جست‌وجوی استاد و پژوهشگر

Sean MacAvaneyمشاهده پروفایل

مدرس

• Machine Learning
• Information Retrieval
• Neural Networks
• +۵ مورد دیگر

Dr. Sean MacAvaney is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. His research focuses on advancing information retrieval techniques, particularly in neural models, sparse/dense retrieval architectures, and system optimization. He actively contributes to open-source tools like PyTerrier and ir_datasets, emphasizing reproducibility in research. Education: Not explicitly detailed in the text, but his academic contributions imply advanced qualifications in computer science or related fields. Research Interests: MacAvaney's work spans neural information retrieval, adversarial model analysis, query expansion strategies, and large-scale system efficiency. He explores topics like instruction-following models, multilingual benchmarking, and the integration of machine learning with traditional retrieval methods. Article Trends: Recent work emphasizes practical system optimizations (e.g., in-memory indexes), cross-lingual evaluation frameworks, and bridging gaps between human and machine relevance judgments. He also investigates challenges in productionizing neural models and maintaining test collection relevance over time. Advising & Grants: Supervises students including Andreas Chari (deep learning for language tools) and Andrew Parry (uncertainty modeling in neural networks). His grants likely support projects in scalable retrieval systems and reproducible research practices. Labs/Teams: Engaged with the Glasgow Information Retrieval (GIR) group and collaborates internationally on initiatives like the Information Retrieval Experiment Platform (IREP).

• Programming Language Theory
• Functional Programming
• Logic Programming
• +۳ مورد دیگر

Tom Schrijvers is a Professor at the Department of Computer Science, Faculty of Engineering Science, KU Leuven. His research focuses on programming language theory, functional programming, logic programming, and computational effects. Advancing multi-stage programming with computational effects (2024–2028) Generating Educational Feedback with Program Analysis (2024–2028) Programino: An Educational Programming Platform (2023–2027) eTeacher: Interactive web-based programming education platform (2023–2025) AmPERSand: Programming Education Runtime System (2023–2026) His recent publications emphasize bidirectional transformations, effect handlers, and programming education tools. He collaborates extensively with British institutions like Imperial College London and works on the book Language Engineering in Haskell with Dr. Nicolas Wu.

• Power Electronics
• Renewable Energy Systems
• Electrical Facilities
• +۳ مورد دیگر

Dr. Yunusemre Yağan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Dumlupınar University, Kutahya. He holds a PhD in Electrical-Electronics Engineering from the same institution (2015–2023), preceded by a Master's (2013–2015) and Bachelor's (2008–2013) in Electrical Engineering. His academic career includes a Research Assistant role since 2013 and current teaching responsibilities for the APPLE I undergraduate course. Research interests focus on Power Electronics , Renewable Energy Systems , and Electrical Facilities , with expertise in neural networks applied to power distribution and inverter control. His work addresses challenges in fault detection, MPPT optimization for PV systems, and grid-connected inverter design. Publications (8 total) span topics like neural network-based fault location, PV system modeling, and automotive electrical test devices. No awards or patents are listed, though ongoing academic duties include course management and lab resources development for the APPLE I module, including assignments and laboratory handouts. No former staff status or retirement indicated; active academic role since 2023.

• Power Electronics
• Electric Vehicles
• Renewable Energy Systems
• +۳ مورد دیگر

Euzeli Dos Santos is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, Indianapolis. His expertise spans power electronics, electric vehicle technologies, and advanced energy systems. He holds a courtesy appointment in the same department and leads research initiatives focused on capacitorless resonant converters, high-torque electric machines, and nanotechnology-enhanced power devices. Research interests include optimizing power converter topologies, developing efficient wireless charging solutions for electric vehicles, and integrating graphene-based materials into semiconductor devices. His work emphasizes practical applications in renewable energy systems, microgrids, and grid stability. Dos Santos has contributed extensively to multilevel converter design, control strategies for power electronics, and transformerless power supply architectures. Publications highlight innovations in capacitorless DC-DC converters, HIL simulation frameworks for distributed energy resources, and thermal optimization of IGBTs using nanomaterials. His research bridges theoretical advancements with real-world challenges in energy conversion and transportation electrification.

Anna Scaglioneمشاهده پروفایل

استاد

• Statistical Signal Processing
• Smart Grids
• Network Science
• +۱۲ مورد دیگر

Anna Scaglione is a Professor in the Department of Electrical and Computer Engineering at Cornell University's College of Engineering, based at Cornell Tech. She rejoined the faculty in September 2021 after holding professorial positions at Arizona State University, UC Davis, and earlier at Cornell (2001–2008). She earned her M.Sc. in 1995 and Ph.D. in 1999 from Cornell University. Research Interests: Her work centers on statistical signal processing with applications in communication networks, electric power systems, intelligent infrastructure, and network science. Key areas include graph signal processing, federated learning, differential privacy, reinforcement learning for energy systems, and cyber-physical security in smart grids. She integrates machine learning, optimization, and control theory to address challenges in modern power systems and IoT. The 15 most recent publications (2021–2025) reveal a dominant trend in privacy-preserving and AI-driven solutions for power systems. Her research emphasizes federated learning , graph neural networks , differential privacy , and reinforcement learning applied to grid stability, cybersecurity, and distributed energy management. There is a strong focus on graph signal processing for power grid modeling and anomaly detection, as well as synthetic data generation and secure transactive energy platforms . IEEE Fellow (2011) IEEE Signal Processing Transactions Best Paper Award (2000) IEEE Donald G. Fink Prize Paper Award (2013) IEEE Signal Processing Society Young Author Best Paper Award (2013, with Lin Li) IEEE Smart Grid Communications Technical Committee Technical Achievement Award (2020) IEEE SPS Distinguished Lecturer (2019–2020) Dr. Scaglione has advised students including Lin Li, whose work earned a best paper award. She has secured significant research grants related to smart grid security, privacy, and optimization. Her editorial leadership includes Editor-in-Chief of IEEE Signal Processing Letters (2012–2013) and Deputy Editor-in-Chief of IEEE Transactions on Control of Networked Systems . She has served on IEEE technical committees, steering committees, and as General or Technical Chair for major conferences such as SPAWC, SmartGridComm, and GlobalSIP. She leads research in smart grid signal processing , secure distributed energy systems , and privacy-aware machine learning for infrastructure . Her team develops frameworks like Grid-GSP (Graph Signal Processing for power grids), SoDa (synthetic solar data), and CIGAR (cybersecurity via inverter reconfiguration). She is involved in blockchain-based transactive energy platforms and resilient simulation environments for critical infrastructure.

Duncan Callawayمشاهده پروفایل

دانشیار

• Power Systems
• Energy Efficiency
• Renewable Energy Integration
• +۳ مورد دیگر

Duncan Callaway is an Associate Professor of Energy and Resources at UC Berkeley with an affiliate appointment in Electrical Engineering and Computer Science. He also serves as the Chair of the Energy and Resources Group (ERG) and is a Faculty Scientist at Lawrence Berkeley National Laboratory. His work focuses on emerging energy technologies, grid operations, and data-driven solutions for energy systems. Callaway holds a Ph.D. in Theoretical & Applied Mechanics from Cornell University and has industry experience with Davis Energy Group and PowerLight Corporation. His research emphasizes quantifying impacts of new technologies on power systems, developing control systems for grid integration, and advancing energy efficiency strategies. Key areas include renewable energy integration, smart grid technologies, and environmental justice in energy policy. Callaway teaches courses on energy systems and data science applications in energy. Education: Ph.D., Theoretical & Applied Mechanics, Cornell University, 2001 B.S., Mechanical Engineering, University of Rochester, 1995 Callaway's recent work explores peer-to-peer energy markets, grid stability in inverter-dominated systems, and equity implications of electrification policies. He received the NSF CAREER Award (2015) and has published extensively on machine learning for grid optimization, wildfire risk mitigation, and residential energy access. His lab develops open-source simulation tools (e.g., PowerSimulations.jl) and collaborates on hardware-in-loop testing for grid technologies. Current projects address climate-aware grid planning, decentralized energy storage, and the socio-technical challenges of energy transitions.

Surya Teja Kandukuriمشاهده پروفایل

پژوهشگر

• Model based fault diagnosis
• Prognostic System Health Management
• System Identification
• +۳ مورد دیگر

Surya Teja Kandukuri is a Researcher at the Department of Engineering Sciences at the University of Agder, Norway. His work focuses on fault diagnosis, prognostic health management, and control systems for renewable energy applications, particularly wind and hydroelectric power systems. Education: PhD in Mechatronics, University of Agder (2014-2018) MSc in Systems and Control, Delft University of Technology, Netherlands (2003-2006) B.Tech in Electrical and Electronics Engineering, Nagarjuna University, India (1999-2003) Research Interests: Dr. Kandukuri specializes in model-based fault diagnosis and prognostic system health management for complex engineering systems. His research integrates system identification, estimation, and control theory with advanced machine learning techniques to develop predictive maintenance solutions for renewable energy infrastructure. He has particular expertise in wind turbine systems, where he has developed innovative approaches for monitoring pitch systems, detecting electrical faults in induction motors, and assessing performance degradation over time. His work extends to hydroelectric power plants through the PHMHydro project, where he applies similar health monitoring principles to water turbine systems. The integration of physics-based models with deep learning frameworks represents a key innovation in his research approach, enabling more accurate and timely fault detection in critical infrastructure. Publications Trends: Dr. Kandukuri's publication record demonstrates a consistent focus on health monitoring systems for renewable energy infrastructure. His recent work (2022-2025) shows increasing integration of deep learning techniques with traditional signal processing methods for fault diagnosis. There's a clear progression from wind turbine systems to broader applications in hydroelectric power, reflecting expanding research scope. His collaborations span multiple countries and institutions, particularly with Norwegian and international research partners. Research Groups: Intelligent Mechatronics (iTron) Intelligent Monitoring Projects: Performance and Health Monitoring for Hydroelectric Powerplants (PHMHydro)

Thomas Spragueمشاهده پروفایل

استادیار

• Neuroscience
• Cognitive Neuroscience
• Visual Cognition
• +۳ مورد دیگر

Thomas Sprague is an Assistant Professor in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara (UCSB), and a member of the UCSB Neuroscience program. He holds a BA in Cognitive Sciences from Rice University (2010) and a PhD in Neurosciences from UC San Diego (2016). His research focuses on understanding how neural systems encode and manipulate information to support goal-directed behavior, leveraging computational neuroimaging (fMRI, EEG) and model-based analysis techniques. Key research interests include visual cognition, working memory, attention, and the neural mechanisms underlying prioritization in dynamic behavioral contexts. His lab, the Perception, Cognition, and Action Lab (PCA Lab), investigates how task goals modulate neural representations of visual information across brain regions. He has received prestigious awards such as the Alfred P. Sloan Research Fellowship and NIH grants. Dr. Sprague’s work is supported by grants from the National Institutes of Health, Army Research Labs, and NVIDIA. He emphasizes open science practices, making data and code publicly available through his lab website and GitHub profile. His contributions include developing methods for multivariate analysis and inverted encoding models to decode neural representations of visual features and behavioral relevance.

Przemyslaw Koralewiczمشاهده پروفایل

پژوهشگر

• Power Hardware-in-the-Loop
• Small Signal Stability Analysis
• Grid Forming Inverters
• +۴ مورد دیگر

Przemyslaw Koralewicz is a senior researcher at the National Renewable Energy Laboratory (NREL) working at the Flatirons Campus. He leads the controllable grid interface (CGI) and manages power hardware-in-the-loop experiments for megawatt-scale systems like batteries, photovoltaics, wind turbines, and hydrogen systems. His work focuses on validating novel power systems technologies for industry and research partners. Research Focus: Power hardware-in-the-loop validation, grid-forming inverters, microgrid stability, real-time simulations, grid integration of renewables, and model validation. Professional Expertise: Specializes in device characterization, smart inverter modeling, and stability analysis of complex systems including microgrids. Education: Master's in Electronics and Telecommunication from Silesian Technical University. Experience: Previously worked as a Lead Engineer and Project Manager at ABB Corporate Research Center (2009–2016). Collaboration: Active member of IEEE Power and Energy Society, IEEE Smart Grids Society, and IEEE networks.

• Robotics
• Point Cloud Registration
• Conformal Geometric Algebra
• +۴ مورد دیگر

Adam Leon Kleppe is an Associate Professor at the Norwegian University of Science and Technology (NTNU) , affiliated with the Department of ICT and Natural Sciences. He works extensively with MANULAB , focusing on robotic welding labs and Industry 4.0 labs , handling procurement, planning, installation, and maintenance. His research spans robotics, geometric algebra, point cloud processing, and automation technologies. Education : Master in Engineering Cybernetics with a specialization in robotics from NTNU (2013); Ph.D. (2018) on Point Cloud Registration for Assembly using Conformal Geometric Algebra. Research Interests revolve around robotics, including inverse kinematics, point cloud alignment, obstacle detection, and geometric algebra applications. He also explores automation in manufacturing and aquaculture, such as deformable body modeling for salmon equipment testing and machine vision for fish bin picking. Publication Trends show a consistent focus on geometric algebra-based solutions for robotic tasks, with recent work extending into biomechanical simulation and aquaculture automation. Teaching includes courses like Mechatronics and Systems Integration , Automation and Mechatronics , and Industrial Control Systems . He also contributes to outreach via program management for AI videos on platforms like YouTube and LinkedIn.

Horst Schulteمشاهده پروفایل

استاد

• Control Systems Engineering
• Electrical Engineering
• Mathematical Modeling of Dynamic Systems
• +۴ مورد دیگر

Prof. Dr.-Ing. Horst Schulte is a Professor at the Department of Engineering I, HTW Berlin - University of Applied Sciences. His expertise lies in Control Systems Engineering, Electrical Engineering, and Renewable Energy Systems, with a focus on modeling, fault-tolerant control, and computational intelligence applications. Department of Engineering I, HTW Berlin Chair in Control Systems Group ResearchGate profile with 214 publications Research Interests include: Model-based and data-driven control systems Wind and photovoltaic power plants Takagi-Sugeno fuzzy systems Robust and fault-tolerant control Dynamic virtual power plants (DVPP) Computational intelligence in energy systems Scientific Awards : 10th Annual ISGAN Award (2024) HTW Berlin Research Award (2018/19) Best Paper in Control Theory (2013) Best BMBF Project of the Month (2012) Key Contributions involve power tracking control for renewables, fault reconstruction in wind turbines, and innovative converter control schemes. His work bridges theoretical control methods with practical energy system implementations.

Olugbenga Moses Anubiمشاهده پروفایل

استادیار

• Resilient Control Systems
• Robust Control
• Adaptive Control Systems
• +۶ مورد دیگر

Olugbenga Moses Anubi is an Assistant Professor in the Department of Electrical & Computer Engineering at the FAMU-FSU College of Engineering, Florida State University. He directs the Resilient and Autonomous Systems Lab (RASLab) and contributes to the Center for Advanced Power Systems (CAPS) and the Center for Intelligent Systems, Control, and Robotics (CISCOR). Ph.D. from the University of Florida His research focuses on Resilient Control Systems for cyber-physical infrastructures, including Adaptive Control , Vehicle Dynamics , and Autonomous Systems . He also explores Real-time Optimization and Collaborative Learning in distributed environments. Recent publications highlight advancements in Cyber-Physical Systems , with emphasis on Resilient State Estimation , Energy Storage Dispatch , and Autonomous Vehicle Control . Key areas include mitigating Adversarial Attacks , Battery Degradation Modeling , and Decentralized Power Systems . He teaches courses such as Signals and Systems , Advanced Control Methods , and Model Predictive Control , alternating specialized electives annually. Labs and teams: RASLab develops self-driving platforms ( MicroNole , AutoNole ), focusing on vision-based lane detection , robust motion control , and collaborative learning in distributed systems.

Jean Mahseredjianمشاهده پروفایل

استاد

• Electrical Networks
• Electromagnetics
• Circuit Theory
• +۴ مورد دیگر

Jean Mahseredjian is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He holds the NSERC/Hydro-Québec/RTE/EDF/OPAL-RT Industrial Research Chair in Multiscale Time Simulation of Transients in Large-Scale Electrical Networks and is a member of the Institute for Data Valorization (IVADO). His research bridges electrical engineering, numerical methods, and renewable energy integration. Research interests include: Advanced numerical simulation of power system transients Modeling and validation of wind farms and HVDC systems Electromagnetic compatibility and circuit theory Real-time simulation and parallel computing for large-scale grids His recent publications (2025-2026) demonstrate strong emphases on: EMTP simulation advancements for circuit breakers, cables, and converters Renewable integration challenges (wind/PV parks and battery systems) High-performance computing methods for power system analysis Awards and honors: IEEE Fellow (2013) for contributions to power system transients simulation Supervision and collaboration: Mentored 68 graduate students (31 PhD, 37 Master's) Leads industry-funded research with Hydro-Québec, RTE, EDF, and OPAL-RT Develops open-source simulation tools (e.g., Julia-based platforms)

• Control Systems
• Hybrid Dynamical Systems
• Robotics
• +۶ مورد دیگر

Professor William Holderbaum is a faculty member at the School of Science, Engineering & Environment at the University of Salford, with additional affiliation to the Centre for Future Engineering. His academic career demonstrates sustained research productivity with 46 documented research outputs spanning from 2012 to 2025. Professor Holderbaum's research interests encompass several interconnected domains: Control Systems Theory and Applications Hybrid Dynamical Systems (particularly power converters) Robotics (Geometric Control, nonholonomic systems, Reinforcement Learning) Rehabilitation Engineering (Robust Control Design) Energy Management (Electric Vehicles, Smart Grids, Multiple Agent Systems) Autonomous Vehicles (Motion planning, AI) His recent publication activity (11 papers in 2025, 13 in 2024) reveals a strong emphasis on power systems protection challenges, particularly addressing microgrid protection issues arising from distributed generation integration. He has developed innovative approaches for overcurrent relay coordination, microgrid frequency stability, and protection schemes for inverter-dominated grids. His work also extends to robotics applications in textile manufacturing, wearable sensor technology for gesture recognition, and digital twin applications for power system protection. Professor Holderbaum maintains an active research program with significant recent output, demonstrating continued scholarly productivity and relevance in his fields of expertise. His interdisciplinary approach bridges theoretical control systems with practical engineering applications across multiple domains.

Michael Finkelمشاهده پروفایل

استاد

• High-Voltage Engineering
• Power Systems
• Smart Grids
• +۵ مورد دیگر

Prof. Dr.-Ing. Michael Finkel is a full Professor at Augsburg University of Applied Sciences in the Faculty of Electrical Engineering, where he serves as Academic Advisor for International Industrial Engineering, Member of the Faculty Council, and Chair of the Examination Board. His professional affiliations include memberships in CIGRE, IEEE, VDE, and the EMC Project Group of FNN. He received the Bavarian Certificate of Higher Education Teaching in 2009 and multiple research awards including ETG Literature Prizes and Young Author Awards. Professor Finkel's research focuses on High-Voltage Engineering, Power Systems, and Smart Grid technologies. His work addresses critical challenges in renewable energy integration, grid stability, and distributed energy resources. Key research areas include: Island grid operation and emergency power systems Smart grid digitalization and control strategies Electric vehicle integration in urban networks Power system stability and frequency control Renewable energy forecasting and grid integration His recent publications demonstrate strong focus on converter-dominated grids, renewable integration, and smart grid solutions. Research trends show increasing emphasis on real-time grid monitoring, AI applications in power systems, and hybrid energy solutions. Collaborative projects frequently involve industry partnerships and international research networks. Significant scientific recognitions include: ETG Literature Prize 2024 for heat pump load modeling Young Author Award 2025 for hydropower island grid research HSA_transfer Award 2022 for technology transfer Multiple best paper awards at international conferences Professor Finkel actively supervises doctoral candidates in power systems research and leads projects funded by industry and public grants. Current initiatives include developing emergency power solutions for critical infrastructure and advanced monitoring systems for distribution networks. His research group collaborates extensively with the Technology Transfer Centers of Augsburg University and industry partners in the energy sector.