Serdar ULUSOY is an Associate Professor in the Department of Civil Engineering at Turkish-German University's Faculty of Engineering. He holds a PhD from Istanbul University-Cerrahpaşa (2019), an MSc from Hochschule für Technik Stuttgart (2014), and a BSc from Atatürk University (2009). His research focuses on structural control, optimization techniques, and metaheuristic algorithm applications in civil engineering contexts. Doctorate (2019): Istanbul University-Cerrahpaşa Institute of Graduate Education, Civil Engineering MSc (2014): Structural Engineering, Hochschule für Technik Stuttgart BSc (2009): Civil Engineering, Atatürk University Faculty of Engineering His work spans fire safety engineering, seismic isolation, reinforced concrete optimization, and steel structure performance analysis. Articles demonstrate expertise in applying PID controllers, genetic algorithms, and ant colony optimization to structural dynamics problems. Current research trends emphasize metaheuristic algorithm implementation for structural systems under extreme conditions (fire, seismic), with notable contributions to time delay analysis and hybrid control system optimization.
Isabel Maria Sousa Jesus é uma Professora do Instituto Superior de Engenharia do Porto (ISEP), com uma carreira acadêmica focada em Engenharia Elétrica, Eletrônica e Ciência da Computação. Ela atua como pesquisadora no Grupo de Investigação em Engenharia e Computação Inteligente para a Inovação e o Desenvolvimento e no Laboratório Associado de Sistemas Inteligentes. Seus cargos incluem Diretora do Laboratório de Eletromagnetismo desde 2008 e Membro do Conselho Pedagógico do ISEP. Além disso, é pesquisadora em 20 projetos científicos e gerente técnica do consórcio Machine Intelligence Research Labs (MIR Labs).
Alexander Dowling is an Assistant Professor at the Department of Chemical and Biomolecular Engineering at the University of Notre Dame. He also holds affiliations with the Department of Applied & Computational Mathematics & Statistics and Notre Dame's energy and nanoscience centers. Ph.D., Chemical Engineering, Carnegie Mellon University B.S.E., Chemical Engineering, University of Michigan His research focuses on integrating chemical engineering, computational optimization, and uncertainty quantification to advance sustainable energy and environmental technologies. Key application areas include: Energy markets and infrastructure Carbon capture and sequestration Shale gas utilization Advanced separations (membranes, ionic liquids) Machine learning for bridging timescales Recent publications highlight his work in: Energy storage system optimization Refrigerant separation using ionic liquids Frequency regulation market economics PID control algorithms with stochastic programming Multi-scale electricity market frameworks Selected scientific awards include: James A. Burns, C.S.C., Award (2025) NSF CAREER award (2020) He teaches courses in data analytics, optimization, and control at both undergraduate and graduate levels. His lab develops mathematical modeling frameworks for resilient energy infrastructures.
Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.
Станиша Перић serves as an Associate Professor in the Department of Automation at the Faculty of Electronic Engineering, University of Niš. His academic appointments and research activities are centered on advanced control systems and signal processing within Serbia's prominent technical institution. His educational foundation includes: BSc in Automation (2009, Faculty of Electronic Engineering, University of Niš) PhD in System Control (2016, Faculty of Electronic Engineering, University of Niš) Perić's research spans Control Systems , Signal Processing , and Automotive Control , with emphasis on sliding mode control, neural network integration, and orthogonal function applications. His work bridges theoretical control engineering with practical automotive implementations, particularly in anti-lock braking systems. Recent publications demonstrate a clear trajectory toward adaptive neural control architectures and real-time system optimization. With 21 impact-factor journal publications, his research output shows consistent focus on digital control algorithms and filter design. Current involvement in 6 active projects (2 national, 4 international) underscores his collaborative research profile. Dr. Perić actively supervises graduate students in control engineering while securing research funding through national and international grants. His laboratory work within the Department of Automation focuses on experimental validation of control algorithms using automotive test benches and real-time simulation environments.
Lazar Alexandru is an Associate Professor at the Technical University of Iasi, specializing in control systems and electrical engineering. His research focuses on predictive control, model-free algorithms, and their applications in automotive systems, electric machines, and robotics. He has published extensively on topics such as motor control, vehicle platooning, and industrial automation. His work emphasizes practical implementation and simulation using tools like MATLAB and LabVIEW. Research interests include advanced control strategies for electric drives, nonlinear systems, and data-driven methods. His contributions span theoretical development and experimental validation, with a particular emphasis on automotive and aerospace applications. His articles explore predictive current control, model-free adaptive systems, and real-time control architectures. Despite his prolific output, no awards or grants are explicitly mentioned in the text. Advising details are also unavailable, though his work suggests involvement in graduate research projects.
Mehmet Akar is a full-time Professor at the Department of Electrical and Electronics Engineering, Faculty of Engineering, Boğaziçi University. His research focuses on control systems, networked control systems, multi-agent coordination, wireless communication, and optimization algorithms. He has contributed extensively to the fields of distributed consensus protocols, resilient networks, and resource allocation in heterogeneous systems. His work integrates mathematical system theory with practical applications in automotive systems, wireless networks, and industrial process control. Key research themes include cluster consensus in multi-agent networks, Byzantine-resilient algorithms, and adaptive control strategies for complex systems. Recent publications emphasize distributed algorithms for resource allocation in NOMA/MEC systems and fault-tolerant consensus mechanisms. His research also addresses challenges in vehicular platooning, industrial process modeling, and time-delay systems. Award-winning interdisciplinary contributions bridge theoretical advancements and real-world applications, with active engagement in both academic research and industry collaborations.
Fikret Çalışkan serves as a Professor in the Department of Control and Automation Engineering at Istanbul Technical University. With an h-index of 15 and 55 research outputs documented through Scopus, his academic career spans multiple decades of contributions to control systems engineering. His research profile shows consistent publication activity from 1995 through projected 2025 works, with significant output in recent years. Professor Çalışkan's research focuses on advanced control systems with particular expertise in Kalman filtering techniques , fault detection and isolation , and autonomous aircraft systems . His fingerprint analysis reveals strong specialization in actuator systems (80%), Kalman filters (100%), and multiple fault detection methodologies (47-53%). His work bridges theoretical control engineering with practical applications in UAV technology, indoor positioning systems, and aircraft propulsion. Analysis of his recent publications shows a clear trajectory toward increasingly complex autonomous systems, with growing integration of machine learning techniques (particularly reinforcement learning) with traditional control methodologies. His work demonstrates strong interdisciplinary connections between aerospace engineering, robotics, and signal processing, with applications spanning from indoor navigation to hybrid-electric aircraft propulsion. Professor Çalışkan has supervised 15 research projects throughout his career, securing funding for significant research initiatives including AI-based visual inspection systems for aircraft surfaces (2020-2022) and nonlinear multirotor modeling, fault detection, and implementation (2018-2021). His research demonstrates strong industry relevance with practical applications in aviation safety, autonomous systems, and energy-efficient control solutions.
Carsten Behn is a Professor at Hochschule Schmalkalden, specializing in Engineering Mathematics within the Faculty of Mechanical Engineering. He holds a habilitation from TU Ilmenau in biologically inspired motion systems and has served as a lecturer at Hochschule Merseburg and TU Ilmenau. Education: Diplom-Mathematiker (2001), Dr.-Ing. (2005), Dr.-Ing. habil. (2013) His research focuses on tactile sensors for surface texture and object contour detection, adaptive control in compliant robotics, and mathematical modeling of biomimetic systems. Publications emphasize sensor dynamics, mechatronic design, and applications of vibrissae-inspired engineering. Recent articles explore tactile sensor signal tuning, flow detection using artificial vibrissae, and adaptive control in snake-like locomotion systems. His work bridges mechanical modeling, control theory, and bioinspired robotics. Behn has served on technical program committees for conferences like ICINCO 2018 and IEEE IRC 2019. He supervises PhD students Moritz Scharff and Lukas Merker in Mechanical Engineering at TU Ilmenau.
Stanimir Yordanov Yordanov is an Associate Professor at the Department of Automation, Information and Control Technology within the Faculty of Electrical Engineering and Electronics at Technical University - Gabrovo. With a Doctorate in Technical Sciences and over 30 years of professional experience since 1993, he has established himself as a leading researcher and educator in control systems and automation. His educational background includes a Master of Engineering from VMEI - Gabrovo (1992) with specializations in Computer Engineering, Management Technologies, and Pedagogy, followed by a Doctorate (2006) and Associate Professor qualification (2010) in specialized technical fields. His teaching portfolio encompasses System Programming, Operating Systems, Digital Control Systems, and Industrial Robotics, among others. Professor Yordanov's research focuses on automated control systems, intelligent management of technological processes, industrial system monitoring, and object/system modeling. His work demonstrates a consistent trajectory toward increasingly sophisticated control algorithms and applications across diverse domains from electrohydraulic systems to environmental monitoring. The recent publications reveal a strong emphasis on advanced control techniques including neuro-PID regulators, model predictive control, and applications of artificial intelligence in industrial contexts. His extensive project portfolio includes 22 significant research initiatives, ranging from national projects like the 'Competence Center for Intelligent Mechatronic Systems' to international collaborations such as the 'MechMate' project focused on European SME growth. These projects demonstrate his ability to secure funding and lead research teams across various technical domains. Professor Yordanov has mentored six PhD students to completion, with several successfully defending dissertations on topics including intelligent energy systems, embedded real-time operating systems, and robotic systems. His academic leadership extends to serving as an academic mentor for over 600 student internships. His laboratory work centers on electrohydraulic control systems, robotic manipulation, and intelligent monitoring applications, with recent projects developing smart dispensers, beehive monitoring systems, and low-cost health monitoring devices for pregnant women, demonstrating practical applications of his theoretical research.
Dan Sui is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, within the Faculty of Science and Technology. His research is centered on drilling automation, digitalization, artificial intelligence, machine learning, data analytics, modeling, optimization, and control systems in petroleum and geothermal energy contexts. His research interests span drilling automation, AI, machine learning, data processing, modeling, optimization, simulation, control system design (including model predictive control, PID, Kalman filters), advanced drilling technologies, drilling event detection, geothermal drilling, and digital twin development. He actively contributes to the development of smart drilling systems and data-driven models for real-time decision support. The recent publications (2020–2025) highlight a strong trend in applying reinforcement learning, deep learning, and data-driven modeling to drilling optimization, ROP prediction, well path design, and subsea control. These works are published in high-impact journals such as SPE Journal , Journal of Petroleum Science and Engineering , and Applied Sciences , as well as in proceedings from ASME and IADC/SPE conferences, indicating a strong presence in both petroleum and mechanical engineering domains. Automatic calibration of directional drilling control Multi-agent reinforcement learning for waterflooding PI controller tuning using Deep Q-Learning Safe operating envelope for directional drilling Neural network optimization for ROP prediction Real-time ROP trend analysis Well path optimization with Bezier curves Anti-collision trajectory design Automated drilling algorithms on lab rigs Subsea shuttle tanker depth control While no specific scientific awards are listed, his extensive publication record and involvement in AI and digital twin projects reflect significant recognition in the field. He advises students and collaborates on research involving laboratory-scale drilling automation systems, hybrid test environments, and smart drilling robots, contributing to both theoretical and applied advancements. His work includes development of algorithms for autonomous drilling agents, feature selection for kick detection, and experimental studies on drillstring dynamics. Dan Sui is a key contributor to the OpenLab project, a modern drilling digitalization infrastructure, and leads research in data quality improvement, downhole data correction, and sensor data reconstruction using recurrent neural networks. His lab-based work includes designing autonomous small-scale drilling rigs and testing machine learning algorithms for incident detection, showcasing a strong integration of experimental and computational research.
Thomas Badgwell is a Professor of Practice in the Department of Chemical Engineering at The University of Texas at Austin, affiliated with the Cockrell School of Engineering. He holds a Ph.D., M.S., and B.S. in Chemical Engineering from UT Austin (1992) and Rice University (1982). His research focuses on modeling, optimization, and control of chemical processes, with notable contributions to model predictive control (MPC) and integration with machine learning. He teaches courses including CHE 348 (Numerical Methods), CHE 354 (Transport Processes), and CHE 360 (Process Dynamics and Control). Awards & Honors: 2024: Babatunde A. Ogunnaike Control Practice Award (AACC) 2024: Distinguished Industrial Lecturer (IEEE Control Systems Society) 2022: Control Global Process Automation Hall of Fame 2013: Computing Practice Award (CAST Division) 2011: Fellow of AIChE His research bridges theoretical advancements and industrial applications, emphasizing MPC's role in process automation. Recent work explores machine learning integration, reinforcement learning for control systems, and digital manufacturing platforms. He has advised on advanced control systems for industries like oil refining and catalytic processes.
Douglas Cooper is a Professor in the Department of Chemical Engineering at the University of Connecticut (UConn). He holds a Ph.D. from the University of Colorado (1985) and has served in leadership roles including Vice Provost for Undergraduate Education (2009-2011), Department Head of Chemical Engineering (2004-2006), and Director of the Process Control Consortium (1997-2004). His research focuses on process control analysis, adaptive control systems, and pattern-based performance monitoring. He is the founder of Control Station, Inc., a software company focusing on process control tools. Cooper’s expertise spans advanced process modeling, monitoring, and control. His work emphasizes practical applications, including automated controller design and training simulators. Recent research explores pattern recognition for real-time controller performance evaluation. He has received numerous awards, including the 2008 ISA Excellence Award and induction into the Connecticut Academy of Science and Engineering (2004). He also holds teaching accolades, such as Connecticut Professor of the Year (2004). His publications cover topics like PID tuning, DMC control strategies, and disturbance rejection in power plants. He has authored textbooks such as Practical Process Control (2008) and developed educational software like the Control Station training simulator. His work bridges academic research and industry applications, emphasizing entrepreneurship in engineering education.
Aleksei Tepljakov is a Senior Researcher at Tallinn University of Technology's Department of Computer Systems, specializing in fractional-order control systems and intelligent automation. His research develops novel methodologies for robust control design. Research focuses on fractional-order PID controllers, robust stabilization techniques, and data-driven control applications in power systems and industrial processes. Recent work integrates digital twin technology for virtual commissioning of control systems. Publications emphasize computational approaches to stability analysis in time-delay systems and practical implementations in energy systems. Article trends show consistent innovation in fractional calculus applications for industrial control challenges. Leads research in extended reality interfaces for control engineering and digital twin implementation frameworks.
Eduard Petlenkov is a Tenured Full Professor at the Department of Computer Systems at Tallinn University of Technology (TalTech), leading the Centre for Intelligent Systems. His research focuses on advanced control systems, energy efficiency, robotics, and artificial intelligence applications. He holds the award of Recognized Lecturer 2023 . Key research areas include: Fractional-order control systems Data-driven optimization for energy systems Robotics and autonomous systems Model predictive control (MPC) Power grid stability and renewable integration Recent work emphasizes: Stabilization techniques for hybrid power systems Radar technologies for unmanned ground vehicles Energy harvesting in metamaterials Real-time control of unknown linear systems Smart grid digital twin frameworks His contributions span 20+ peer-reviewed articles (2022–2025), addressing topics like reinforcement learning in microgrid control and fractional-order PID tuning methods. Active in bridging industry 5.0 education through blended learning approaches.
