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Aristia Ioana PopoviciView profile

Lecturer

• Mechanical Engineering
• Wear and Friction Analysis
• Hydraulic Systems
• +4 more

Aristia Ioana Popovici serves as a Lecturer in the Department of Machines and Advanced Technologies in Construction at the Technical University of Civil Engineering Bucharest. She holds a Doctor of Engineering Sciences degree and leads research in mechanical systems for construction equipment within the Technological Equipment Engineering in Construction research center. Education: 2008: Graduated from Faculty of Technological Equipment, Technical University of Construction Bucharest 2009: Advanced studies in Engineering of Recovery and Recycling of Construction Materials 2012: Doctor of Engineering Sciences (Thesis: "Contributions to the study of wear effects on the performance of automatic control hydraulic systems") Dr. Popovici specializes in wear analysis of mechanical/hydraulic construction systems and sustainable energy applications. Her research integrates friction dynamics, vibration diagnostics, and green energy solutions for construction machinery, with recent focus on solar-powered drive systems and material recovery technologies. Research Projects: SOLUT (2022) : Project director for "Construction machinery drive system using solar energy" (UTCB-CDI-2022-016) Intelligent Buildings Practice Bases (2021) : Project member developing UTCB educational infrastructure (CNFIS-FDI-2021-0147) ROSE/INTRU (2019) : Tutor for secondary education project on construction material reuse (Grant AG242/SGU/NC/II)

• Pattern recognition
• Optimization

Zerhouni Noreddine serves as a Professor at the National School of Mechanics and Microtechnology (ENSMM) in Besançon, France, with institutional affiliation to the FEMTO-ST Institute's Department of Automation, Systems and Micro-Mechatronics (AS2M). He is an active member of the International Federation of Automatic Control (IFAC) Technical Committee 5.2 focused on Management and Control in Manufacturing and Logistics. His research program centers on Pattern recognition and Optimization methodologies, specifically targeting applications in cyber-physical manufacturing systems and logistics engineering. These interests align with his department's focus on automation and micro-mechatronics within industrial contexts. The FEMTO-ST Institute operates as a joint research unit (UMR 6174) under the French National Center for Scientific Research (CNRS), University of Franche-Comté, ENSMM, and University of Technology of Belfort-Montbéliard. Within this structure, AS2M develops advanced control systems for manufacturing enterprises, reflecting Zerhouni's technical specialization.

Wolfgang MarwanView profile

Professor

• Molecular Networks
• Systems Biology
• Cell Differentiation
• +6 more

Prof. Wolfgang Marwan is a Full Professor at the Institute of Biology within the Faculty of Natural Sciences at Otto von Guericke University Magdeburg. His research focuses on the structure and dynamics of molecular networks in pro- and eukaryotes, with particular expertise in reverse engineering regulatory networks, sensory control of sporulation in Physarum polycephalum , and light-controlled swimming behavior in Halobacterium salinarum . His educational background includes studies in Chemistry and Biology at the University of Erlangen (1979-1984), followed by a Diploma in Biology (1985) and PhD at the Max Planck Institute for Biochemistry (1985-1989). After postdoctoral work and serving as a Group Leader at the Max Planck Institute, he held positions at the University of Freiburg as a Heisenberg Fellow and at the University of Hertfordshire as a Research Professor of Biocomputation before joining Otto von Guericke University in 2005. In 2007, he co-founded the Magdeburg Centre for Systems Biology. Prof. Marwan's research interests center on understanding cellular decision-making processes through systems biology approaches. His work integrates experimental and computational methods to investigate how molecular networks control cell differentiation and reprogramming. He has made significant contributions to the field of Petri net modeling of biological systems, developing frameworks for biomodel engineering and network reconstruction. His recent publications demonstrate a consistent focus on cellular differentiation mechanisms, particularly in Physarum polycephalum , where his team has pioneered methods for single-cell time series analysis to empirically validate the Waddington landscape concept. His scientific contributions include the development of methods for automatic network reconstruction from time-series data and the application of Petri nets to model complex biological processes. His interdisciplinary approach bridges experimental biology with computational modeling to provide quantitative insights into cellular decision-making, with implications for understanding stem cell differentiation and regenerative medicine. Prof. Marwan has led numerous research projects funded by the DFG and EU programs, including studies on cellular reprogramming, pain regulation mechanisms, and signal transduction networks. His laboratory investigates how cells make differentiation decisions, with particular focus on the molecular switches that control commitment points in developmental pathways.

• Energy Systems
• Control Engineering
• Railway Systems
• +3 more

Anne-Lise GEHIN is a Lecturer (HDR) at the University of Lille, affiliated with the ESPRIT School of Engineering. She maintains an office in room S3.25 at ESPRIT within Lille's Scientific City and is a core member of the PERSI research team at CRIStAL (Centre de Recherche en Informatique, Signal et Automatique de Lille). Her research spans energy systems engineering with emphasis on resilient production frameworks, particularly green hydrogen infrastructure. She pioneers diagnostic methodologies using 'Energy Activity' metrics and develops model-generation techniques for supervising hybrid dynamic systems in critical applications like railway networks. Her work bridges theoretical control systems with industrial energy transition challenges. Dr. GEHIN has supervised three doctoral theses within CRIStAL: Mohd Faizan: 'Design and management of efficient and resilient energy production systems. Application to green hydrogen production systems.' Manarshhjot Singh: 'Energy Activity: a common metric to perform diagnosis and prognosis' (defended April 22, 2021) Beranger Six: 'Automatic generation of models for the supervision of hybrid dynamic systems: Application to railway systems' (supported September 27, 2018) She actively contributes to the PERSI team's mission in signal processing and system supervision within CRIStAL's multidisciplinary research ecosystem, focusing on practical implementations for industrial resilience.

Simon Pierre DembeleView profile

Lecturer

• Database Systems
• Green Computing
• Energy Efficiency
• +2 more

Simon Pierre Dembele is a Lecturer (ATER) at ISAE-ENSMA, France, affiliated with the LIAS laboratory—a joint research unit between ISAE-ENSMA and the University of Poitiers with locations in Poitiers and Chasseneuil. He is a core member of the Data Engineering Team within LIAS, focusing on sustainable data management systems. Dr. Dembele holds a PhD and specializes in green computing for database systems. His research interests include: Database Systems Green Computing Energy Efficiency Big Data Machine Learning His work addresses carbon footprint reduction in data-intensive applications through innovative query optimization techniques, with emphasis on parallel processing and machine learning integration. Recent publications (2018-2020) demonstrate consistent advancements in energy-efficient database deployment and auditing methodologies. Scientific Awards: No awards documented in source material Advising and Grants: No information provided regarding student supervision, research grants, or funding sources in the available text. Dr. Dembele operates within the Data Engineering Team at LIAS, which collaborates closely with the Automatic Control and Real Time research teams on cross-disciplinary sustainable computing initiatives.

Ahmad GhamrawiView profile

Research Fellow

• Renewable Energy
• Solar Power
• Power Electronics
• +3 more

Ahmad Ghamrawi is a Postdoctoral Researcher in the Automatic Control Team at the LIAS laboratory, part of the School of Engineering (ENSIP) at the University of Poitiers, France. His research focuses on renewable energy systems, specializing in solar photovoltaic technology and power electronics for energy conversion. He holds a Ph.D. in Electrical Energy from the University of Poitiers (2018), with his thesis titled "Optimisation et gestion des flux énergétiques d'un générateur solaire photovoltaïque" addressing optimization of photovoltaic generator energy flows. His core expertise spans control systems design, maximum power point tracking algorithms, and DC-DC converter topologies for solar applications. Dr. Ghamrawi's publication record from 2016-2021 reveals concentrated innovation in photovoltaic system efficiency. Key contributions include dual-mode MPPT algorithms, comparative analyses of quadratic versus standard DC-DC converters, and variable step-size control strategies. His work consistently bridges theoretical control design with practical power electronics implementation in solar energy systems. As an active member of the Automatic Control Team within the LIAS laboratory—a joint research unit of the University of Poitiers and ISAE-ENSMA—he contributes to advancing control theory applications in energy systems. His research demonstrates strong alignment with international power electronics conferences including IECON, EPE, and ENERGYCON.

• Control Systems
• Mathematical Modeling
• Network Theory
• +4 more

Mohammad Ghousein is a Postdoctoral Researcher in the Department of Automatic Control and Systems at the School of Engineering, University of Poitiers. He is a core member of the Automatic Control Team within the Laboratoire d'Ingénierie des Systèmes Automatisés (LIAS), a joint research unit operating across two campuses: ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil-du-Bois. His research spans Control Systems , Mathematical Modeling , and Network Theory with applications in Refrigeration Engineering and Epidemiology . Key methodologies include adaptive observer design for uncertain PDE-ODE systems and stochastic modeling of networked epidemic processes with time delays. His work bridges theoretical control theory with industrial and public health applications, emphasizing robustness under uncertainty and complex system dynamics. Recent publications demonstrate expertise in applying advanced mathematical frameworks to real-world problems: refrigeration system optimization through hyperbolic PDE control and epidemic forecasting using semi-Markov network models. These contributions highlight interdisciplinary approaches combining control theory, thermal engineering, and public health analytics. He operates within LIAS laboratory's infrastructure, utilizing facilities at both the Poitiers campus (Bâtiment B25, 2 rue Pierre Brousse) and the ISAE-ENSMA campus (Téléport 2, 1 avenue Clément Ader), focusing on automated systems engineering research.

• Data Engineering
• Automatic Control
• Real-Time Systems
• +1 more

Michel KAYS serves as an Associate Professor in the Data Engineering department at ISAE-ENSMA, a premier French engineering institution specializing in aeronautics and aerospace. He is affiliated with the LIAS (Laboratory of Information Systems and Automation for Systems) laboratory, operating across dual sites: ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil-du-Bois, France. His work integrates with three core research teams—Data Engineering, Automatic Control, and Real Time—within LIAS, reflecting his cross-disciplinary technical engagement. Dr. KAYS' research spans Data Engineering with critical applications in aerospace contexts, emphasizing robust data processing pipelines and system integration. His work extends into Automatic Control for precision engineering systems and Real-Time Systems requiring deterministic computational responses. These interconnected fields address aerospace industry challenges where data fidelity, control accuracy, and timing constraints directly impact system safety and performance in flight-critical applications. No student advisement records or scientific awards are documented in available sources. However, his laboratory affiliation indicates active participation in industrial projects and collaborative research initiatives, leveraging LIAS' infrastructure for applied aerospace solutions. The laboratory's structure supports technology transfer through software development, patent generation, and industry partnerships documented in their project portfolio. LIAS operates as Dr. KAYS' primary research ecosystem, with the Data Engineering Team focusing on scalable data architectures, the Automatic Control Team on dynamic system regulation algorithms, and the Real Time Team on deadline-guaranteed computing frameworks. This tripartite structure enables comprehensive aerospace system development from sensor data acquisition through control execution, with physical facilities spanning Poitiers' urban campus and Chasseneuil's aerospace-dedicated research park.

Adrian S. LewisView profile

Professor

• Nonsmooth Optimization
• Variational Analysis
• Eigenvalue Optimization
• +6 more

Adrian S. Lewis is the Samuel B. Eckert Professor of Engineering at Cornell University's School of Operations Research and Information Engineering. His research focuses on nonsmooth optimization, variational analysis, and semi-algebraic geometry, with applications in control systems and applied modeling. He holds a B.A., M.A., and Ph.D. from Cambridge University. Education: B.A. (Mathematics), Cambridge University (1983); M.A. and Ph.D. (Engineering), Cambridge University (1987). Research interests include optimization algorithms, eigenvalue problems, and the intersection of classical mathematics with computational methods. He has authored 100+ publications and serves as Co-Editor of Mathematical Programming . Notable awards: 1995 Aisenstadt Prize, 2003 Lagrange Prize, 2005 SIAM Outstanding Paper Prize. Editorial roles: Mathematical Programming, SIAM Journals, and others. Teaching spans linear optimization, nonlinear optimization theory, and convex analysis at both undergraduate and PhD levels.

Musa AuduView profile

Research Professor

• Musculoskeletal Modeling
• Rehabilitation Engineering
• Human Locomotion
• +4 more

Dr. Musa Audu is a Research Professor in the Department of Biomedical Engineering at Case Western Reserve University, with joint appointments in the Case School of Engineering and School of Medicine. He specializes in musculoskeletal computer modeling applied to human locomotion and rehabilitation technologies for spinal cord injury patients. His research focuses on developing computational models and control systems for neuroprosthetic devices. His educational background includes: Bachelor of Engineering in Mechanical Engineering from Ahmadu Bello University (1977) Master of Engineering in Mechanical Design from Ahmadu Bello University (1980) PhD in Biomechanics from Case Western Reserve University (1985) Dr. Audu's primary research explores: Dynamic optimization of human bipedal standing and seated postures Design of rehabilitation devices for mobility-impaired individuals Control systems for functional neuromuscular stimulation Biomechanical modeling of human locomotion His work provides critical insights into neural control mechanisms without invasive human experimentation. Analysis of his recent publications reveals strong focus on: Hybrid neuroprosthetic systems combining electrical stimulation and robotic exoskeletons Machine learning applications for gait stabilization Biomechanical assessment of paralyzed muscle function Postural control mechanisms in spinal cord injury patients with increasing emphasis on real-time control algorithms and experimental validation of theoretical models. Dr. Audu leads research at the APT Center (Advanced Platform Technology Center) and collaborates extensively on rehabilitation technology development. His work has contributed to significant advancements in assistive devices for individuals with balance disorders and spinal cord injuries.

Joakim GustafsonView profile

Professor

• Speech Technology
• Human-Robot Interaction
• Multimodal Systems
• +3 more

Joakim Gustafson is a Professor of Speech Technology and Head of the Department at the Division of Speech, Music and Hearing, KTH Royal Institute of Technology. His research focuses on multimodal systems, conversational speech synthesis, social robotics, and interactional analysis of spontaneous spoken dialogue. He leads VR-funded projects such as CONNECTED (context-aware speech synthesis for conversational AI) and co-leads projects like STANCE (speaker stance perception) and CAPTivating (public speaking analysis with TTS). He is also involved in Digital Futures-funded AAIS (social robots for elderly assistance) and WASP-funded PerCorSo (robot behavior in crowded environments). Education: Defended his PhD thesis at KTH in 2002 under supervision of Björn Granström, with faculty opponent Julia Hirschberg from Columbia University. Prior roles include Senior Researcher at Telia Research (2000-2007), leading projects like NICE (speech-enabled computer game for children) and TänkOm (animated agent Pixie). Research interests span speech synthesis realism, robot interaction design, and accessibility technologies. Projects aim to improve AAC devices, dementia detection via multimodal analysis, and sustainable cooking interfaces. He actively contributes to editorial boards of journals including Speech Communication and International Journal of Human-Computer Studies , and served as Technical Program co-Chair for Interspeech 2017. His lab, the Intelligence Augmentation Lab, supports data collection for projects like Food Talk. Grants & Collaborations: VR, RJ, WASP, Vinnova, and EU funding across multiple projects. Co-PI in international initiatives like BabyRobot (robot learning for children) and EACare (dementia early detection). Labs/Teams: Intelligence Augmentation Lab (IA-Lab) at KTH, collaborating with RPL (Robot Perception & Language Lab) and ISCA (International Speech Communication Association).

Alex LewView profile

Assistant Professor

• Probabilistic Reasoning
• Programming Languages
• Machine Learning
• +4 more

Alex Lew is an Assistant Professor of Computer Science at Yale University, affiliated with the Yale School of Engineering & Applied Science. He is a core member of the GenLM consortium, a multi-university initiative focused on controlling and understanding large language models through probabilistic programming and Bayesian inference. His research emphasizes automating and scaling principled probabilistic reasoning, combining techniques from programming languages, machine learning, Bayesian statistics, and cognitive science. Alex holds a Ph.D. and S.M. from the Massachusetts Institute of Technology (MIT) and a B.S. from Yale University. His work bridges theoretical foundations and practical applications, particularly in probabilistic programming languages and their integration with modern machine learning systems. Key contributions include advances in programmable variational inference, denotational semantics for probabilistic programs, and scalable Bayesian data cleaning. His research has been recognized with prestigious awards, including the ACM SIGPLAN Distinguished Paper Award (POPL 2023), ACM SIGLOG Distinguished Paper Award (LICS 2023), and the Probability and Programming Research Award from Meta (2023). His publications span top venues like ICLR, PLDI, and POPL, focusing on topics such as sequential Monte Carlo, stochastic inference algorithms, and formal semantics. Alex collaborates with industry and academia through the GenLM consortium and contributes to open-source probabilistic programming systems like Gen. His lab focuses on advancing the theoretical and practical tools for probabilistic reasoning in complex systems.

• Control Systems
• Model Predictive Control (MPC)
• System Identification
• +6 more

Prof. Roy Smith is a Professor at ETH Zurich specializing in control systems, with a focus on model predictive control (MPC), system identification, and robust control. His research spans energy systems, aerospace applications, and data-driven methods. He collaborates with leading institutions like the Swiss Federal Institute of Technology (ETH Zurich) and has co-authored numerous high-impact papers in top journals like IEEE Transactions on Automatic Control and Automatica. Key research interests include: distributed control of interconnected systems, kernel-based system identification, and real-time optimization for energy networks. His work bridges theoretical advancements with practical applications in buildings, renewable energy, and aerospace. Recent publications highlight contributions to MPC design under uncertainty, closed-loop identification techniques, and stochastic control systems. He has pioneered methods for energy hub networks and developed tools for data-driven predictive control in smart buildings. Academic collaborations include projects with Prof. John Lygeros (ETH Zurich) and Prof. Florian Dörfler (ETH Zurich), focusing on multi-agent coordination and energy systems. His research also extends to aerospace applications such as autonomous kite power systems and spacecraft pose estimation.

• Automatic and Systems Modeling
• Estimation and Analysis of Systems
• Robust and constrained control of complex systems
• +8 more

Marwane REZZOUKI is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des signaux et systèmes (L2S). His research focuses on systems modeling, control engineering, telecommunications, and optimization. Key research areas include robust control of complex systems, intelligent physical layers for communication systems, and energy-related applications. He is part of multiple research groups at L2S, including MODESTY (Modeling & Estimation), COMEDY (Dynamic Systems Control), SYCOMORE (Robust Control), IPHYCOM (Telecom), ILOCOS (Optimization), and MULTINET (Multimedia Networks). He is based at the CentraleSupélec campus in Gif-sur-Yvette, France. His work intersects transverse axes such as energy and industry innovation, reflecting a commitment to applied research in engineering and information sciences. Labs/Teams: Active member of L2S's core teams and transverse projects, contributing to both theoretical and applied research in systems and communications.

Wangkun XuView profile

Research Fellow

• Machine Learning
• Optimization
• Power Systems
• +3 more

Dr. Wangkun Xu is a Research Fellow in the Control and Power Research Group at Imperial College London's Department of Electrical and Electronic Engineering (Faculty of Engineering). He leads research on machine learning and optimization methodologies for robust power system operations within the UK-EPICS project (funded by EPSRC and The Leverhulme Trust). His work focuses on integrating data-driven techniques with physical system constraints to enhance grid resilience under high renewable penetration. Education: PhD in Electrical Engineering (2024): Imperial College London, supervised by Dr. Fei Teng and Dr. Imad M. Jaimoukha M.Sc. Control Systems (2019): Imperial College London B.Eng. Electrical & Electronic Engineering (2018): University of Liverpool/Xi'an Jiaotong Liverpool University Research Interests: Machine learning optimization, system dynamics, renewable energy integration, cybersecurity in power grids, and decision-making frameworks for energy systems. His work bridges data-driven methods with physical system constraints to address challenges like grid stability, adversarial attacks, and decentralized market design. Awards: Recipient of EEE Department PhD Scholarship (2019) William Rathbone Medal (2018) David Inglis Prize (2017) Associate Fellow of HEA (AFHEA) (2025) Advising & Grants: Supervised by leading academics in control systems and power engineering. His research is funded by UK-EPICS and EPSRC. Engages in pedagogical innovation as an AFHEA-certified educator. Labs & Affiliations: Active in the Control and Power Research Group, CIGRE UK Universities Hub, and IEEE Power & Energy Society. Collaborates with industry partners like MediaTek (2023–2024 deep learning internship).