جست‌وجوی استاد و پژوهشگر

Bo Yuanمشاهده پروفایل

استاد

• Cybersecurity
• Malware Analysis
• Computational Intelligence
• +۳ مورد دیگر

Bo Yuan is a Professor in the Department of Cybersecurity at Rochester Institute of Technology (RIT), affiliated with the Golisano College of Computing and Information Sciences. He served as Department Chair from 2014 to 2022, leading significant growth in cybersecurity programs. His research focuses on computational intelligence applied to cybersecurity, including malware analysis, machine learning, and covert channel detection. Education: Ph.D. in Systems Science (1996, Binghamton University), MS and BS in Mathematics (Shanghai Normal University, China). Research Interests: Cybersecurity, malware detection, computational intelligence, and machine learning applications in security. He has secured over $23M in grants, including NSF CyberCorps® SFS (2015-2024, 2019-2024, 2024-2029) and DoD Cyber Scholarship Programs. His work includes developing covert channel detection methods, improving malware analysis via machine learning, and advancing cybersecurity education through game-based learning. Grants/Projects: Multiple NSF and DoD grants totaling millions, focusing on cybersecurity workforce development and research. He leads RIT’s CAE-CD and CAE-R initiatives. His research has been applied in competitions like the Northeast Collegiate Cyber Defense Competition (NECCDC). Teaching includes courses on cybersecurity analytics, machine learning, and authentication models. He advises students on topics like adversarial machine learning and network security.

• Mechanical Engineering
• Automotive Engineering
• Thermal Engineering
• +۳ مورد دیگر

Kun Liang is an Associate Professor in Mechanical Engineering at the University of Sussex , specifically within the School of Engineering and Informatics and Department of Engineering and Design . He has held significant roles such as Harry Kroto Research Fellow at KTH Royal Institute of Technology (2017) and Visiting Fellow at the University of Oxford (since 2016). PhD in Mechanical Engineering (2014, University of Oxford) M.Sc. in Automotive Engineering (Tongji University) B.Eng. in Automotive Engineering (Jiaotong University) Research Interests focus on low-carbon heating, cooling, and power generation , with expertise in: Combustion and cryogenic/refrigeration systems Linear compressors and Stirling engines Thermal management for electronics and industrial applications Alternative fuel characterization and emission analysis Microchannel cooling with nanofluids His publication trends highlight advancements in: Linear compressor efficiency for cryogenic and heat pump systems Thermal management using spray cooling and microstructures Combustion dynamics of biofuels and blended fuels Modeling of heat transfer under vibrational and sub-atmospheric conditions Scientific Awards & Certifications : Harry Kroto Research Fellow (2017, KTH Royal Institute of Technology) Visiting Fellow (University of Oxford, since 2016) Chartered Engineer (CEng, Institution of Mechanical Engineers) Fellow of the Higher Education Academy Teaching includes postgraduate courses on Automotive Systems and Control Engineering , as well as undergraduate instruction in Applied Technology . He supervises MSc projects and Year 3 Individual Projects . Grants supported projects such as: Modeling novel moving magnet linear compressors for CHP applications (British Council, 2019) High-efficiency linear compressors for cryogenic systems (University of Sussex, 2018) Linear electromagnetic actuator-driven camless valve trains (University of Sussex, 2017-2018) Ammonia-based linear compressors for refrigeration (University of Sussex, 2017)

• Energy network resilience and reliability
• Climate change adaptation and mitigation in energy systems
• Intelligent control of distributed energy resources (DER)
• +۹ مورد دیگر

Dr. Spyros Skarvelis-Kazakos is an Associate Professor in Electrical Engineering at the University of Sussex , leading the Critical Infrastructure Resilience Network (CIReN) and managing the Power Systems and Smart Grid sections of the Energy Dynamics Laboratory. He has held academic positions at the University of Sussex (since 2015) and previously at the University of Greenwich (2012–2015). His research focuses on energy network resilience, distributed energy resources (DER) control, multiple energy carriers, and artificial intelligence in smart grids.

Mihai V. MICEAمشاهده پروفایل

استاد

• Intelligent Robotic Environments
• Energy Management and Optimization
• Embedded and Real-Time Systems
• +۵ مورد دیگر

Mihai V. Micea is a Professor and Head of the Department of Computer and Information Technology at the Politehnica University of Timisoara. He also serves as Director of the CCCTI Research Center and Coordinator of the DSPLabs. With a B.Sc., M.Sc., and Ph.D. (Cum Laude) from Politehnica University of Timisoara, he has been a faculty member since 1996. His research focuses on intelligent robotic environments, energy management, embedded systems, and real-time hardware/software systems. He has supervised 4 completed PhD theses and currently oversees multiple doctoral candidates. Micea has authored over 125 publications, 3 patents, and managed over 50 R&D projects worth €1.88M. His awards include the 'Eminent Young Researcher of Timisoara' (2006) and the IEEE Outstanding Reviewer Distinction (2017). Education: B.Sc. in Computer Engineering (Politehnica University of Timisoara, 1995) M.Sc. in Computer Engineering (Politehnica University of Timisoara, 1996) Ph.D. in Computer Engineering (Politehnica University of Timisoara, 2005) Habilitation Degree (2015) Research Interests: His work spans intelligent robotic systems, energy optimization, signal processing, and real-time embedded systems. Key areas include cyber-physical systems, sensor networks, and secure IoT communication protocols. His recent projects include RoNaQCI (Quantum Communication Infrastructure) and TEEFIOS (Time-Efficient Framework for Smart Devices). Grants & Projects: As Principal Investigator/Manager, he has led 29 projects with total funding exceeding €1.88M. Notable projects include CloudPUTing (High-Performance Cloud Platform) and MELISSEVS (Robotic-Sensor Collaboration Models). Awards & Recognition: 2006: Eminent Young Researcher of Timisoara (ANCS) 2017: IEEE T-IM Outstanding Reviewer Labs & Teams: Founder and Coordinator of DSPLabs (Digital Signal Processing Laboratories), focusing on real-time systems and smart sensing. Active in IEEE SSIT Romanian Chapter (2013–2022) and international conference organization (e.g., IEEE ROSE 2014).

• Formal Methods
• Parallel and Distributed Computing
• Symbolic Computation
• +۳ مورد دیگر

Wolfgang Schreiner is an A.Univ.-Prof. (Associate University Professor) at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz, Austria. His research focuses on formal methods, parallel and distributed computing, and symbolic computation. He leads projects in automated reasoning, programming language semantics, and educational software tools. His work emphasizes practical applications of formal methods, including the development of tools like RISCAL for model checking and the RISC ProgramExplorer for program reasoning. He collaborates on interdisciplinary projects, such as analyzing queueing systems using probabilistic model checking and designing grid computing frameworks for medical applications. Key research interests include semantics-based language design, theorem proving, and educational technology. He has authored textbooks like *Concrete Abstractions* and *Thinking Programs*, emphasizing foundational concepts in computer science. His contributions span theoretical advancements and practical implementations, with notable work in distributed systems, formal verification, and computational logic.

• Machine Learning
• Natural Language Processing
• Social Network Analysis
• +۳ مورد دیگر

Dr. Ahmad Zareie is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Natural Language Processing (NLP) research group. His work focuses on machine learning, social network analysis, and optimization algorithms, particularly in the context of information diffusion, influence maximization, and network dynamics. His research interests include developing advanced methods for analyzing complex social networks, identifying influential users, and mitigating misinformation spread. He has contributed to areas such as temporal link prediction, fuzzy influence maximization, and rumor control strategies. His work often integrates machine learning techniques with network science to address real-world challenges in online social networks. Dr. Zareie’s publications highlight a trend toward behavior-aware network analysis, leveraging optimization algorithms and fuzzy logic to enhance network intervention strategies. His recent work emphasizes ethical considerations in network fairness and diversity of information exposure. While no awards or grants are explicitly listed, his research demonstrates significant contributions to foundational and applied aspects of social network analysis. He is part of the NLP research group, collaborating on projects at the intersection of language processing and network dynamics.

• Machine Learning
• Healthcare Analytics
• Agricultural Technology
• +۳ مورد دیگر

Dr. Yu Zhang is a Researcher at the School of Computer Science, University of Sheffield, affiliated with the Pervasive Computing research group. His work focuses on advanced machine learning techniques applied to healthcare and agricultural domains. He specializes in multi-task learning, spatio-temporal data analysis, and tensor methods. Key research areas include Alzheimer’s disease progression prediction using neuroimaging data and precision fertilization management through sustainable farming systems. His contributions bridge computational methods with real-world applications in healthcare analytics and environmental sustainability. Dr. Zhang’s research integrates temporal relation graphs, adaptive control systems, and interpretable AI frameworks to address complex challenges in disease modeling and agricultural optimization. He explores innovations like continuous incremental learning and randomized feature learning to enhance predictive accuracy and scalability. His work emphasizes practical applications, such as the ParallelFarm system for carbon-neutral farming and frameworks for medical decision-making based on MRI data analysis. His research trends show a strong focus on multi-task learning frameworks that adapt to temporal dynamics, with applications spanning healthcare biomarker identification, resource-efficient farming, and robust algorithm design. Collaborations likely involve interdisciplinary teams in computer science, neuroscience, and environmental science. Future directions may include expanding these methods to other healthcare domains and environmental sustainability initiatives. No scientific awards are explicitly mentioned in the provided text. Advising and grant details are not listed here, but his affiliations suggest involvement in collaborative research projects within the School of Computer Science. His work is rooted in the Pervasive Computing group, emphasizing embedded systems and AI-driven solutions for real-world problems.

Ali Bahadırمشاهده پروفایل

مدرس

• Embedded Systems
• Artificial Neural Networks
• Adaptive Control
• +۶ مورد دیگر

Ali Bahadır is a Lecturer at the Faculty of Electrical and Electronics Engineering (EEE) at Istanbul Technical University (ITU). He holds a PhD from Konya Technical University (2022), an MSc from Niğde University (2001), and a BSc from Gazi University (1993). His primary role involves teaching core engineering courses such as 'Introduction to Scientific and Engineering Computing (C)', 'Electrical Circuits Lab', and 'Engineering Ethics'. Research interests span embedded systems, electric vehicle technologies (including brushless DC motor design and control), adaptive control systems, and power electronics. He has published work on electric vehicle motor systems, fuzzy logic control, and electromagnetic crane design in mining. Teaching Responsibilities: Recent courses include EHB 110E, EHB 221E, EHB 311E, and BLG 212E (2017–2021). Focus areas: Microprocessor systems, electronics labs, and industrial electronics. Professional Contributions: Active in curriculum development for ITU's EEE program. Contributed to interdisciplinary projects like the NJIT-ITU dual degree program.

Mesut Kartalمشاهده پروفایل

استاد

• Microwave Circuits and Systems
• Radar Systems
• Synthetic Aperture Radar (SAR)
• +۳ مورد دیگر

Prof. Dr. Mesut Kartal is a Professor at Istanbul Technical University's Department of Electronics and Communication Engineering within the Faculty of Electrical and Electronics Engineering. His expertise lies in Microwave Circuits and Systems, Radar Systems, and Synthetic Aperture Radar (SAR) Imaging. He holds a BSc (1990), MSc (1993), and PhD (2000) from ITU. Prof. Kartal has contributed significantly to research on SAR data simulation, radar signal processing, and microwave imaging technologies, collaborating on projects like the ITU SAGRES satellite ground station. His work spans theoretical advancements and practical implementations in radar systems, remote sensing, and signal processing algorithms. Education: BSc in Electronics and Communication Engineering, ITU (1990) MSc in Electronics and Communication Engineering, ITU (1993) PhD in Electronics and Communication Engineering, ITU (2000) Research Interests: Kartal focuses on microwave circuit design, SAR system engineering, radar imaging techniques, and signal processing for remote sensing applications. His recent work includes developing advanced SAR algorithms, microwave biosensors, and anti-jamming methods for 5G networks. Key Contributions: Co-developed SAR raw-data simulation tools and algorithms for real-time SAR imagery. Contributed to radar systems capable of detecting and tracking moving targets (GMTI). Explored applications of microwave imaging in biomedical and environmental monitoring. Labs/Teams: He is affiliated with the ITU VLSI Measurement Laboratories and leads research groups in radar signal processing and microwave engineering. His team collaborates on projects involving SAR data analysis, antenna design, and electromagnetic compatibility solutions.

Shady Gadoueمشاهده پروفایل

دانشیار

• Power Electronics
• Control Systems
• Electrification Solutions
• +۳ مورد دیگر

Dr. Shady Gadoue is an Associate Professor in the School of Electronic Engineering and Computer Science at Queen Mary University of London. His roles include research leadership in the Centre for Electronics and Centre for Sustainable Engineering. He holds a BSc, MSc, and PhD from Alexandria University and Newcastle University, respectively, and is a Fellow of the Higher Education Academy (FHEA). Dr. Gadoue's research focuses on Power Electronic Converters, Control Systems, and Electrification Solutions for Low Carbon applications. Key areas include Smart Grids, Electric Transportation, and Digital Twins. He has over 23 years of academic and industrial experience, collaborating with Jaguar Land Rover, National Grid, and others. His work has resulted in over 80 IEEE publications, recognized by Stanford University as among the top 2% scientists globally in Energy Engineering (2020–2021). His recent grants include a £49,923 Innovate UK project (2022–2023) for training in power electronics. He teaches modules on electric powertrains and power systems analysis, emphasizing renewable integration and HVDC. Dr. Gadoue’s awards include IEEE’s 'high quality research' designation and top 2% global rankings. His research addresses critical challenges in energy systems, transportation electrification, and sustainable engineering.

• Control Systems
• IoT and Smart Grids
• Renewable Energy Systems
• +۳ مورد دیگر

Seyed Amir Alavi is a Teaching Fellow at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on control systems, IoT integration in energy grids, and fractional-order control methodologies. He specializes in microgrid design, distributed control strategies, and state estimation techniques for smart energy systems. His work addresses challenges in renewable energy integration, delay compensation in power systems, and battery health monitoring for electric vehicles. Key research areas include: IoT-based distributed control systems for microgrids Advanced control algorithms for DC and AC power systems Quantum computing testbed infrastructure development Application of machine learning in battery diagnostics Resilient communication networks for energy systems Recent publications highlight innovations in: Privacy-preserving IoT data collection methods Event-triggered control for distributed systems Forecast-based consensus control using deep learning No academic awards or grants are explicitly mentioned in the provided materials. His work contributes to both theoretical advancements and practical implementations in smart grid technologies and energy management systems.

Nicole Celestineمشاهده پروفایل

مدرس

• Organizational Behavior
• Cognitive Psychology
• Knowledge Transfer
• +۵ مورد دیگر

Nicole Celestine is a Dr Casual Teaching (Lecturer) in Multi-Discipline at The University of Western Australia. Her research focuses on organizational behavior, cognitive psychology, and workplace dynamics with particular emphasis on knowledge transfer, intrinsic motivation, and ethical attitudes. She holds a PhD from her 2021 unpublished doctoral thesis titled Let's play: An examination of play-at-work through the lens of energy management . Expertise: UN Sustainable Development Goals contributing to workplace innovation and ethical practices Key research areas: Play-at-work mechanisms, neurocognitive creativity frameworks, and configurational analysis of ethical attitudes Her work bridges cognitive science and organizational management, with notable contributions to understanding attentional mechanisms in creativity and energy management strategies in workplaces. Recent publications (2016–2024) emphasize cross-disciplinary approaches to workplace motivation, gamification principles, and knowledge transfer dynamics across cultures. Over 158 citations and a h-index of 5 reflect growing academic impact.

Valentin Robuمشاهده پروفایل

استاد

• Artificial Intelligence
• Decentralized Energy Systems
• Multi-Agent Systems
• +۴ مورد دیگر

Valentin Robu is a Full Professor of Artificial Intelligence for Decentralized Energy Systems at Eindhoven University of Technology (TU/e). He is a Senior Researcher at CWI (National Research Institute for Mathematics and Computer Science, Amsterdam) and holds a visiting appointment at Princeton University's ECE department. His core expertise lies in multi-agent systems and distributed AI, with a focus on applying AI to energy challenges like smart grids, electric vehicle coordination, and renewable integration. He has authored over 150 peer-reviewed publications and won awards such as the 2019 UK Innovation of the Year Award and the 2018 Low Carbon Transport Award. Education and Career: Robu earned his PhD from TU Eindhoven and CWI (2009). Before his current roles, he was an Associate Professor at Heriot-Watt University (UK), a Senior Research Fellow at the University of Southampton, and held visiting roles at Harvard and MIT. He collaborates globally with institutions like MIT, Harvard, and TU Delft. Research Interests: His work addresses challenges in decentralized energy systems, including AI-driven grid optimization, game-theoretic models for renewable integration, and blockchain applications in energy trading. He focuses on ensuring grid stability, fair resource sharing, and sustainable energy access. Awards: In addition to his Innovation and Low Carbon Transport awards, he has been recognized as a 'Best Reviewer' in 2018. His research impacts UN Sustainable Development Goals related to affordable energy and climate action. Grants and Projects: He leads projects like CESI (UK Energy Systems Integration), CEDRI (India Demand Reduction), and ORCA (Offshore Robotics Hub). His work is featured in media outlets like the BBC, Economist, and World Economic Forum. Labs and Teams: He contributes to TU/e's EAISI (Eindhoven AI Systems Institute) and CWI's Intelligent and Autonomous Systems Group. Collaborations span academia and industry, including Scottish Power Energy Networks and Microsoft Research.

• Medical Oncology
• Hematology
• Critical Care Medicine
• +۳ مورد دیگر

Dr. Minh Nguyen is a Clinical Professor of Medicine with a focus on oncology and critical care. He currently serves on the Pharmacy and Therapeutics Committee at Hoag Hospital. His research emphasizes chemotherapy side effect management, drug efficacy in hematological disorders, and critical care pharmacology. Dr. Nguyen earned his MD from Wayne State University School of Medicine, followed by residency and a Medical Oncology Fellowship at William Beaumont Hospital. He has been recognized for academic excellence, including Clinical Honors in Neurology and Pediatrics during medical school. His research interests include prevention of chemotherapy-induced alopecia via scalp hypothermia, tumor lysis syndrome treatment efficacy, and thrombin inhibitor use in critical care settings. His work bridges clinical practice with technological advancements, such as automated mobility assessment systems and image retrieval algorithms. Awards: Clinical Honors in Neurology (2000–2001) National Golden Key Honor Society Member (1996) Class Valedictorian (1993) Dr. Nguyen’s contributions span clinical oncology and biomedical engineering, with notable work in wearable sensor technology and disaster response data analysis.

Joris Degrooteمشاهده پروفایل

استاد

• Fluid-Structure Interaction
• Computational Fluid Dynamics
• Nonlinear Dynamical Systems
• +۵ مورد دیگر

Joris Degroote is a Full Professor at Ghent University's Faculty of Engineering and Architecture, where he leads research in computational mechanics and multiphysics simulation. He obtained his MSc (2006) and PhD (2010) in Electromechanical Engineering from Ghent University, with research stays at Massachusetts Institute of Technology (2007–2008) and Technische Universität München (2011). His research focuses on fluid-structure interaction (FSI) , computational fluid dynamics, surrogate modeling, and machine learning applications in engineering systems. Key areas include optimization under uncertainty, reduced-order modeling, and development of efficient numerical coupling techniques for complex multiphysics problems such as renewable energy systems, biomedical flows, and industrial processes. Recent publications demonstrate strong emphasis on high-fidelity simulations for aerospace applications (airborne wind energy, drone aerodynamics), biomedical engineering (aortic hemodynamics, cerebrospinal fluid dynamics), and advanced manufacturing (textile mechanics, tribology). Awards & Recognition: Post-doctoral Fellowship, Research Foundation Flanders (FWO) Affiliations: Core lab member of Flanders Make; serves on scientific and editorial boards of multiple journals and conferences. Leads the Sustainable Thermo-Fluid Energy Systems (STFES) and EEDT-MP research units.