Professor Howard Schwartz is a distinguished academic in Electrical and Computer Engineering at Carleton University , with a career spanning academia, industry, and robotics research. He earned his BEng in Civil Engineering from McGill University (1982), followed by MSc (1984) and PhD (1986) in Aerospace and Mechanical Engineering from MIT. His academic leadership includes serving as Department Chairman (2009-2013) and authoring the seminal text Multi-Agent Machine Learning: A Reinforcement Approach (Wiley, 2014). As an Associate Editor for IEEE Transactions on Cybernetics , he shapes discourse in cybernetic systems and machine learning. Research Focus Professor Schwartz's research bridges theoretical and applied domains: Machine learning for robotics and autonomous systems Adaptive control theory and multi-agent systems Reinforcement learning algorithms with fuzzy logic integration Real-time video analytics and GPS receiver development Nonlinear control systems for UAVs and wind energy optimization Industry Experience His career includes impactful industry engagements: Early development of high-performance GPS receivers at Canadian Marconi Co. (1982-1984) Sabbatical at March Networks (2001-2002) for video analytics Software quality control at CMC Electronics (2008) Software development for TV set-top boxes at Espial Inc. (2014-2015)
Dr. Maha Elouni is an Assistant Professor of Computer Science at Randolph-Macon College since 2022, with expertise in Intelligent Transportation Systems , Machine Learning , and Traffic Control . She earned her PhD in Computer Engineering (2021) and MSc in Applied Mathematics (2015) from Virginia Polytechnic Institute and State University, and her BS and ME in Computer Science Engineering from the National School of Computer Science in Tunisia. PhD : Computer Engineering, Virginia Tech, 2021 MSc : Applied Mathematics, Virginia Tech, 2015 BS/ME : Computer Science Engineering, National School of Computer Science, Tunisia, 2012 Her research focuses on Intelligent Transportation Systems , integrating machine learning and control theory to optimize traffic flow and develop adaptive controllers for urban road networks. She has contributed to studies on sliding mode control , game-theoretic decentralized systems , and connected vehicle technologies . Selected publications highlight her work on network perimeter control , dynamic freeway speed controllers , and weather-adaptive traffic systems . Recent articles explore applications of machine learning and clustering algorithms in transportation engineering. Student of the Year , Urban Mobility and Equity Center (UMEC), 2020 Best Paper Award , VEHITS Conference, 2018 Dr. Elouni teaches foundational and advanced courses, including Introduction to Computer Science , Data Structures , Object-Oriented Programming , and Android App Development . Her interdisciplinary background bridges computer engineering , mathematics , and transportation systems .
Siri Schlanbusch is a Postdoctoral Researcher at the Department of Information & Communication Technology, Faculty of Engineering and Science, University of Agder. Her research focuses on advanced control systems, particularly adaptive and quantized control methodologies applied to mechanical systems such as helicopters, cranes, and robots with complex dynamics. She investigates challenges like input delays, quantization effects, and nonlinear uncertainties in real-world applications. Her work emphasizes practical implementation, with publications spanning both theoretical developments and experimental validations. Collaborations involve interdisciplinary projects in robotics, aerospace engineering, and marine systems. Schlanbusch contributes to advancing control strategies for underactuated systems, rigid body dynamics, and multi-loop control architectures. Key technical areas include backstepping control, sliding mode control, robust-adaptive algorithms, and uncertainty management. Her research bridges theoretical innovation with industrial relevance, addressing challenges in automation, signal processing, and mechanical engineering.
Marko T. Milojkovic is a full professor at the Faculty of Electronics, University of Nis, leading the Department of Automation since 2022. He holds a PhD in Systems Management (2012), Master's in Automation (2008), and a Bachelor's in Computer Engineering & Informatics (2003), all from the same institution. His research focuses on adaptive control systems, neural networks, and dynamical systems modeling, with 27 papers in impact-factor journals. He currently heads the Laboratory for Modeling, Simulation and Systems Management and participates in 2 national and 2 international projects. Education: PhD: Systems Management (2012) MSc: Automation (2008) BSc: Computer Engineering & Informatics (2003) Research interests include neuro-fuzzy systems, MIMO system optimization, and endocrine neural networks applied to adaptive control. His publications demonstrate expertise in quasi-orthogonal filters, sliding mode control, and time-series forecasting. No scientific awards are explicitly mentioned, but his extensive project participation highlights active collaboration in control systems and automation. Prof. Milojkovic's work bridges theoretical modeling and practical applications, with recent emphasis on intelligent control systems and nonlinear dynamics. His laboratory facilitates interdisciplinary projects addressing complex system management challenges.
Mohammad Hassan Khooban is an Associate Professor at the Department of Electrical and Computer Engineering, specializing in Electrical Energy Technology at Aarhus University . His research emphasizes advanced control strategies for power systems, renewable energy integration, and smart grid technology. While specific educational background details are not explicitly stated, his work demonstrates expertise in power electronics, control systems, and machine learning applications. His projects include pioneering initiatives like QuantumEcoCircuits (2024–2027) and Smart Synergy Mechanism (2023–2025), focusing on sustainable energy systems, electric vehicle charging dynamics, and resilient grid operations. His research interests span adaptive control methodologies, grid resilience under cyber threats, and the optimization of energy storage systems. He has contributed to peer-reviewed journals such as IET Renewable Power Generation and IEEE Transactions on Smart Grid , exploring topics ranging from PID controllers to fractional-order sliding mode control for unmanned aerial vehicles. No scientific awards are listed, but his work is supported through grants and collaborative projects. He is actively involved in lab initiatives related to power systems and renewable energy technologies.
Ramprasad Potluri is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has been contributing to control systems research and education. His academic journey includes a PhD from the University of Kentucky, USA (2003) and a Master of Science in Electrical Engineering from Saint Petersburg State Technical University, Russia (1996). Dr. Potluri's research focuses on the practical applications of Control Systems theory, with particular emphasis on motor control, autonomous vehicle navigation, and fault-tolerant systems. His work bridges theoretical control concepts with real-world engineering applications, making significant contributions to both academic research and educational practices in the field. His publication record from 2011-2015 demonstrates a strong focus on applying control theory to practical engineering challenges, particularly in the domains of electric vehicles, robotic systems, and educational laboratory development. His research shows consistent progression from theoretical foundations toward increasingly complex real-world applications, with a notable emphasis on making advanced control systems accessible through low-cost educational platforms. Best Teaching Assistant award recipient Manavaalan Gunasekaran developed the new Control Systems Laboratory under his guidance Jitendra Bharadwaj, working with Dr. Potluri and Dr. KS Venkatesh, received a first prize at Techkriti (IIT Kanpur technology festival) His laboratory work in Western Labs 217A focuses on developing practical control systems experiments that make advanced control concepts accessible to undergraduate students, complementing his research on advanced control applications in autonomous vehicles and robotics.
Maria Elena Martin Cañadas is an Associate Professor at the Department of Electrical Engineering within the Barcelona East School of Engineering (EEBE) at Universitat Politècnica de Catalunya (UPC). She leads research at the SEPIC group (Power Electronics and Control Systems), focusing on renewable energy integration and power systems optimization. Research Interests: Her work spans power electronics, microgrid design, energy policy analysis, and control systems for distributed generation. Key research areas include: Regulatory frameworks for renewable energy adoption Uncertainty modeling in energy systems High-temperature heat pump technologies Economic optimization of microgrids Solar energy integration and policy analysis Publication Trends: Recent articles (2020-2025) demonstrate strong focus on regulatory impacts in energy systems, with methodologies addressing uncertainty through stochastic modeling and probabilistic analysis. Dominant themes include microgrid optimization, solar policy evolution, and decarbonization strategies for industrial applications. Student Advising & Projects: Supervised doctoral candidates include Alonso (microgrid design), Coronas (distributed generation), and El Mariachet (power quality). Actively leads competitive R&D projects such as: Decarbonization of energy-intensive industries Power quality improvement in remote systems Regulatory framework development for Latin American biogas projects Research Group: Core member of SEPIC laboratory specializing in power electronics applications for sustainable energy systems, collaborating with industrial and international partners.
Shengdun Zhao is an active researcher in the fields of Electrical Engineering, Automotive Engineering, and Machine Learning, contributing extensively to optimization techniques and control systems for electric vehicles and motors. His work spans journals like IEEE Transactions on Vehicular Technology and Journal of Intelligent & Fuzzy Systems , focusing on practical applications of deep reinforcement learning, meta-learning, and multi-objective optimization. Key research areas: Electric motor control, energy management systems, and clustering algorithms. Collaborates with researchers such as Yiming Zhang, Wei Du, Chee-Kong Chui, and Chin-Boon Chng. His publications from 2007–2025 address technical challenges in mechatronics, sustainable transportation, and data-driven engineering solutions. Research Trends Recent articles highlight Zhao's emphasis on deep reinforcement learning for motor control, meta-learning in energy systems, and evolutionary algorithms for multi-objective optimization. He integrates machine learning with automotive engineering to improve electric vehicle efficiency and motor performance.
Mahmoud Amin is a Professor in the Department of Electrical & Computer Engineering at Manhattan College. He holds a Ph.D. from Florida International University and has over 70 publications in professional journals and conferences. His research focuses on power systems, smart grid security, and sustainable energy systems. He has secured grants totaling over $2 million, including the Intel FPGA University Program Grant and the Typhoon HIL award. He advises numerous students and leads research projects involving advanced power electronics and grid integration. Amin is a Senior Member of IEEE and active in multiple technical committees. Education: B.Sc. in Electrical Power & Machines Engineering, Helwan University (Cairo, Egypt) M.S. in Electrical Power & Machines Engineering, Helwan University Ph.D. in Electrical Engineering, Florida International University Research Interests: His work emphasizes power systems reliability, renewable energy integration, and advanced control techniques for sustainable energy applications. He has developed laboratory-scale testbeds for power electronics and smart grid research. His projects include hardware-in-loop realizations and cybersecurity mitigation strategies for smart grids. Grants & Awards: $2M grant for energy storage in renewable power systems (KSA Ministry of Education) $20K Typhoon HIL award for HIL402 emulator development Intel FPGA Grant ($8K, 2016) 7x24 University Challenge Award (2019) Professional Activities: Amin chairs Manhattan College's ABET committee and serves on editorial boards for IEEE Transactions. He advises student chapters of IEEE and robotics clubs. His leadership includes the NSF-funded Engineering Scholars Training and Retention (STAR) Center. Labs & Infrastructure: He oversees the power laboratory in Leo 305, equipped with advanced testbeds for power electronics and machine drives. Recent upgrades include $35K in equipment for experimental validation of smart grid technologies.
Meysam Razmara is an Adjunct Assistant Professor in the Department of Mechanical and Aerospace Engineering. His research focuses on advanced control strategies for energy systems and building-grid integration. PhD in Mechanical Engineering from Michigan Technological University His work spans Model Predictive Control (MPC) , Exergy-based Control of Energy Systems , and Integration of Energy Efficient Buildings to Grid (B2G) . Key applications include HVAC systems, MicroCSP, and solar energy. Recent publications highlight trends in Building-to-Grid Systems , Exergy Analysis , and Optimal Control for energy efficiency. Collaborative projects emphasize demand response, renewable integration, and grid flexibility. While no specific awards are listed, his research contributes to Smart Grids , Energy Storage , and Automotive Control Systems . He has not been explicitly identified as advising students or securing grants in this dataset.
Md Rasedul Islam is an Associate Professor in the Department of Mechanical Engineering at the University of Wisconsin-Green Bay, affiliated with the College of Science, Engineering and Technology. His research focuses on bio-robotics, ergonomic mechanisms, intelligent control systems, and automation. Ph.D. in Mechanical Engineering (2020), University of Wisconsin-Milwaukee B.Sc. in Mechanical Engineering (2012), Khulna University of Engineering & Technology, Bangladesh Islam's work bridges robotics with rehabilitation, emphasizing wearable systems like exoskeletons and humanoid robots. His studies explore hybrid control methodologies, adaptive algorithms, and automation of dynamic systems for therapeutic applications. His publications highlight advancements in exoskeleton robotics, EMG-based control, and real-time sensor systems. Notable awards include the Chancellor Graduate Student Award (2020), Distinguished Dissertation Fellowship (2018-2019), and Prime Minister Gold Medal (2012).
Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.
Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.
Bo Wang is an active academic researcher primarily affiliated with multiple Chinese institutions, with strong connections to Tsinghua University, Beijing Jiaotong University, and other leading Chinese universities. His research spans artificial intelligence, machine learning, computer vision, medical image analysis, and intelligent control systems, demonstrating significant interdisciplinary work across computer science, engineering, and biomedical applications. Primary institutional affiliation: School of Computer Science and Technology at multiple Chinese universities Active research areas: AI/ML applications in healthcare, computer vision, federated learning, and intelligent control systems Extensive publication record across top-tier venues in multiple disciplines Wang's research interests focus on the intersection of artificial intelligence and practical applications. His work demonstrates strong expertise in developing novel machine learning architectures for medical image analysis, including applications in CT imaging, MRI, and sperm tracking. He has made significant contributions to federated learning approaches for large language models, sliding mode control systems, and molecular optimization frameworks. His research consistently bridges theoretical advances with practical implementations across healthcare, manufacturing, and environmental monitoring domains. Analysis of Wang's recent publications reveals a strong trend toward interdisciplinary AI applications, particularly in medical imaging and bioinformatics. His work on VAE-GANMDA for microbe-drug association prediction, ACE-QSM for accelerating MRI acquisition, and text-guided molecular optimization demonstrates innovative approaches at the intersection of AI and life sciences. Wang also maintains active research in industrial applications including digital twin technology for energy systems and robust scheduling approaches for multi-factory production. Notable research contributions include: FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method with Federated Learning VAE-GANMDA: Microbe-drug association prediction model ACE-QSM: Accelerating quantitative susceptibility mapping using diffusion models Digital twin-empowered power consumption prediction systems Wang actively collaborates with researchers across China and internationally, with publications spanning computer science, engineering, medical imaging, and environmental science journals. His work demonstrates strong technical depth across multiple AI methodologies while maintaining focus on practical applications that address real-world challenges in healthcare, manufacturing, and environmental monitoring.
Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.
