جست‌وجوی استاد و پژوهشگر

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +۶ مورد دیگر

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.

• Power Amplifiers
• Signal Processing
• Radar Systems
• +۷ مورد دیگر

Souheil Ben Smida is an Associate Professor at the School of Engineering & Physical Sciences at Heriot-Watt University, affiliated with the Institute of Sensors, Signals & Systems. His research focuses on power amplifiers, signal processing, radar systems, and biomedical applications, with a particular emphasis on integrating neural networks and wireless communication technologies. His work spans areas such as nonlinear systems, 5G technology, and healthcare innovation, leveraging radar and machine learning for non-invasive monitoring. Recent contributions include dual-band radar systems for vital signs detection, low-complexity neural network-based channel estimation, and predistortion techniques for power amplifiers. Key research trends include biomedical applications using radar and neural networks, optimization of communication systems for 5G, and hardware implementations on FPGA platforms. His publications reflect a balance between theoretical advancements and practical applications in electronics, healthcare, and telecommunications. Dr. Ben Smida's expertise contributes to the UN Sustainable Development Goals, particularly in advancing health technologies and sustainable industrial innovation.

Özlem Ak Gümüşمشاهده پروفایل

دانشیار

• Mathematical Biology
• Nonlinear Differential Equations
• Stability Theory
• +۴ مورد دیگر

Özlem Ak Gümüş is an Associate Professor in the Department of Mathematics at Adıyaman University's Faculty of Arts and Sciences. Her research focuses on linear/nonlinear differential equations, mathematical biology, and stability theory. She earned her PhD from Selçuk University (2011) with honors. Research domains include: Population dynamics and Allee effects Bifurcation analysis in biological systems Epidemiological modeling Fractional calculus applications Her publications demonstrate consistent focus on stability analysis and chaos control in biological systems, with recent emphasis on discrete-time models and harvesting effects. Awards include multiple TÜBİTAK Publication Incentive Awards (2012, 2015, 2023) and top-ranking achievements in undergraduate/graduate programs. She has supervised 5 MSc students and serves on editorial boards including the International Journal of Systems Science and Applied Mathematics.

• Transportation Planning
• Traffic Engineering
• Public Transport Integration
• +۹ مورد دیگر

Assoc. Prof. Dr. Murat Ergün currently serves at Istanbul Technical University's Faculty of Civil Engineering, Department of Civil Engineering, specializing in transportation planning, traffic engineering, and sustainable infrastructure. He holds a PhD in Transportation Engineering and has contributed extensively to urban mobility, pavement engineering, and risk assessment in transportation systems. PhD (1993): Transportation Engineering, Istanbul Technical University MSc (1985): Transportation Engineering (Mixed), Istanbul Technical University BSc (1981): Civil Engineering, Uludağ University His research focuses on GIS integration for spatial transport analysis, centroid optimization in planning, and sustainable asphalt materials. Recent projects include risk assessment for urban roadside assets and dynamic creep analysis of polymer-modified asphalt. Collaborations with İ. Adalıoğlu dominate his 2025 publications. Key projects span from 2005 BAP initiatives to 2025 studies on autonomous vehicles in public transport. Though no specific scientific awards are listed, his Scopus h-index of 8 reflects significant scholarly impact.

Lassi Paunonenمشاهده پروفایل

دانشیار

• Functional Analysis
• Operator Theory
• Infinite-Dimensional Linear Systems
• +۳ مورد دیگر

Lassi Paunonen is an Associate Professor in Mathematics at the Faculty of Information Technology and Communication Sciences (ITC), Tampere University. He leads the Systems Theory Research Group, focusing on functional analysis, operator theory, and robust control of infinite-dimensional systems. His work addresses challenges in stability analysis of semigroups, output regulation, and control design for distributed parameter systems. Research Interests: Functional analysis, operator theory, infinite-dimensional linear systems, robust control, stability of strongly continuous semigroups, and mathematical systems theory. Key Contributions: Recent publications explore admissibility in infinite-dimensional systems, boundary control of heat equations, and model reduction techniques for neural ODEs. His research emphasizes theoretical foundations with applications in engineering and physics. Lassi Paunonen collaborates actively through his research group, maintaining professional presence on Systems Theory Research Group and personal website .

• Linear Statistical Models
• Covariance Analysis
• Biostatistics
• +۳ مورد دیگر

Jarkko Isotalo is a University Lecturer in the Department of Computing Sciences at Tampere University, Finland. His affiliation is within the Faculty of Information Technology and Communication Sciences, focusing on theoretical and applied statistical research. His research interests span linear statistical models , covariance analysis , biostatistics , and computational statistics . Notably, he explores topics like the Best Linear Unbiased Estimator (BLUE), mixed models, and their applications in clinical trials and ecological studies. Recent work includes studies on permissible covariance structures in linear models, treatment outcomes for gambling disorder (using randomized controlled trials), and partitioning of forest CO2 emissions. His articles often emphasize theoretical foundations while addressing practical implications in health and environmental sciences. Isotalo has contributed to methodological advancements in statistical modeling without explicitly mentioned awards or grants. His research often involves interdisciplinary collaborations, bridging mathematics, computing, and applied sciences.

• Control Systems
• Mechatronics Design
• Signal Processing
• +۴ مورد دیگر

Mohammad Narimani is a Senior Lecturer in the Department of Mechatronic Systems Engineering at Simon Fraser University’s Faculty of Applied Sciences. He holds a PhD in Mechatronics Engineering from King's College London (2011), a MASc in Electrical Engineering from Isfahan University of Technology (2001), and a B.Sc. in Electrical Engineering from Sharif & Isfahan University of Technology (1997). His teaching focuses on core mechatronic disciplines, including Control Theories, Mechatronic Design, Signal Processing, and Digital Logic Circuits. Dr. Narimani’s research spans multiple domains: Biomedical Engineering : Developing machine learning models for healthcare applications like blood pressure estimation and spinal injury detection. Control Systems : Stability analysis in fuzzy logic-based systems and nonlinear control theory. Renewable Energy : Investigating fuel cell dynamics during oxygen starvation to improve efficiency and safety. Machine Learning : Multimodal sensing for activity recognition and physical monitoring systems. Recent work emphasizes practical applications such as wearable sensor integration for health monitoring and smart home systems. His articles reflect a trend toward combining traditional engineering principles with modern AI-driven methodologies. Advising and grants: No formal advisees listed, but his courses (e.g., MSE 381 Feedback Control Systems, MSE 250 Electric Circuits) suggest involvement in student mentorship. No specific grants mentioned in available texts. Lab affiliations: Active within the Faculty of Applied Sciences’ research ecosystem, though specific lab names are not detailed in the provided materials.

Edwin Burmeisterمشاهده پروفایل

استاد پژوهشی

• Economic Theory
• History of Economic Thought
• Arbitrage Pricing Theory (APT)
• +۳ مورد دیگر

Edwin Burmeister is a Research Professor Emeritus of Economics at Duke University's Trinity College of Arts & Sciences. His career includes roles as Research Professor (1990–2007) and Visiting Scholar (1988, 1990). He holds a Ph.D. in Economics from MIT (1965), an M.A. from Cornell University (1962), and a B.S. from Cornell (1961). His research focuses on economic theory, the history of economic thought, and financial econometrics, particularly the Arbitrage Pricing Theory (APT). He has explored topics like multi-sector models, capital theory, and global economic stability. His work bridges theoretical frameworks with empirical analysis, addressing issues such as price expectations and market efficiency. Recent publications (2008–2016) analyze Sraffa-Samuelson marginalism, Hicks’s capital theory, and APT models. Earlier works (1972–1996) delve into multi-sector economic systems and nonlinear econometric techniques. While no awards are listed, his contributions span academic and applied economics. No advising or grant information is provided. His work is rooted in foundational economic theories, with applications to modern financial markets and dynamic systems.

• Public Transportation Electrification
• Electric Vehicle Drive Control
• Battery Degradation Modeling
• +۴ مورد دیگر

Dr. Syed M Nawazish Ali is a Research Fellow at RMIT University’s School of Engineering (City Campus, Australia) since July 2022. He has held academic roles at Macquarie University (Casual Academic, 2018–2020) and Aalborg University (Visiting Research Fellow, 2019–2020). His industry experience includes roles at Evenergi Pty Ltd and Magnetica Limited in Australia. Education: B.Sc. in Electrical Engineering, University of Engineering & Technology, Lahore (2013) M.Sc. in Electrical Engineering, COMSATS University, Lahore (2016) Ph.D. in Electrical Engineering, Macquarie University, Sydney (2022) Research Interests: Dr. Ali specializes in data analysis of public transportation electrification, electric vehicle (EV) drive control, battery degradation studies, optimization algorithms for battery health estimation, and grid impact of EV penetration. His work addresses thermal degradation in EV motor drives, fault-tolerant inverter designs, and sustainable integration of renewable energy with transport systems. Article Trends: His recent publications focus on harmonizing EV charging with smart grids, optimizing energy control via Machine Learning and genetic algorithms, and enhancing fault tolerance in power electronics. Key themes include thermal management of motor drives, multi-energy system integration, and decentralized energy trading models. Scientific Awards: Postgraduate Research Fund grant and DVC Research Commendation, Macquarie University (2020) 1st position in oral research presentation at Macquarie University’s Higher Degree Research Conference (2019) International Research Training Program Scholarship (Commonwealth Government-funded) for Ph.D. (2017) Prime Minister’s Laptop Scheme award (2016) Top of FA14-MSEE batch at COMSATS University (2016) KAKEZAI Scholarship at UET Lahore (2011–2013) Supervision & Grants: Available for Masters/PhD supervision in EV control systems. Secured grants including the Commonwealth Government’s International Research Training Program Scholarship and Macquarie University’s Postgraduate Research Fund. Key projects: C4NET Project 66 (Zero Emission Bus Trial) and EV battery degradation control research.

Jing Fuمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Telecommunications
• +۶ مورد دیگر

Dr. Jing Fu is a Lecturer at the School of Engineering, RMIT University in Australia. Her research focuses on applying optimization algorithms and machine learning to telecommunications, wireless networks, and edge computing. She specializes in restless bandit models for dynamic resource allocation, multi-agent coordination, and energy-efficient systems design. Key areas include satellite communications, radar systems, and distributed computing architectures. Her work bridges theoretical operations research with practical applications in 5G/6G networks, UAV communication platforms, and smart sensor networks. She has published extensively on topics like beam scheduling, edge computing offloading strategies, and adaptive wireless protocols. Current supervision interests span neural networks for sensor systems, IoT resource allocation, and next-generation mega satellite networks. Research Themes: AI-driven network optimization, distributed radar systems, energy-efficient edge computing Key Projects: Reinforcement learning-based IoT resource allocation 6G wireless communication with AI integration Optimal beam scheduling for phased array radars Collaborations: Active in multi-disciplinary projects involving telecommunications, aerospace engineering, and computer science Dr. Fu supervises research projects on neural networks for telecommunications, adaptive wireless techniques, and satellite formation flying. She is based at RMIT's City Campus and open to guiding postgraduate research in her areas of expertise.

Meghdad Fazeliمشاهده پروفایل

مدرس ارشد

• Renewable Energy Systems
• Smart Grids
• Microgrids/Nanogrids
• +۵ مورد دیگر

Dr. Meghdad Fazeli is a Senior Lecturer in the Department of Electronic and Electrical Engineering at Swansea University, Faculty of Science and Engineering. His research focuses on renewable energy integration, smart grids, and microgrid technologies, with a particular emphasis on virtual synchronous machines (VSM) and energy management strategies. He leads projects addressing climate change mitigation through decarbonized energy systems, aligning with UN Sustainable Development Goals 7, 11, and 13. Dr. Fazeli collaborates with National Grid ESO and colleagues across disciplines including Computer Science and Business School via the CARI initiative. He founded Innoverters-Ltd, a Swansea spin-out consultancy for future power systems innovation. His teaching uses blended learning, including MATLAB-SIMULINK integration and recorded lectures, coordinating modules like Power Systems (EG-342) and Advanced Power Systems (EGLM05). Research interests span renewable energy control, grid-forming inverters, and energy communities. Recent articles address VSM applications, peer-to-peer trading, and buffered microgrid designs. He advises PhD projects on topics like solar forecasting and grid-less power system architectures. His work emphasizes sustainable energy transitions, long-term resilience, and interdisciplinary solutions for net-zero systems.

Donald J. Chmielewskiمشاهده پروفایل

استاد

• Control Systems Engineering
• Energy Systems Design
• Fuel Cell Technology
• +۳ مورد دیگر

Donald J. Chmielewski is a Professor of Chemical Engineering at Illinois Institute of Technology (IIT), affiliated with the Chemical and Biological Engineering Department within the Armour College of Engineering. He holds a Ph.D. in Chemical Engineering from UCLA (2000), an M.S. in Electrical Engineering from UCLA (1993), and a B.S. in Electrical Engineering from IIT (1991). His research focuses on advanced process control strategies for energy systems, including fuel cells, hybrid vehicles, and power generation infrastructure. Chmielewski has received notable awards such as the CAST Division Plenary Lecture (2010) and Excellence in Teaching Award (2003). His expertise spans control system economics, energy process modeling, and system optimization. Key application areas include IGCC power plants, building HVAC systems, and electric power storage. Recent publications emphasize economic model predictive control for energy systems and novel fuel cell designs. Chmielewski advises on energy systems and has collaborated on projects involving oxy-combustion power cycles and PEM fuel cell dynamics. His work bridges theoretical control methodologies with practical engineering challenges in sustainable energy. Professional contributions include plenary lectures and peer-reviewed articles on process systems engineering and energy storage solutions.

Setareh Maghsudiمشاهده پروفایل

استاد

• Machine Learning
• Wireless Communications
• Edge Computing
• +۷ مورد دیگر

Dr. Setareh Maghsudi is a Professor in the Learning Technical Systems group at the Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. She joined Ruhr-University Bochum in August 2023 after serving as an Assistant Professor at the University of Tübingen (2020-2023) and at the Technical University of Berlin (2017-2020). Her academic journey began with an M.Sc. from Kiel University (2008-2010), followed by her Ph.D. and postdoctoral work at Technical University of Berlin (2011-2015), Yale University (2016-2017), University of Manitoba (2015-2016), and Kyushu University (2019). Dr. Maghsudi's research focuses on the application of machine learning to communication networks and distributed systems, with particular emphasis on bandit algorithms, federated learning, and resource allocation in dynamic environments. Her work bridges theoretical machine learning with practical networking challenges, developing algorithms that can adapt to non-stationary environments with partial information. She has made significant contributions to multi-armed bandit frameworks for wireless communications, edge computing, and network optimization. Her recent publications (2023-2025) demonstrate a strong trend toward addressing challenges in integrated sensing and communication (ISAC), federated learning for edge networks, and non-stationary decision-making problems. The publications show expertise spanning theoretical machine learning foundations, wireless communications engineering, and practical implementation for real-world networked systems. Her work increasingly incorporates causal reasoning and robustness considerations into learning frameworks for communication systems. Dr. Maghsudi leads the Learning Technical Systems research group at Ruhr-University Bochum, where she supervises PhD students and postdoctoral researchers working at the intersection of machine learning and communication systems. Her research is supported by various grants focusing on AI for future communication networks. Current projects include developing AI-driven solutions for next-generation communication systems with emphasis on robustness, efficiency, and adaptability in dynamic environments.

David Beggمشاهده پروفایل

مدرس ارشد

• Structural Engineering
• Concrete Technology
• Building Information Modeling (BIM)
• +۵ مورد دیگر

Dr David Begg is a Senior Lecturer at the University of Portsmouth, affiliated with the Faculty of Technology and School of Civil Engineering and Surveying. He contributes to the Portsmouth Centre for Advanced Materials and Manufacturing and serves as a PhD supervisor. Academic Rank: Senior Lecturer University: University of Portsmouth School: Faculty of Technology Department: School of Civil Engineering and Surveying His research focuses on structural engineering, concrete technology, and smart systems. Key areas include: Structural Modeling, Monitoring, and Design Steel Fiber Reinforced Concrete Building Information Modeling (BIM) Optimization Seismic Analysis and Rehabilitation Intelligent Structural Systems Research trends show consistent contributions to concrete material properties, BIM integration, and seismic vulnerability assessment. Scientific awards are not explicitly mentioned in available data. He accepts PhD students but notes no current funding opportunities.

Yuhan Dingمشاهده پروفایل

عضو هیئت علمی

• Applied Mathematics
• Data Science
• Numerical Analysis
• +۳ مورد دیگر

Yuhan Ding is an Associate Teaching Professor of Applied Mathematics and Program Director of the Master of Data Science program at Illinois Institute of Technology's College of Computing. Their research focuses on numerical analysis, adaptive algorithms, and computational methods. Ding has contributed to the development of adaptive approximation techniques and integration libraries like GAIL, emphasizing guaranteed error control and algorithmic efficiency. Education details are not explicitly provided in the text, but their work aligns with advanced computational mathematics and data science education. Research interests include multivariate approximation, algorithm design, and mathematical modeling. Advising and grant details are not specified here. The GAIL library demonstrates expertise in creating robust computational tools for scientific applications.