جست‌وجوی استاد و پژوهشگر

Jeff Offuttمشاهده پروفایل

استاد

• Software Testing
• Mutation Testing
• Model-Based Testing
• +۸ مورد دیگر

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, part of the College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University, where he led the MS in Software Engineering program and developed numerous courses in software testing, web engineering, and software analysis. His research has significantly influenced both academia and industry in software testing and engineering education. His research interests span software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has pioneered techniques such as bypass testing and model-based testing, with applications embedded in tools like Selenium. His work emphasizes practical, impactful research—what he calls "science fiction"—with a clear path to real-world use by software engineers. His recent publications reflect a strong trend in cost-effective mutation testing, model-based testing strategies, educational innovations like self-paced learning models (e.g., SPARC), and security aspects of web applications. The keywords across his work include software testing, empirical studies, automation, and education, with subfields such as fault localization, test oracles, input partitioning, and scalable pedagogy. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University Faculty Member of the Year (2020) Virginia Outstanding Faculty Award (2019) George Mason Teaching Excellence Award (2013) 10-year Most Influential Paper Award, MODELS 2020 Best Paper Award, ICST 2021 Most Influential Paper, ISSRE (2019) Jeff Offutt has advised numerous students and collaborated internationally, including at Swedish universities. He has led major educational initiatives such as the NSF-funded SPARC project, aimed at improving scalability and diversity in introductory CS courses. He has also been actively involved in curriculum development, including co-authoring IEEE/ACM guidelines for software engineering programs. His leadership extends to founding and chairing major conferences like IEEE ICST and serving as Editor-in-Chief of Software Testing, Verification and Reliability . He leads research projects on smart test automation, minimal mutation, and K–5 computer science integration. His work has resulted in widely used tools including muJava, Mothra, and SpecTest. He frequently gives keynote talks on testing, education, and research methodology, and is known for his popular talk "How to Get Your Paper Rejected."

• Reactive Control Strategies
• Supervision and Prognosis
• Large Scale Systems
• +۱۱ مورد دیگر

Eric Duviella is a Professor at IMT Nord Europe, specifically within the Department of Automatic Control and Computer Sciences. He has been a permanent faculty member since 2007 and was promoted to full Professor in 2015. His academic affiliations include strong ties with the University of Lille, Universitat Politècnica de Catalunya, and the University of Seville through collaborative research and joint Ph.D. supervision. Education: Diplôme d'Ingénieur, Ecole Nationale d'Ingénieurs de Tarbes (ENIT), 2001 M.S., Institut National Polytechnique de Toulouse (INPT), 2001 Ph.D. in Industrial Systems, Institut National Polytechnique de Toulouse, 2005 HDR (Habilitation à Diriger des Recherches), University of Lille 1, 2014 His research interests center on Reactive Control Strategies , Supervision and Prognosis , and the Modeling of Large-Scale and Environmental Systems , particularly Hydrographical and Water Systems . His work aims at predictive maintenance, adaptive management under climate change, and improving navigation and water quality through advanced control techniques. The 15 most recent publications reflect a strong trend toward Model Predictive Control (MPC) , distributed and decentralized control architectures , leak detection in water networks , and resilience of inland waterways under climate stress . Keywords across these works include control engineering, water systems, fault diagnosis, and data-driven modeling, indicating a multidisciplinary approach combining automation, environmental science, and computational intelligence. Scientific Awards: Best paper award at CODIT 2017 Eric Duviella has actively supervised numerous Ph.D. and Master’s students from institutions including IMT Lille Douai, Universitat Politècnica de Catalunya, and the University of Seville. He has led significant research grants such as GEPET-Eau, CHEEF2, and CASTR-Eau, focusing on sustainable water management, hydropower optimization, and robotic monitoring. His work often involves partnerships with agencies like VNF, EDF, and local water authorities. He is involved in key research labs and teams including the Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), and collaborates internationally through mobility programs with UPC and the University of Seville. His leadership in organizing special sessions at major conferences (e.g., IFAC, HIC) underscores his role in shaping discourse in water system control and supervision.

• Control Systems
• Data-Driven Modeling
• Distributed Systems
• +۷ مورد دیگر

Dr. Lala Rajaoarisoa is an Assistant Professor at IMT Nord Europe (formerly Institut Mines-Télécom Lille Douai), where he conducts research in control systems and data-driven modeling for large-scale distributed systems. His work bridges automatic control theory with practical applications in energy efficiency and hydraulic system management. His research interests focus on developing data-driven tools for observation and control of complex systems, including the analysis of fundamental system properties such as stability, observability, identifiability, and controllability. He develops predictive models and controllers aimed at assessing system behavior and optimizing performance, particularly in building systems and hydraulic networks. With over 80 publications in refereed journals and conferences, Dr. Rajaoarisoa's scholarly output reflects sustained contributions to the fields of control engineering and system optimization. His recent work is associated with research projects such as ALPAGA and CASTREAU, indicating active engagement in cutting-edge technological challenges involving autonomous and resilient systems. Although specific awards or grants are not listed in the provided text, his involvement in multiple research initiatives suggests ongoing funding and collaborative activity. He also participates in academic events and seminars, contributing to the broader scientific community in robotics and intelligent systems. Dr. Rajaoarisoa is affiliated with research teams working on multi-robot systems and resilient cyber-physical systems, as evidenced by his connection to the Robot-IA events and projects like ALPAGA. These affiliations highlight his integration into dynamic, interdisciplinary research environments focused on future technologies.

Bo Bernhardssonمشاهده پروفایل

استاد

• Automatic Control
• Control Theory
• Modeling and Control of Uncertain Systems
• +۱۷ مورد دیگر

Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.

• Control Theory
• Real-Time Systems
• Embedded Systems
• +۷ مورد دیگر

Anton Cervin is a Senior Lecturer at the Department of Automatic Control, Faculty of Engineering, Lund University. He is currently on leave of duty but remains actively involved in research and supervision. His work spans real-time systems, event-based control, cloud computing, and embedded systems, with strong ties to industrial applications and sustainable technologies. His research interests include: Event-Based and Stochastic Control Networked and Embedded Control Systems Cloud-Based Control and Resource Management Real-Time Scheduling and Performance Analysis Digital Twins and Fluid Modeling in Cloud Environments He has led major research projects funded by the Swedish Research Council, WASP, and ELLIIT, focusing on robust and secure cloud control, self-adaptive systems, and distributed learning. His software tools—TrueTime, Jitterbug, and TinyRealTime—are widely used in academia and industry for simulating and analyzing real-time control systems. The most recent publications highlight a shift toward intelligent, adaptive control using reinforcement learning, fluid modeling for microservices, and timing-robust cloud control—indicating a strong trend in merging traditional control theory with modern computing paradigms. His scientific contributions have been recognized with multiple Best Paper Awards: Best Paper Award - ECRTS 2021 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Best Paper Award - ECRTS 2003 Best Student Paper Award - RTCSA 1999 Anton Cervin has supervised numerous PhD students, including Max Nyberg Carlsson, Nils Vreman, and Claudio Mandrioli, and has served in key academic roles such as Director of PhD Studies and Deputy Head of Department. He has also led educational initiatives like doctoral study circles on programming languages and the history of control. He is affiliated with research environments such as WASP and ELLIIT and contributes to UN Sustainable Development Goals through advancements in smart systems and sustainable automation.

David Ohlinمشاهده پروفایل

مدرس پاره‌وقت

• Optimal Control
• Control of Large-Scale Networks
• Social Networks
• +۴ مورد دیگر

David Ohlin is a PhD student and part-time lecturer in Automatic Control at Lund University's Faculty of Engineering (LTH), actively contributing to the Wallenberg AI, Autonomous Systems and Software Program (WASP) since 2021. His position bridges advanced doctoral research with instructional responsibilities across multiple control theory courses. He earned his MSc in Engineering Physics in 2021 with a thesis focused on machine learning applications for EEG signal interpretation, establishing his interdisciplinary approach at the intersection of control systems and biomedical engineering. His educational trajectory demonstrates a clear progression from foundational physics to specialized control theory expertise. Ohlin's research centers on optimal control methodologies for large-scale networked systems, with innovative applications to social network dynamics and opinion modeling. His work on graph search methods for controlling positive systems and nonlinear synchronization models represents significant contributions to both theoretical control frameworks and their real-world implementations in social dynamics. This dual focus on mathematical rigor and practical application characterizes his scholarly approach. His publication pattern reveals a growing expertise in network control systems, with recent work examining consensus formation in polarized networks, linear cost optimization, and heuristic search algorithms. These publications collectively demonstrate his ability to translate complex control theory concepts into solutions for networked social systems. Teaching since Fall 2021: Non-linear control, Learning-based control, and Network dynamics courses Research collaborations with prominent scholars including Anders Rantzer and Emma Tegling Active participant in WASP, Sweden's premier AI and autonomous systems initiative

Julie Wallمشاهده پروفایل

استاد

• Artificial Intelligence
• Advanced Computing
• Deep Learning
• +۱۲ مورد دیگر

Professor Julie Wall is a faculty member at the University of West London, serving as Professor of AI and Advanced Computing in the School of Computing and Engineering. She is actively engaged in research, teaching, and professional service, including her role as an expert at the British Standards Institution (BSI) in the domain of artificial intelligence. Research Interests: Julie Wall's research is centered on the design and application of intelligent systems for processing and modeling temporal data, particularly in speech and language. She leverages neural network architectures—ranging from biologically inspired models to computationally efficient deep learning systems—to analyze diverse data types such as audio, video, images, tabular data, and 3D features. Her work extends to developing production-grade deep learning and natural language understanding systems for immersive environments like virtual and augmented reality. Publications and Research Trends: Her body of work, comprising over 50 high-quality papers and multiple patents, reflects a strong trajectory in AI systems that integrate multimodal data with temporal dynamics. The research spans core areas of machine learning, natural language processing, and applied AI, with increasing focus on real-world deployment, efficiency, and intelligent interaction in extended reality platforms. Scientific Awards: US Patent UK Patent Advising and Grants: Julie Wall supervises research students across disciplines including forensic science and artificial intelligence. She contributes extensively to academic programs, teaching courses such as BSc and MSc in Computer Science, Artificial Intelligence, Data Science, and specialized AI programs in cybercrime and criminal justice. While specific grant details are not listed, her patent holdings and publication volume suggest sustained research funding and project leadership. Labs and Teams: As a leading researcher in AI and advanced computing, she is likely involved in or leads research groups focused on intelligent systems, deep learning, and multimodal AI at the University of West London, though specific lab names are not mentioned in the text.

• Computational Electromagnetics
• Antenna Design
• Electromagnetic Scattering
• +۳ مورد دیگر

Eliseo García García is a Professor at the University of Alcalá, affiliated with the Department of Automatic Control and Systems Engineering. He holds a Ph.D. from the University of Alcalá, awarded in 2005 for his thesis on computational electromagnetics. His primary research focuses on computational methods for electromagnetic analysis, including the development of efficient algorithms like the Characteristic Basis Function Method (CBFM) and hybrid techniques with the Multilevel Fast Multipole Algorithm (MLFMA). He is a core member of the GEC (Computational Electromagnetic Group), where he explores geophysical applications, antenna design, and high-frequency electromagnetic problems. His research interests bridge theoretical and applied electromagnetics, with particular emphasis on reducing computational costs in solving large-scale EM problems. Notable contributions include advancements in radar cross-section (RCS) computation, radome structure analysis, and geothermal exploration frameworks using hydrogeophysical methods. He has also published extensively on numerical techniques for antenna trajectory simulations and sparse matrix preconditioning. Eliseo’s work spans multiple disciplines, including aerospace engineering, environmental science, and software development, as evidenced by his contributions to Altair Feko 2023 updates. Despite no listed awards, his prolific publication record (over 70 articles from 2013–2024) underscores his active role in advancing computational electromagnetics and its interdisciplinary applications. His advising and grants section remains unreported, but his involvement in collaborative projects is implied through frequent co-authorships and institutional affiliations. He leads the GEC lab, focusing on cutting-edge computational tools and their practical deployment in real-world engineering challenges.

Omar AL-Buraikiمشاهده پروفایل

استاد

• AI/ML Robotic Vision
• Autonomous Systems
• Automatic Control
• +۵ مورد دیگر

Omar AL-Buraiki is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He specializes in Robotics Automation, Machine Intelligence Algorithms, and Autonomous Systems, with over 12 years of research experience across Canadian and international institutions. His work focuses on developing industrially oriented solutions for defense, robotics, and telecommunications sectors. Education: Ph.D., Electrical and Computer Engineering, University of Ottawa M.A.Sc., Systems Engineering, King Fahad University B.Sc., Electronics and Communications Engineering, Hadhramout University Research Interests: His research spans AI/ML Robotic Vision, Autonomous Systems, Automatic Control, Intelligent Automation, and Multi-Robot Coordination. He has pioneered nonlinear control systems, task allocation algorithms, and machine learning-driven robotic vision solutions, notably through his leadership of the SMART Lab during his PhD. Publications & Trends: His 20+ publications emphasize probabilistic task allocation, sensor fusion, and adaptive control in robotics. Recent work (2023–2025) focuses on slip-compensated visual-inertial odometry and heterogeneous robotic systems under actuator delays. Earlier papers (2016–2020) established foundational methods for specialized agent task assignment in swarm robotics. Achievements: Secured a USPTO patent for robotics automation technology Over 20 peer-reviewed articles and 12+ conference presentations Teaching & Advising: Instructor for courses on control systems, robotics, and AI-based vision at both undergraduate and postgraduate levels Developed specialized courses like Autonomous Vehicles Navigation and Control Labs & Teams: Former leader of the SMART Lab (University of Ottawa), currently active in robotics research at the University of Waterloo’s Mechanical and Mechatronics Engineering Department.

Raffaele Rossiمشاهده پروفایل

استاد

• Macroeconomics
• Public Finance
• Applied Time Series Econometrics
• +۴ مورد دیگر

Raffaele Rossi is a Professor of Economics in the Department of Economics at the Birmingham Business School, University of Birmingham , where he also serves as Director of the MSc Money, Banking and Finance Programme. He joined the university in July 2023, having previously held the position of Reader at the University of Manchester. PhD, University of Glasgow MSc, University of Edinburgh BSc, University of Milano-Bicocca His research lies at the intersection of macroeconomics, public finance, and applied time series econometrics , with additional interests in inequality, household finance, monetary policy, and fiscal policy. His work often employs structural estimation and dynamic macroeconomic modeling to analyze tax reforms, fiscal-monetary interactions, and economic shocks. The recent publications of Professor Rossi reveal a strong focus on the long-term effects of taxation, particularly marginal and consumption tax changes, using structural models. His work also addresses contemporary macroeconomic challenges such as the economic impacts of the COVID-19 pandemic and uncertainty. A recurring theme is the design and evaluation of optimal fiscal and monetary policy rules in complex economic environments, often incorporating habits, rule-of-thumb consumers, and sectoral heterogeneity. While no formal awards are listed in the provided texts, his publications in leading journals such as the Journal of Monetary Economics , American Economic Journal: Economic Policy , and European Economic Review attest to the high caliber of his research. His work demonstrates a consistent commitment to rigorous empirical and theoretical analysis of critical policy issues in modern macroeconomics. Professor Rossi supervises research in macroeconomic policy and econometrics, though specific student names are not listed. He is actively engaged in ongoing research, including working papers on taxing consumption in unequal economies and housing credit supply shocks. He is based at the University of Birmingham, contributing to the research and teaching mission of the Department of Economics.

Miljana L. Milićمشاهده پروفایل

استاد

• Electronics
• VLSI Design
• Analog and Digital Circuit Diagnosis
• +۷ مورد دیگر

Miljana L. Milić is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, Department of Electronics. She has been an integral part of this institution since earning her degrees and advancing through academic ranks, culminating in her appointment as full professor in 2024. Education: PhD in Electronics, Faculty of Electronics, University of Niš (2009) Master’s in Electronics, Faculty of Electronics, University of Niš (2005) Bachelor’s in Electronics, Faculty of Electronics, University of Niš (2001) Her research interests span a broad spectrum of electronics engineering, with emphasis on VLSI design, analog and digital circuit diagnosis, cryptographic hardware security, timing analysis under aging, and performance optimization in neural prediction systems. She applies simulation, statistical methods, and AI techniques to solve complex problems in electronic systems design and reliability. The analysis of her recent publications reveals a strong focus on hardware-level innovation, including secure cryptographic cells, fault diagnosis in analog circuits, performance modeling under fading and shadowing, and timing degradation in VLSI systems. Her work bridges theoretical analysis with practical implementation in electronic design and communication systems. She leads the Laboratory for Design of Electronic Processes, Circuits and Automatic Control Systems, and is currently involved in one national research project. Her contributions include over 10 journal publications in high-impact venues. Scientific Contributions: Head of Laboratory for Design of Electronic Processes, Circuits and Automatic Control Systems Active participant in national research projects Author/co-author of 10+ journal papers with impact factor She mentors students through research supervision and contributes to academic leadership within her department. Her work continues to influence both academic research and practical applications in electronic systems engineering.

• Control Engineering
• Automatic Control
• Sliding Mode Control
• +۳ مورد دیگر

Staniša Perić is an Associate Professor at the Department of Automation, Faculty of Electronic Engineering, University of Niš, Serbia. Elected to this position in 2023, he specializes in control systems and signal processing with 21 impact-factor journal publications to his name. Education: PhD in Systems Control, Faculty of Electronic Engineering, University of Niš (2016) Diploma in Automation, Faculty of Electronic Engineering, University of Niš (2009) Research Focus: Dr. Perić's work bridges Control Engineering and Signal Processing , with groundbreaking contributions in Sliding Mode Control for Automotive Systems and Advanced FIR Filter Design . His research integrates Orthogonal Endocrine Neural Networks for real-time control applications, particularly in Anti-lock Braking Systems (ABS), while developing ultra-selective multiplierless filters for hardware-efficient signal processing. Publication Analysis: His 11 representative publications (2010-2017) reveal a clear trajectory: early work focused on orthogonal function applications in antenna systems and filter design, evolving into neural network-enhanced sliding mode control for automotive safety systems. The 2015-2017 period shows intensive development of biologically inspired neural networks for dynamic system modeling and time-series forecasting. Research Engagement: National Research Projects: 2 International Research Projects: 4 Collaborative Environment: As a core member of the Department of Automation, Dr. Perić contributes to research groups specializing in intelligent control systems and advanced signal processing. His work directly supports the faculty's strategic focus on automotive electronics and embedded control solutions, with experimental validation conducted through university-industry partnerships in automotive safety systems.

Boris Jutziمشاهده پروفایل

استاد

• Active Optical Sensors
• Computer Vision
• Laser Scanning
• +۷ مورد دیگر

Prof. Dr. Boris Jutzi is a leading academic in photogrammetry and remote sensing, currently serving as Professor at Karlsruhe Institute of Technology (KIT) and acting head of the Chair of Photogrammetry and Remote Sensing at Technical University of Munich (TUM). His research focuses on active optical sensors, computer vision, laser scanning, and 3D reconstruction techniques. He holds a diploma in electrical engineering from University of Kaiserslautern, a PhD from TUM, and venia legendi from KIT. Affiliations: KIT Institute of Photogrammetry and Remote Sensing, TUM Chair of Photogrammetry and Remote Sensing Education: Diploma in Electrical Engineering (University of Kaiserslautern) Doctorate (TUM) Venia Legendi (KIT) His research interests include: Neural Radiance Fields (NeRF) for 3D reconstruction LiDAR technology and applications UAV-based remote sensing Geospatial data fusion Computer vision in environmental monitoring Awards: Faculty Teaching Award (2020) Best Paper Awards (2019, 2016, 2014, 2006) His work emphasizes novel sensor integration and automated scene analysis , with contributions to urban and environmental 3D modeling. Current projects include underwater scene reconstruction via NeRFs and large-scale digital twin datasets (e.g., TUM2TWIN).

Volker Heunمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Protein Folding
• Data Structures
• +۴ مورد دیگر

Volker Heun is a Professor of Bioinformatics at Ludwig-Maximilians-Universität München (LMU), holding positions within the Faculty of Mathematics, Computer Science, and Statistics since 2003. He currently serves as Vice Dean of the faculty and holds various academic coordination roles, including Bioinformatics program coordination and ERASMUS coordination. His academic journey includes a Ph.D. from TU München (1996) and research fellowships at the International Computer Science Institute (Berkeley, 1997–1998). Education: Studied Computer Science and Mathematics at Johann Wolfgang Goethe-Universität Frankfurt (1984–1991), followed by research roles at TU München and other institutions. Professional roles include Research Assistant (1991–1996) and Research Associate (1996–2003). Research focuses on algorithmic bioinformatics, protein folding, data structures, and computational biology. Key contributions include work on RNA sequence-structure analysis, chemical shift correction tools (e.g., CheckShift), and efficient algorithms for range queries and suffix arrays. He has authored multiple influential books on algorithms and bioinformatics. Publications span over two decades, emphasizing algorithm design, bioinformatics applications, and parallel computing. Supervised PhD students include Benjamin Albrecht (2016), Luca Chiarabini (2009), and Simon W. Ginzinger (2008). Teaching includes courses on Algorithmic Bioinformatics, Algorithms on Sequences, and bioinformatics-related seminars. Administrative roles include serving as Vice Dean (2004–2007, 2013–2017, 2019–present) and Dean (2011–2013, 2017–2019). His work bridges theoretical computer science and applied bioinformatics, with a focus on interdisciplinary research.

• Automation Engineering
• Control Systems
• Industrial Automation
• +۵ مورد دیگر

Срђан Вукмировић is a Full Professor at the Department of Computer Science and Automation, Faculty of Technical Sciences, University of Novi Sad. He specializes in automation, control systems, and industrial data management. His academic journey includes a B.Sc. (2000), M.Sc. (2004), and Ph.D. (2011) from the University of Novi Sad. Education: B.Sc. in Computer Technology and System Control (2000) M.Sc. in Automatic Control (2004), Thesis: 'Application of OPC XML-DA Servers in Supervisory Control Systems' Ph.D. (2011), Thesis: 'Intelligent Task Scheduling in Large Supervisory Control Systems' Research focuses on industrial automation, distributed control systems, and optimization techniques. He has participated in 6 scientific projects and conducted international collaboration through training at the University of Lugano (Switzerland) and University of Cologne (Germany) under COST EU projects (2011). Teaching includes courses such as Automatic Control Systems, System Modeling & Simulation, Optimization Methods, and Distributed Control Systems. Affiliations include IEEE and Microsoft Professional. His work emphasizes practical implementation of industrial control devices and data acquisition systems, combining theoretical expertise with real-world applications.