جستجوی استاد راهنمای دانشگاهی

• Automatic Control Systems
• Energy Loss Minimisation in Electric Motors
• Fuzzy Logic Control
• +۳ مورد دیگر

Eleftheria Sergaki is a permanent Laboratory Teaching Staff member (EDIP) at the Technical University of Crete , School of Electronic and Computer Engineering and the Electrical Power Systems Laboratory . She has served the university continuously since 1985 in roles ranging from Contract Lecturer-Researcher to her current academic post. Education PhD in Electronic & Computer Engineering, Technical University of Crete, 2011 MSc in Electronic & Computer Engineering, Technical University of Crete, 1997 BSc in Physics, Aristotle University of Thessaloniki, 1985 Research Interests Her research lies at the intersection of automatic control, energy efficiency and machine learning . Core themes include the design of fuzzy-logic-based control algorithms that minimise energy losses in electric motors—work that has yielded an individual 2008 patent—the application of machine-learning techniques for predictive modelling of physical processes, and the development of AI-assisted diagnostic algorithms for medical imaging. Across these areas she leverages techniques such as adaptive control, artificial neural networks, neuro-fuzzy systems, discrete wavelet transforms and convolutional neural networks, bridging power engineering, biomedical engineering and computer science. Publication Trends Her 27 peer-reviewed works (2000–2023) exhibit a clear trajectory: early contributions focused on laser instrumentation and physics education shifted after 2006 toward electric-motor efficiency optimisation and advanced motor-control algorithms , and since 2018 have expanded into deep-learning applications in medical imaging , particularly capsule endoscopy and brain-tumour detection. Scientific Awards & Distinctions Teaching Work Award (EDIP) – Senate of the Technical University of Crete, 2018 National distinction – Greek delegation leader, International Physics Teaching Conference, CERN Distinction – Panhellenic Physics Construction and Experiment Competition, National Research Foundation 2 nd eligible contractor – national educational textbook authoring programme Grants, Projects & Service Scientific Project Manager, EPEAEK Project (Greek National Research Council), 1998–2000 Evaluator, IEEE Transactions on Industrial Electronics, IEEE Transactions on Fuzzy Systems, Elsevier journals, etc. Co-author, Laser: Working Principles and Applications (Evgenidis Foundation, 2008) Laboratory & Team Affiliations She is a key member of the Electrical Power Systems Laboratory , where she co-supervises student projects and experimental work. Previously she co-founded and worked in the Laboratory of Matter Structure & Laser Physics (TUC) from 1985 to 2013, contributing to its establishment and instrumentation.

Tsokotun Pavlo Vasylevichمشاهده پروفایل

مدرس ارشد

• Heat and Mass Transfer
• Crack Propagation in Anisotropic Media
• Internal Combustion Engines
• +۳ مورد دیگر

Dr. Pavlo Tsokotun is a Senior Lecturer at the Department of Automobiles, Heat Engines and Hybrid Power Plants within the Faculty of Transport at National University Zaporizhzhia Polytechnic . With over four decades of service since 1978, his expertise spans thermal engineering, internal combustion engines (ICE), and mathematical modeling of industrial systems. Education: Dnipropetrovsk State University (1978), Diploma in Hydroaerodynamics Research Focus: Heat/mass transfer in industrial products, crack propagation in anisotropic media, ICE optimization, and thermovacuum processes. Key Contributions: Developed mathematical models for transformer drying processes and ICE performance enhancement, with recent work on gas-dynamic functions and shock wave analysis. Scientific Works: 10+ publications including Scopus-indexed articles and conference proceedings on energy systems, mechanical reliability, and thermal engineering. Honors: Multiple ZNTU accolades for professional excellence and a Zaporizhia City Council Certificate of Honor.

• Mechanics of Deformable Solids
• Mathematical Modeling
• Economics
• +۳ مورد دیگر

Tetyana Ihorivna Levitska is an Associate Professor in the Department of Applied Mathematics at Zaporizhzhia Polytechnic National University. With over 25 years of academic experience since 1999, she specializes in mechanics of deformable solids and mathematical modeling in economics. Her expertise spans both theoretical and applied mathematics with significant contributions to engineering applications. Her educational background includes: Bachelor's and Master's in Applied Mathematics from Zaporizhia State University (1994) Candidate of Technical Sciences (PhD equivalent) in Mechanics of Deformable Solids (2000) Professor Levitska's research focuses on the mechanics of deformable solids, particularly stress-strain analysis of shells and composite structures, and mathematical modeling in economics. Her work bridges theoretical mathematics with practical engineering applications, especially in aerospace engineering and economic systems modeling. She has developed innovative approaches to fuzzy logic applications in economic modeling and has contributed significantly to understanding complex shell structures under various loading conditions. Her recent publications demonstrate a strong trend toward interdisciplinary research, combining mechanical engineering principles with computational mathematics. The majority of her work centers on stress-strain analysis of aircraft components, composite materials, and shells of rotation, while also exploring applications of fuzzy logic in economic modeling and migration phenomena. Professor Levitska teaches Higher Mathematics, Probability Theory and Mathematical Statistics, Discrete Mathematics, and Higher and Applied Mathematics to undergraduate and graduate students. Her teaching approach integrates theoretical foundations with practical applications, preparing students for both academic and industry careers.

• Applied Mathematics
• Continuum Mechanics
• Integro-Differential Equations
• +۷ مورد دیگر

Ганна Анатоліївна Шишканова is an Associate Professor at the Department of Applied Mathematics within the Faculty of Computer Science and Technologies at Zaporizhzhia Polytechnic National University. With over two decades of academic experience since 1999, she holds the academic title of Associate Professor and a Candidate of Physical and Mathematical Sciences degree. Her expertise spans applied mathematics, continuum mechanics, and mathematical modeling, with significant contributions to contact mechanics and econometrics. Zaporizhzhia State University (Applied Mathematics, Specialist) Zaporizhia National Technical University (Entrepreneurship, Master's degree, 2019) Dr. Shyshkanova's research focuses on solving complex mathematical problems in engineering and economics. Her work in spatial contact problems with unknown contact regions has practical applications in structural engineering and green building design. She has developed innovative approaches to multidimensional integro-differential equations and applied catastrophe theory to various scientific domains. Her recent research has expanded into econometrics, applying mathematical models to business optimization and economic forecasting. Her 77 scientific publications demonstrate consistent scholarly output across multiple disciplines, with recent work addressing sustainable construction, cylindrical structure deformation, and precision alloy production. The publications reveal a trend toward interdisciplinary research that bridges applied mathematics with practical engineering and economic challenges. Certificate of Merit on the 110th anniversary of ZNTU (2010) Certificate of Merit on the 115th anniversary from Zaporizhzhia City Council (2015) Honorary Certificate for long-term work and contribution to technical education (2021) Appreciation from NU "Zaporizhzhia Polytechnic" for 50th anniversary (2023) Certificate from Zaporizhzhia Regional State Administration (2023) Dr. Shyshkanova teaches Higher Mathematics, Probability Theory and Mathematical Statistics, Optimization Methods and Models, and Econometrics. Her pedagogical approach integrates traditional instruction with innovative distance learning technologies to enhance student engagement with mathematical concepts. She has contributed to educational methodology through research on student evaluation systems and the application of mixed learning approaches to mathematical disciplines.

Siddharthaمشاهده پروفایل

استادیار

• Energy Engineering
• Electrical Engineering
• Energy Conservation
• +۴ مورد دیگر

Siddhartha is an active Assistant Professor - Senior Scale in the Department of Electrical and Electronics Engineering at the Manipal Institute of Technology, a constituent unit of Manipal Academy of Higher Education in Manipal, Karnataka, India. His research focuses on sustainable energy systems with emphasis on electric vehicle infrastructure and building energy efficiency. His educational qualifications include: Bachelor of Engineering (BE) Master of Technology (MTech) Research interests span: Energy Conservation in Residential and Educational Buildings Electric Vehicle Charging Infrastructure Optimization Weather Impact on Energy Consumption Thermal Comfort Modeling Genetic Algorithm Applications Urban Energy Planning His work bridges computational optimization with practical energy challenges in Indian urban contexts, particularly Guwahati city. Analysis of his 12 research outputs (2016-2024) reveals increasing focus on transportation electrification since 2023, while maintaining core expertise in building energy systems. Recent publications demonstrate sophisticated application of non-dominated sorting genetic algorithms to charging station placement, building on earlier foundational work in residential energy conservation. No scientific awards or honors were documented in the provided information. Details regarding graduate student supervision, research grants, or collaborative funding mechanisms were not specified in the source material. The profile does not reference any dedicated research laboratories, centers, or structured research teams associated with his academic activities.

Tong Liuمشاهده پروفایل

مدرس

• Machine Learning
• Data Science
• Lifelong Learning
• +۱۵ مورد دیگر

Dr. Tong Liu is a Lecturer in Pervasive Data Science at the Department of Computer Science, School of Computer Science, University of Sheffield, UK. Previously, he served as a Research Associate at the Sargent Centre for Process Systems Engineering at both Imperial College London (2022-2023) and the University of Sheffield (2021-2022), and as a visiting researcher at the University of Southampton (2018-2019). His academic journey reflects a strong foundation in control theory and data science with applications across multiple domains. Dr. Liu obtained his B.S. degree in Automation and Ph.D. degree in Control Theory and Engineering in 2016 and 2021, respectively, from Chongqing University, China. During his doctoral studies, he also conducted research at the School of Electronics and Computer Science, University of Southampton (2018-2019). Dr. Liu's research focuses at the intersection of machine learning, data science, and decision-making. His primary expertise lies in advancing sustainable and lifelong AI methodologies for pervasive data analytics to support intelligent decision-making. His work spans multiple application domains including industrial automation, precision agriculture, and healthcare informatics. Specific research areas include lifelong learning, online learning, transfer learning, time series and streaming data analytics, adaptive modeling of dynamic processes, and safe learning and optimization. His research has practical implications for developing more efficient, sustainable, and intelligent systems across various industries. Dr. Liu's recent publications demonstrate a strong focus on lifelong learning frameworks, adaptive modeling for industrial processes, and applications in precision agriculture and healthcare. His work shows a progression from foundational algorithm development to practical implementations in real-world systems. A notable trend is the integration of domain knowledge with machine learning techniques to create more efficient and reliable systems. His research increasingly emphasizes sustainability and safety considerations in AI applications, reflecting broader trends in responsible AI development. Dr. Liu serves as Course Director for MSc Advanced Computer Science at the University of Sheffield. He is actively involved in research funding, including as Co-PI on the KG-PPN project: 'Knowledge Guided Multimodal AI for Automatic Quantification and Infestation Estimate of Plant-Parasitic Nematodes,' funded by Innovate UK (06/2025 - 02/2026, £54,388). His research group, the Pervasive Computing Group, focuses on developing AI solutions for real-world challenges across multiple domains. Dr. Liu is affiliated with the Pervasive Computing Group at the University of Sheffield's School of Computer Science. His research collaborations span multiple institutions including Imperial College London, University of Southampton, and various industry partners including Shell. His work often involves interdisciplinary teams bringing together expertise in machine learning, control systems, and domain-specific knowledge from agriculture, healthcare, and industrial engineering.

• Marine Structures
• Naval Architecture
• Ship Energy Efficiency
• +۱۰ مورد دیگر

Zhiyuan Li is a Senior Researcher at Chalmers University of Technology in the Department of Shipping and Marine Technology, affiliated with the Mechanics and Maritime Sciences school. His work focuses on marine structural integrity and energy efficiency, particularly for ships operating in Arctic and winter navigation conditions. He integrates environmental load modeling (waves, wind, ice) with hydrodynamic and structural analyses to address reliability and uncertainty in maritime systems. PhD in Naval Architecture (2013) BEng in Mechanical Engineering MSc in Automotive Engineering His research spans marine structural integrity, energy-efficient ship propulsion, voyage planning, and Arctic shipping strategies. Recent projects include modeling ice-induced loads, optimizing high-strength steel for electric ships, and co-simulation frameworks for wave energy converters. Publications emphasize decarbonization, fatigue analysis, and environmental load quantification. Zhiyuan collaborates on grants from the Swedish Transport Administration, European Commission, and Swedish Energy Agency. He contributes to projects like DeWaTra (decarbonizing waterborne transport), POWERSHIP (SMR-powered ships), and SEASNAKE+ (marine cable monitoring). No scientific awards or student advisories are explicitly documented.

Michael J. O'Sheaمشاهده پروفایل

استاد

• Condensed Matter Physics
• Magnetic Materials
• Financial Time Series Analysis
• +۳ مورد دیگر

Michael J. O'Shea is a Professor of Physics at Kansas State University , with a research career spanning experimental condensed matter physics, financial time series analysis, and physics education. His work on magnetic materials includes studies of rare-earth alloys in thin film and nanoparticle forms, while his financial research explores quasi-periodic signals in stock market indices through novel folding techniques. Research areas include: Experimental condensed matter physics (Nd2Fe14B and SmCo5 magnets, neutron irradiated superconductors) Financial signal analysis (DJIA periodicity studies) Outdoor sports modeling (climbing, snowboarding, canoeing) Teaching portfolio spans conceptual physics to graduate-level statistical mechanics. Current service roles include Chair of the Graduate Entrance Committee and Departmental Graduate Advisor. Recent publications focus on: Human frame scaling models for backpacking Periodic components in financial indices Canoe river-crossing dynamics Climber force analysis Collaborations and grants include NSF, Army Research Office, and NATO funding. Graduate advisees include multiple PhD candidates working on magnetic materials and physics education topics.

• Energy Network Optimization
• Mathematical Programming
• Time Series Forecasting
• +۳ مورد دیگر

Milena Petkovic is a Researcher at the Applied Algorithmic Intelligence Methods department of the Zuse Institute Berlin (ZIB) . Her work focuses on energy network optimization, mathematical programming, and spatio-temporal forecasting in complex systems. Education: Not explicitly mentioned Current affiliation: ZIB, Germany Her research interests include: Mathematical modeling of energy networks Hybrid forecasting approaches combining machine learning and optimization Real-world applications in natural gas transmission systems Analyzing regime changes in gas nomination time series Recent publications highlight trends in: High-dimensional time series prediction for energy systems Constraint-based optimization under supply-demand balance Deep learning applications for gas flow dynamics Hybrid methods integrating FAR, LSTM, and mathematical programming

• Smart Energy Markets
• Congestion Management
• Artificial Intelligence in Energy Systems
• +۲ مورد دیگر

Prof.dr.ir. J.A. la Poutré is a professor at the Faculty of Electrical Engineering, Mathematics and Computer Science, specializing in intelligent electrical power grids. His work bridges computer science, energy economics, and power systems engineering. Research Interests : Smart energy market design and fair pricing mechanisms Congestion management in power distribution networks AI-driven optimization of energy systems Demand response and deferrable load coordination Research Outputs : His publications demonstrate expertise in applying multiagent systems, hybrid congestion models, and real-time forecasting to energy systems. Recent work focuses on balancing fairness and welfare in grid management while enabling carbon-free energy transitions through AI. Supervision : He has supervised 5 academic works, though specific student names are not detailed in the provided text.

• Energy Systems
• Machine Learning
• Power Electronics
• +۴ مورد دیگر

Hussain Syed Kazmi is an Assistant Professor at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) and the Faculty of Engineering Science. He serves as head of the Subdivisie EnergyVille Electa - Kazmi and is a member of EnergyVille and KIES (KU Leuven Institute for Energy and Society). His work focuses on energy systems innovation through data science and machine learning. Assistant Professor, Department of Electrical Engineering (ESAT) Head of Subdivisie EnergyVille Electa - Kazmi Member of EnergyVille and KIES Institute His research spans energy systems, machine learning, and building dynamics, with projects addressing electric vehicle smart charging, renewable energy forecasting, grid stability analysis, and climate-resilient building design. Publications emphasize interpretable AI, physics-informed modeling, and energy flexibility quantification. Recent projects include: Physics-infoRmEd: Data-driven control for building energy demand Esurrogates: Climate resilience and energy efficiency co-optimization FlexIQ: User flexibility in distribution networks Kazmi teaches courses such as Capita Selecta Data Science for Energy and Smart Distribution Systems, integrating his research into energy-focused academic training.

Joseph Konstanمشاهده پروفایل

استاد

• Human-Computer Interaction
• Recommender Systems
• Social Computing
• +۶ مورد دیگر

Joseph A. Konstan is a Distinguished McKnight University Professor and Distinguished University Teaching Professor in the Department of Computer Science and Engineering at the University of Minnesota , where he also serves as Associate Dean for Research of the College of Science and Engineering. His work spans human-computer interaction , recommender systems , and social computing , with significant focus on improving personalization algorithms and ethical AI applications. Education : Ph.D. in Computer Science from University of California, Berkeley (1993) M.S. in Computer Science, UC Berkeley (1990) A.B. in Computer Science, Harvard College (1987) Dr. Konstan's research addresses human-centric AI challenges, including collaborative filtering , online community dynamics , and health technology interventions . He leads the GroupLens Lab , known for developing foundational recommender systems frameworks like MovieLens. His work on organizational communication and health apps demonstrates practical applications of behavioral interventions. Recent publications analyze algorithmic fairness , user inaction patterns , and human-AI collaboration in media recommendation. He chairs the ACM Publications Board and has led major NSF grants for human-centered news recommenders . His 130+ publications include influential works on user modeling , social media ethics , and interactive learning systems . ACM Software Systems Award for GroupLens Fellow, ACM/AAAS/IEEE CHI Academy member As an advisor, Dr. Konstan has mentored 16 Ph.D. graduates including Ruoyan Kong (2023) and Max Harper (2019). He leads NSF-funded research on recommendation ethics and organizational communication . The GroupLens Research Group under Konstan explores recommender system infrastructure , user experience optimization , and health technology for School of Nursing and HealthPartners collaborations.

• Smart Grids
• Energy Consumption Modeling
• Urban Mobility
• +۴ مورد دیگر

Cruz Enrique Borges Hernandez is a Professor at the Faculty of Engineering, University of Deusto, specializing in computational approaches to energy systems and urban sustainability. His research integrates data science, agent-based modeling, and behavioral analysis to address critical challenges in smart grids, energy consumption patterns, and urban mobility. His primary research interests include: Smart grid optimization and electricity demand forecasting Urban mobility modeling and 15-minute city concepts Behavioral interventions for energy efficiency in buildings Cross-generational climate change perception studies Blockchain applications for peer-to-peer energy markets His recent publications demonstrate strong trends in applying machine learning to smart meter data analysis, developing agent-based simulations for urban systems, and examining socio-technical aspects of energy transitions. Key methodological contributions include cascading models for behavioral nudges and novel approaches to electricity consumption typology derivation. As an active researcher in EU-funded projects, he bridges technical energy modeling with social science perspectives, creating policy-relevant outputs that address both engineering challenges and human behavior aspects of sustainability transitions. His work emphasizes practical implementation through platforms like GreenSoul for building energy monitoring and GTPlat for transport planning simulations.

William Currieمشاهده پروفایل

استاد

• Sustainability
• Environmental Science
• Ecosystem Ecology
• +۵ مورد دیگر

William Currie is a Professor at the University of Michigan with dual appointments in the School for Environment and Sustainability and the College of Literature, Science, and the Arts. His research centers on ecosystem ecology in wetland and Great Lakes environments, addressing nutrient cycling dynamics, invasion processes, and anthropogenic impacts including urbanization and climate change. His primary research interests encompass wetland biogeochemistry, nutrient cycling under environmental stressors, invasion ecology of species like Typha in coastal systems, and the integration of machine learning for environmental prediction. Currie's work bridges experimental field studies, ecosystem modeling, and socio-ecological frameworks to address sustainability challenges in the Great Lakes region and continental US water systems. Analysis of his 2020-2025 publications reveals a significant shift toward computational approaches, particularly machine learning applications for water quality prediction and Great Lakes forecasting, while maintaining core experimental research on wetland nutrient dynamics. His scholarship increasingly addresses cross-cutting issues including urban stream pollution, blue carbon valuation, and the ecological consequences of infrastructure development. Currie directs the Currie Lab Group, which focuses on sustainability science through ecosystem modeling, field experimentation, and stakeholder engagement to advance understanding of environmental change in freshwater coastal systems.

Francesco Lombardiمشاهده پروفایل

استادیار

• Energy System Modelling
• Computational Optimization
• Stochastic Simulation
• +۴ مورد دیگر

Francesco Lombardi is an Assistant Professor at the Delft University of Technology (TU Delft) within the School of Technology, Policy and Management , Energy and Industry department. He specializes in computational methods for energy system design, emphasizing social justice and technical robustness. PhD in Energy Engineering from Politecnico di Milano Visiting researcher at ETH Zurich and KU Leuven His research focuses on Modelling to Generate Alternatives (MGA) methods, including the SPORES algorithm , integrated into the open-source energy framework Calliope . He leads the development of RAMP software , a stochastic model for energy demand profiles in data-scarce scenarios like remote areas and future electric-vehicle fleets. Current projects include advancing MGA methods for policy-relevant energy questions and contributing to the PowerWeb Institute , Urban Energy Institute , and Open Energy Modelling Initiative . His work spans urban-to-national scales, addressing multi-energy systems and decarbonization strategies. Notable publications include studies in Joule and Applied Energy on renewable deployment, fossil fuel elimination, and smart charging. Collaborations involve industrial and academic partners, with software like RAMP co-funded by institutions such as Reiner Lemoine Institut and University of Liège.