculty of Automation, Robotics and Electrical Engineering at Poznań University of Technology, where he works in the Institute of Robotics and Machine Intelligence. He holds the title of Professor with a habilitation degree (dr hab. inż.) in automation and control engineering. His research focuses on advanced control systems, particularly anti-windup compensation techniques, model predictive control applications for nuclear power plants, UAV control systems, and fractional-order controllers. Professor Horla has published extensively across these domains, with recent work addressing challenges in cooperative control systems, optimization methods for control applications, and practical implementations of advanced control algorithms in real-world systems. Analysis of his recent publications (2015-2025) reveals a strong emphasis on practical control applications, particularly in nuclear power plant systems and unmanned aerial vehicles. His work bridges theoretical control methods with industrial implementation challenges, often focusing on optimization approaches and stability considerations in constrained systems. Professor Horla has supervised multiple doctoral students and has developed significant educational materials in control systems and automation. His intellectual property contributions include inventions related to UAV height control systems, demonstrating the practical impact of his research. His academic service includes participation in habilitation committees and development of educational materials that have shaped control engineering education at Poznań University of Technology.
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. Keshav Sood is a Senior Lecturer at Deakin University's School of Information Technology, where he leads research in cybersecurity, AI, and next-generation networks. His affiliations include roles as Graduate Research Coordinator and South Asia Country Coordinator. He completed his PhD at Deakin University and a post-doctoral fellowship at the University of Newcastle. Research Interests: Dr. Sood focuses on securing distributed systems, with emphasis on: Federated learning for intrusion detection in IoT/5G networks Biometric privacy in immersive technologies (VR/AR) RF fingerprinting for IoT device authentication Quantum-resistant software-defined networks Adversarial robustness in voice authentication systems His publications consistently explore AI-driven security frameworks, with recent work addressing data sparsity in IoT sensors, cross-domain IIoT authentication, and phishing mitigation using large language models. Awards: Professor of IT Award (2016) IEEE TNSE Excellent Reviewer (2023) Deakin HDR Supervision Award (2023) Course Team Award for Industry Certification Alignment (2021) Supervision & Grants: Dr. Sood currently advises 8 graduate researchers and has secured $655,313 in competitive funding. Key projects include: Smart Farming Cyber Resilience (DFAT Maitri Grant) Secure Access for Critical Infrastructure (Cyber CRC) IoT Data Integrity for Defense Systems (Australian Defence) He leads the Deakin Cyber Research and Innovation Centre, focusing on scalable security solutions for industry partners.
Dr. Ahmad Farooqi serves as Assistant Professor at Central Michigan University College of Medicine, where he provides statistical leadership through the Clinical Research Institute (CRI). He delivers weekly statistics instruction to medical trainees at Children's Hospital of Michigan and offers comprehensive analytical support for clinical research projects across the institution. His academic credentials include: Ph.D. in Biostatistics from Wayne State University M.S. and M.A. in Statistics from University of Windsor M.Phil in Statistics from Government College University, Lahore Dr. Farooqi's research pioneers non-parametric, robust, and exact statistical methodologies applied to pediatric clinical challenges. His work addresses critical gaps in small-sample clinical studies through innovative approaches to longitudinal data analysis, survival modeling, and diagnostic accuracy assessment. As a SAS-certified expert, he implements advanced techniques across SAS, R, and SPSS environments to solve complex analytical problems in cardiology, emergency medicine, and immunology research. His publication record demonstrates consistent high-impact contributions across pediatric specialties, with particular emphasis on cardiac outcomes, emergency department operations, and immunological responses. The research portfolio reveals strong methodological rigor combined with practical clinical applications, often addressing resource-constrained scenarios requiring robust analytical solutions. Key recognitions include: Best Article Award in Pediatric Neurology (2019) Best Paper Award at ICCS-15 statistical conference Wayne State University Full Tuition Scholarship Dr. Farooqi actively mentors medical researchers through statistical consulting and teaching, with collaborations spanning multiple departments and international institutions. His role in the CRI facilitates cross-disciplinary research partnerships while advancing methodological standards in clinical investigation. Current work focuses on refining statistical approaches for pediatric cardiac interventions and emergency care protocols through ongoing clinical data analysis projects.
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.
Eric Monteiro is a Lecturer at École Nationale Supérieure d'Arts et Métiers (ENSAM) and Academic Director of the Master 1 Factory of the Future program. He is affiliated with the Laboratory for Processes and Engineering in Mechanics and Materials (PIMM), specifically within the DISCO research team focusing on Laser Processes. His research spans several key areas in mechanical engineering and materials science: Structural Health Monitoring : Developing advanced Lamb wave-based techniques for damage detection using signal processing and machine learning approaches Topology Optimization : Creating constrained natural element methods for stress and fatigue-constrained design optimization Polymer Processing : Modeling viscohyperelastic behavior in manufacturing processes like stretch blow molding Computational Mechanics : Developing novel numerical methods including hybrid twin approaches and meshfree techniques Laser Processes : Researching applications in materials processing and manufacturing His recent publications demonstrate a strong focus on computational methods development, with applications spanning structural optimization, wave propagation analysis, manufacturing process simulation, and material behavior modeling. This work consistently combines theoretical frameworks with experimental validation. As Academic Director, he oversees the Master 1 Factory of the Future program while maintaining an active research portfolio through PIMM laboratory, which provides access to experimental platforms for materials characterization and advanced manufacturing processes.
Fabrice Lamarche serves as an Associate Professor at Université de Rennes 1 within the ESIR School of Engineering, while maintaining dual affiliation with the MimeTIC research team at IRISA / INRIA Rennes. His academic career spans uninterrupted service since 2004, evolving from Assistant Professor roles at IFSIC (2003-2009) to current positions at ESIR. As co-founder of Golaem (2009), he bridges academic research with commercial application in crowd simulation technology. His institutional journey includes sequential membership in SIAMES (2004-2006), Bunraku (2007-2011), and MimeTIC (2011-present) research teams at INRIA. Lamarche earned his PhD in Computer Sciences from Université de Rennes 1 in 2003 with thesis work on virtual human autonomy. His educational foundation includes a Master of Computer Sciences specializing in Computer Graphics and AI (1999-2000), a Master of Engineering from INSA de Rennes (1997-2000), and a Technical degree from IUT de Limoges (1995-1997). His research program centers on virtual human behavior modeling with emphasis on decision-making systems, path planning under environmental constraints, and crowd simulation architectures. Key innovations include TopoPlan for human-scale navigation and frameworks integrating high-level task scheduling with low-level motion planning. This work addresses fundamental challenges in creating autonomous virtual characters capable of navigating complex 3D environments while exhibiting realistic behaviors, with applications spanning virtual reality, gaming, and simulation-based training systems. Publication analysis reveals consistent output from 2001-2014, evolving from foundational behavioral animation (2001-2004) to sophisticated crowd simulation systems (2013-2014). A notable trajectory shows increasing integration of cognitive modeling with motion planning, alongside exploration of Brain-Computer Interfaces for virtual navigation. Recent work demonstrates particular strength in semantic decomposition of urban environments and time-space constrained task scheduling. His scientific contributions have earned significant recognition: Rennes city medal (2009) for research excellence Second prize at Deutsch Telekom Awards (FMX 2008) for TopoPlan/MKM integration As an active researcher and educator, Lamarche advises students through Université de Rennes 1 while leveraging INRIA resources and Golaem industry partnerships. His publication record indicates sustained grant funding, particularly through INRIA channels, with collaborative projects extending to neuroscience applications via Brain-Computer Interface research. The MimeTIC team affiliation provides infrastructure for multimodal interaction research in complex virtual environments. Lamarche's laboratory work through MimeTIC focuses on developing practical implementations of virtual human autonomy systems. His research group maintains strong industry connections via Golaem, which commercializes crowd simulation technology. Current efforts emphasize semantic understanding of virtual urban spaces and robust path planning under dynamic constraints, building on foundational work in topological navigation and behavioral decision systems.
Daniel W. C. HO is a Chair Professor of Applied Mathematics and Associate Dean (Undergraduate Education) at the College of Science, City University of Hong Kong. He has been with City University of Hong Kong since 1989, having previously served as a Research Fellow at the University of Strathclyde, Glasgow, UK from 1985 to 1988. Prof. Ho received first class honours in BSc, MSc, and PhD degrees in mathematics from the University of Salford, Greater Manchester, UK in 1980, 1982, and 1986, respectively. His academic journey began with foundational work in control theory and has evolved into a distinguished career spanning over three decades. Prof. Ho's research interests span multiple domains in control theory and systems engineering. His primary focus areas include Control Theory , Estimation and filtering theory , Complex dynamical distributed networks , Multi-agent networks , Nonlinear singular systems , and Stochastic systems . His work bridges theoretical advances with practical applications, particularly in networked control systems, cybersecurity for cyber-physical systems, and distributed optimization. Prof. Ho has made significant contributions to the understanding of synchronization phenomena in complex networks, resilient control under cyber attacks, and quantized control systems with communication constraints. His research has evolved from classical control theory to address contemporary challenges in networked and distributed systems, reflecting the changing landscape of control engineering. Prof. Ho's publication record shows a strong emphasis on secure control systems under cyber attacks, distributed optimization with communication constraints, event-triggered control schemes, quantized control systems, and synchronization of complex networks. His work demonstrates a consistent progression from theoretical foundations to addressing practical implementation challenges in cyber-physical systems, with increasing focus on security aspects in recent years. Prof. Ho has received numerous prestigious awards and honors throughout his career. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2017 and elevated to IEEE Life Fellow status in 2024. He was awarded the Chang Jiang Chair Professorship by the Ministry of Education, China in 2012. Prof. Ho has been recognized as a Highly Cited Researcher for eleven consecutive years from 2014 to 2024, and is among the Top 2% of most highly cited scientists globally from 2020 to 2024. He received the Best Paper Award from The 8th Asian Control Conference in 2011 and the Teaching Excellence Award from City University of Hong Kong in 2020 for his innovative teaching approaches. Prof. Ho has held significant editorial responsibilities, serving as Subject Editor of the Journal of Franklin Institute, Co-Editor in Chief of Franklin Open, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Asian Journal of Control, and Action Editor of Neural Networks. He has also served on the editorial boards of several other prestigious journals, contributing to the advancement of his field through scholarly communication. His leadership extends beyond research and teaching as Associate Dean (Undergraduate Education) of the College of Science at City University of Hong Kong, where he plays a key role in shaping the educational experience for science students.
Jorge Isaac Chairez-Oria is an Associate Professor in the Department of Biomedical Engineering at Tecnológico de Monterrey's School of Engineering, Campus Guadalajara. He maintains an active research program bridging biomedical engineering, neural networks, and robotic control systems with significant contributions to rehabilitation technology and sustainable manufacturing. His research focuses on neural network applications for system identification and control , particularly in constrained dynamical systems. His work spans medical rehabilitation robotics (including exoskeletons and orthotic devices), bioprinting and tissue engineering , and sustainable environmental technologies utilizing neural network approaches. Recent publications demonstrate strong interdisciplinary work connecting engineering principles with biological and medical applications. Analysis of his 2024-2025 publications reveals consistent focus on barrier Lyapunov functions, constrained state systems, and neural network identification methods applied across diverse domains from wastewater treatment to surgical robotics. His work shows particular strength in translating theoretical control concepts into practical biomedical applications. Scientific Recognition: Mexican Researcher Certification - Level 2 Dr. Chairez-Oria teaches courses in Biofluid Mechanics, Clinical Engineering, and Bioinstrumentation Systems design, integrating his research expertise into the classroom. His work aligns with multiple UN Sustainable Development Goals including Good Health and Well-being, Industry Innovation, and Clean Water and Sanitation.
Dr. Hunmin Kim serves as Assistant Professor in the Department of Electrical and Computer Engineering at Mercer University's School of Engineering, specializing in security and operational resilience of cyber-physical systems with applications in autonomous vehicles, UAVs, and smart grids. His educational background includes: PhD in Electrical Engineering from Pennsylvania State University (2018) BSE in Mechanical Engineering from Pusan National University (2012) Dr. Kim's research centers on developing attack/fault detection mechanisms, robust control frameworks, and path planning algorithms for cyber-physical systems operating in dynamic environments. His work addresses critical security vulnerabilities and coordination challenges in multi-agent systems, with emphasis on verifiable safety guarantees for autonomous operations. Analysis of his 2023-2025 publications reveals strong thematic convergence in resilient control under adversarial conditions, motion planning in complex environments, and assistive technology applications. His work consistently bridges theoretical control systems with practical implementations in autonomous vehicles and UAVs, featuring increasing integration of machine learning techniques for adaptive security. Professional recognition includes: Nomination for 2024 Clayton R. Paul Teaching Excellence Award Dr. Kim actively mentors undergraduate researchers through Mercer's Bear Day events and honors projects, guiding student teams in developing gesture-based drone controls, EMG signal processors, and assistive navigation devices for visually impaired individuals. He maintains extensive peer review commitments across 45+ journals/conferences annually including IEEE Transactions on Automatic Control and Automatica. He contributes to the Collaborative Center for Computing at Mercer University and serves on the IEEE CSS Technology Conferences Editorial Review Board.
Zuzana Nevěřílová serves as an Assistant Professor at Masaryk University's Department of Czech Language within the Faculty of Arts. She also maintains external cooperation with the Department of Machine Learning and Data Processing at the Faculty of Informatics, demonstrating interdisciplinary engagement between linguistic theory and computational approaches. Her research interests span computational linguistics with particular emphasis on Czech language processing, natural language processing methodologies, and development of language resources. Her work bridges theoretical linguistics with practical computational applications, focusing on areas such as named entity recognition, text analysis, word embeddings, and language resource development. Dr. Nevěřílová's recent publication trends (2021-2025) reveal strong focus on Czech language technologies, including named entity processing in parallel corpora, efficient language models, and educational applications of NLP. Her work demonstrates consistent contributions to Slavonic language processing communities through workshops like RASLAN and conferences like TSD. Her scientific contributions include extensive publications in proceedings of international conferences and workshops, software development for language resources, and educational materials for digital humanities. She has supervised various projects related to language technology and has contributed to significant resources like the New Encyclopedic Dictionary of Czech and VerbaLex lexical database. Her research has practical applications in language services, educational technology, and digital humanities. Dr. Nevěřílová works within the Institute of the Czech Language at Masaryk University, contributing to research teams focused on Czech language processing, corpus development, and computational lexicography.
