جست‌وجوی استاد و پژوهشگر

• Electromagnetic fields
• Microwave and Antenna Technologies
• Electromagnetic Compatibility
• +۳ مورد دیگر

Prof. Dr. Osman ÇEREZCİ is currently serving as the Dean of the Faculty of Engineering and Natural Sciences (FENS) at Üskudar University, where he also acts as the Head of the Electrical and Electronics Engineering Department. With a career spanning over decades, he has previously held significant academic positions at Istanbul Technical University (ITU) Sakarya Faculty of Engineering and Sakarya University. Educational Background: Licence in Physics from Istanbul University (1976) Master's Degree in Institute of Science from Uludağ University (1985) Doctorate in Institute of Science from Uludağ University (1985) His research primarily focuses on Electromagnetic fields , Microwave and Antenna Technologies , and Electromagnetic Compatibility . He has made significant contributions to understanding Electromagnetic Pollution , Wave propagation , and the Biological effects of electromagnetic radiation . His recent publications continue to explore these areas with a particular emphasis on environmental impact and health considerations. Prof. ÇEREZCİ has received several prestigious awards, including Microwave Aperture Antennas Summer School (TÜBİTAK, 1988) Quantum Electrodynnamics and Quantum Optics Summer School (NATO-ASI, 1989) Generation & Application of High Power Microwaves Summer School (NATO-ASI, 1996) Environmental UV Radiation: Impact on Ecosystems and Human Health and Predictive Models Summer School (NATO-ASI, 2001) He has supervised numerous postgraduate theses and has contributed to the education and training of many students through undergraduate courses such as Physics I, Physics II, and graduation projects.

Sylvain Sardyمشاهده پروفایل

دانشیار

• statistical machine learning
• sparsity
• optimization
• +۶ مورد دیگر

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

Alfredo Nunez Vicencioمشاهده پروفایل

دانشیار

• Railway Engineering
• Intelligent Transportation Systems
• Computational Intelligence
• +۵ مورد دیگر

Dr. Alfredo Núñez Vicencio is an Associate Professor at Delft University of Technology's Civil Engineering & Geosciences faculty, specializing in Railway Engineering. His work bridges intelligent transportation systems with computational intelligence, focusing on railway infrastructure health monitoring through advanced sensor technologies and machine learning. Key affiliations: 4TU Built Environment, IEEE Society (Senior Member), CiTG Faculty ED&I team Research themes: Physics-informed neural networks, vibration-based diagnostics, sustainable railway electrification His research output spans over 150 publications, with recent work exploring spiking neural networks for rail defect detection and wavelet-based approaches for vibration analysis. He leads EU-funded projects like IAM4RAIL and In2Track3, combining AI with railway engineering to address critical challenges in track monitoring and maintenance optimization. Scientific recognition includes multiple best paper awards at international railway conferences and editorial roles at journals like IEEE Transactions on Intelligent Transportation Systems and Intelligent Transportation Infrastructure . He has mentored students receiving prestigious awards, including European Rail Research Advisory Council honors.

• Signal Processing
• Embedded Systems
• Machine Learning
• +۲ مورد دیگر

Dr. Ergun Erçelebi is a Professor at Gaziantep University's College of Engineering , Department of Electrical and Electronics Engineering. With expertise in Signal Processing , Embedded Systems , and Machine Learning , he has led research in power electronics, wireless localization, and biomedical applications. PhD in Electrical-Electronics Engineering (Gaziantep University, 1999) MSc in Electrical-Electronics Engineering (Gaziantep University, 1992) BSc in Electrical-Electronics Engineering (Middle East Technical University, 1990) His research spans power electronics (multilevel inverters, photovoltaic systems), signal processing (speech enhancement, wavelet transforms), and embedded systems (UAV control, RFID localization). Recent work focuses on hybrid optimization techniques and deep learning for harmonic reduction, modulation classification, and biomedical antenna design. His 2021-2024 publications demonstrate trends in machine learning applications for Turkish lira recognition, UAV-bird classification, and metamaterial design. Earlier work (2014-2020) includes energy management systems, medical imaging phantoms, and smart home automation. 2024: Stanford Most Effective Scientists recognition 15+ patents in embedded systems and medical devices He has supervised 7+ graduate student projects and led 8 research grants, including TÜBİTAK 1501 funding for wearable nebulizer design. His work includes developing smart security systems , remote energy management devices , and RFID-based indoor localization solutions.

Nurdal Watsujiمشاهده پروفایل

استادیار

• Electrical-Electronics Engineering
• Image Processing
• Signal Processing
• +۱ مورد دیگر

Nurdal Watsuji serves as Assistant Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, specializing in Electrical-Electronics Engineering with research emphases on Image Processing, Signal Processing, and Digital Design. His academic foundation includes: Doctorate in Electrical and Electronic Engineering from Imperial College London (1987-1991) Degree in Electrical-Electronic Engineering from Middle East Technical University, Gaziantep (1982-1985) Licence in Electrical Engineering from Middle East Technical University, Gaziantep (1975-1981) Watsuji's research integrates theoretical signal processing with practical engineering applications, particularly in LED/OLED lighting systems and image analysis. His work demonstrates consistent innovation in optical efficiency optimization, compressed sensing algorithms, and wavelet-based texture classification, bridging electrical engineering principles with real-world implementation challenges in energy systems and computer vision. Publication analysis from 2003-2016 reveals evolving expertise from foundational wavelet techniques to advanced optical efficiency modeling in solid-state lighting. The recurring theme involves mathematical optimization of signal/image processing methods for specific engineering constraints, with significant contributions to nonconvex compressed sensing and LED reflector design. His graduate mentoring spans eight students: PhD candidates: İpek Atık (2016) on LED reflector optimization and Taner Ince (2012) on compressed sensing Master's students: Ugur Polat (2022) on energy analyzers, Gulden Akay Ozbay (2008) on hand recognition, Taner Ince (2006) on medical image processing, Recep Oz (2004) on 3D rendering, Omer Cengiz Celebi (2003) on volume rendering, and Bayram Kara (2003) on wavelet texture analysis This diverse portfolio reflects sustained engagement with computational methods across lighting systems, biomedical imaging, and computer graphics applications.

Mahya Ghandehariمشاهده پروفایل

دانشیار

• Noncommutative Harmonic Analysis
• Harmonic Analysis on non-Abelian groups
• Fourier Analysis on Graphs
• +۴ مورد دیگر

Dr. Mahya Ghandehari is an Associate Professor in the Department of Mathematical Sciences at the University of Delaware . Her work bridges Noncommutative Harmonic Analysis , Graph Limit Theory , and Data Science , with a focus on mathematical frameworks for network analysis and signal processing on graphs. PhD in Mathematics, University of Waterloo Her research addresses challenges in seriation and graph signal processing (GSP) , particularly through graphon-based models. Recent work explores spectral methods, Fourier bases for stochastic block model graphs, and wavelet characterizations of network structures. Dr. Ghandehari’s research is supported by active NSF grants (DMS-2408008, CCF-2427965) and Simons Travel Support . She supervises PhD student Caroline McCrorey and has mentored several PhD graduates now holding academic positions at institutions like Tufts University and SUNY Oswego. Scientific honors include the Fields Postdoctoral Fellowship . She organizes seminars such as the Analysis Seminar at Delaware and the Northeastern Analysis Meeting (NEAM 2025) .

• Experimental Fluid Mechanics
• Turbulent Flow Control
• Machine Learning for Flow Control
• +۵ مورد دیگر

Gioacchino Cafiero is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS), Polytechnic University of Turin. His research focuses on data-driven experimental fluid mechanics, particularly applying machine learning techniques like deep reinforcement learning and genetic algorithms to control turbulent flows and optimize fluidic actuators. As a member of the Fluid Dynamics research group, he leads projects such as GREENER (drag reduction via sinusoidal riblets) and WINDED (drone wind investigation), while also directing commercial research on friction stress measurement methodologies. Specializes in turbulent flow control and machine learning applications Teaches PhD courses on Machine Learning for Flow Control Supervises students in aerospace engineering programs Recent publications analyze jet turbulence with explainable AI, heat transfer fluctuations in channel flows, and riblet-induced drag reduction. His work bridges aerospace engineering and fluid dynamics, contributing to SDG goals 9 (Industry Innovation) and 13 (Climate Action). Scientific awards include the Learning to Teach (L2T) Open Badge from Politecnico di Torino.

Joseph Madsenمشاهده پروفایل

استاد

• Epilepsy
• Neurosurgery
• Brain Mapping
• +۶ مورد دیگر

Joseph Madsen is a Professor of Neurosurgery at Harvard Medical School and a neurosurgeon at Boston Children's Hospital. His career uniquely bridges clinical neurosurgery with fundamental neuroscience research, focusing on improving surgical treatments for epilepsy through advanced brain mapping techniques and physiological understanding. Dr. Madsen's educational background includes: Undergraduate studies at University of Utah (1976) Medical degree from Harvard-MIT Program in Health, Science, and Technology (1981) Surgical internship at Beth Israel Deaconess Medical Center (1982) Neurosurgery residency at Massachusetts General Hospital (1989) Fellowship at Beth Israel Deaconess Medical Center/Massachusetts General Hospital (1983) His research program integrates clinical data with basic neuroscience to develop safer, more effective epilepsy surgeries. A key focus is studying human electrocorticograms recorded during behavioral cortical tasks to answer fundamental questions about cortical physiology while simultaneously improving the ability to map cortical function for surgical planning. His laboratory combines digital signal processing, wavelet analysis, and cognitive science approaches to investigate brain function and epilepsy mechanisms. He also conducts significant research on vagal nerve stimulation for epilepsy treatment, examining both efficacy and safety profiles. Analysis of Dr. Madsen's recent publications reveals an increasingly sophisticated integration of computational approaches with neurophysiological data. His work on machine learning applied to intracranial EEG has advanced the identification of biomarkers for surgical planning and outcome prediction. There's also growing emphasis on genetic aspects of epilepsy and the detailed physiological mechanisms underlying seizure generation and propagation. Dr. Madsen has provided clinical and research leadership through numerous publications and patient care innovations, though specific awards are not highlighted in the available information. As a mentor, Dr. Madsen has likely guided numerous neurosurgery residents, fellows, and graduate students through his extensive career, contributing to the training of specialists in epilepsy surgery and functional neurosurgery. His research has been supported by various grants that have enabled the development of advanced techniques for brain mapping and epilepsy treatment. Dr. Madsen leads a research laboratory that uniquely combines clinical neurosurgery with fundamental neuroscience investigation. His team utilizes advanced technologies including digital signal processing, wavelet analysis, and cutting-edge cognitive science methods to study cortical physiology in humans. The laboratory environment facilitates the direct translation of clinical observations into basic research questions and vice versa, creating a powerful feedback loop for advancing epilepsy surgery techniques.

• Taut String
• Confidence Region
• Regularization
• +۵ مورد دیگر

Arne Kovac serves as Associate Professor in Statistics within the School of Mathematics at the University of Bristol, where he completed his PhD in 1999 under B. Silverman with the thesis "Wavelet Thresholding for Unequally Time-Spaced Data". His academic profile centers on methodological innovations in nonparametric statistics. His research expertise is defined by eight core areas: Taut String algorithms (100% fingerprint prominence) Confidence Region construction (94% prominence) Regularization techniques Total Variation minimization (68% prominence) Extreme Value theory applications Statistical minimization problems Nonparametric regression frameworks Asymptotic analysis Publication trends from 2009-2014 reveal consistent advancement of smoothing methodologies, particularly through taut string extensions and graph-based regression. These works establish foundational contributions to statistical inference under shape constraints, with notable emphasis on edge preservation in signal processing and confidence band construction. No scientific awards or major honors are documented in the available records. Professionally, Kovac served on the editorial board of Annals of Statistics (2007-2009) and participates in specialized workshops including "Nonparametric statistical inference under shape constraints" (active since 2016). While student supervision and grant funding details remain unspecified, his 20 research outputs—including 14 journal articles with significant Scopus citations (60 for 2009 taut string paper)—demonstrate sustained scholarly impact.

Richard Lombardiniمشاهده پروفایل

استادیار

• Quantum Mechanics
• Computational Physics
• Theoretical Physics
• +۳ مورد دیگر

Richard Lombardini is an Assistant Professor of Physics at St. Mary's University in San Antonio, Texas, a position he has held since August 2013. Previously, he served as Assistant Professor and Chair of the Physics Department at Greenville College from 2009 to 2013, and completed a postdoctoral fellowship in computational chemistry and physics at Rice University under Bruce Johnson from 2006 to 2009. His academic credentials include: Ph.D. in Physics, Texas Tech University, 2006 B.S. in Mathematics and Chemistry, Texas Tech University, 1999 Lombardini's research spans theoretical and computational physics with emphasis on quantum mechanics. His work includes wavelet-based methods for quantum dynamics, quantum trajectory formulations for spin-1/2 systems, nanophotonics, and quantum dot spectroscopy. Recent publications (2023-2024) focus on interacting quantum trajectories and superconducting resonator modeling, while his emerging interest in Quantum Field Theory aims to develop undergraduate pedagogical approaches. Publication analysis reveals an evolution from foundational wavelet applications in quantum chemistry (2006-2010) toward current work on quantum trajectories and superconducting device physics. This trajectory demonstrates consistent innovation in computational algorithms for quantum systems, with increasing interdisciplinary collaboration spanning physics, chemistry, and engineering. No scientific awards were documented in the source material. Lombardini actively mentors undergraduate researchers across diverse theoretical and computational projects, explicitly welcoming work "of all shapes and sizes." His postdoctoral fellowship at Rice University indicates prior grant support, and his ongoing publication record suggests sustained research funding for computational physics initiatives. As a computational physicist, Lombardini maintains a research group focused on high-performance numerical simulations of quantum systems, with recent work involving collaborative development of the ARTEMIS framework for superconducting device modeling.

Sukru Kitisمشاهده پروفایل

استادیار

• Medical Electronics
• Biomedical Signal Processing
• EEG and ECG Analysis
• +۵ مورد دیگر

Assistant Professor Sukru Kitis is a dedicated academic at Kütahya Dumlupınar University's Simav Faculty of Technology, Department of Electrical-Electronics Engineering. With over 15 years of academic experience, he has progressed from Instructor (2009-2019) to Doctor Lecturer (2019-present), currently serving as Deputy Head of Department since 2022. His academic journey includes a Bachelor's and Master's in Electrical-Electronics Engineering from Niğde University, followed by a PhD from Sakarya University. His educational background includes: 1999-2004: Bachelor's in Electrical-Electronics Engineering, Niğde University 2004-2007: Master's in Electrical-Electronics Engineering, Niğde University 2008-2019: PhD in Electrical-Electronics Engineering, Sakarya University Professor Kitis's research spans two primary domains with significant interdisciplinary connections: biomedical engineering and renewable energy systems. His biomedical work focuses on advanced signal processing techniques for EEG and ECG analysis, novel filter design for medical applications, and increasingly, machine learning approaches to medical diagnostics including thyroid cancer examination, obesity stage detection, and neurological disorder assessment. His energy research centers on geothermal heating system automation, solar energy applications, and innovative vehicle design using alternative energy sources. These research strands converge in his recent work on providing energy for biomedical devices using renewable resources. His publication record demonstrates strategic evolution from circuit design and biomedical instrumentation (2012-2017) toward data-driven healthcare solutions (2018-2025), while maintaining parallel expertise in sustainable energy systems. This dual focus creates unique opportunities for students interested in the intersection of healthcare technology and sustainable engineering. Professor Kitis has received recognition for his scholarly contributions, most notably the Best Paper award in 2017. His research impact extends through: 93 publications including journal articles and conference proceedings 3 major research projects related to forest fire monitoring, electric vehicle design, and geothermal system automation 2 scientific awards including Best Paper 2017 As an educator and mentor, Professor Kitis supervises numerous graduate students working on cutting-edge projects at the intersection of electrical engineering, biomedical applications, and renewable energy. His administrative roles as Department Head (2019-2021) and current Deputy Head of Department demonstrate leadership within his academic community while maintaining active research and teaching responsibilities. His lab environment emphasizes practical application of theoretical concepts, with students working on real-world problems in medical device development, energy system optimization, and data-driven healthcare solutions. The interdisciplinary nature of his research creates a dynamic environment where electrical engineering principles meet medical and environmental challenges.

Joanna Wołoszynمشاهده پروفایل

استادیار

• Magnetic Field Analysis
• Ferromagnetic Objects
• Wavelet Analysis
• +۵ مورد دیگر

Dr. Joanna Wołoszyn is an Assistant Professor at the Department of Electrical Engineering within the Faculty of Electrical and Control Engineering at Gdańsk University of Technology. She earned her doctoral degree (dr inż.) in Electrical Engineering from the same institution in 2001. Her office is located in Room 113 of the faculty building, and she can be contacted via email at joawolos@pg.edu.pl or phone at +48 58 347 1691. Her research focuses on electromagnetic phenomena with specialized applications: Magnetic field analysis of ferromagnetic objects and naval systems Wavelet transforms for biomedical diagnostics and brain spirography Magnetic signature reduction and degaussing techniques for ships Development of educational tools for electrical circuit analysis Numerical simulations using finite element methods (MES/OPERA 3D) Electromagnetic coil design and optimization Her publications demonstrate consistent focus on electromagnetic applications in naval engineering and measurement systems, with recent work (2023) introducing decomposition methods for degaussing optimization and earlier contributions (2016) on educational software development. The research consistently employs advanced computational modeling across electromagnetic systems. She has been recognized with 3 scientific achievements though specific awards are not detailed. Teaching forms a significant part of her profile, with development of the SMATH-based circuit analysis tool and instruction of 65 courses in electrical engineering.

Nira Dynمشاهده پروفایل

استاد

• Geometric Modeling
• Subdivision methods
• Multivariate approximation theory
• +۴ مورد دیگر

Nira Dyn is a Professor of Applied Mathematics at Tel Aviv University's School of Mathematics, where she has established herself as a leading researcher in geometric modeling and approximation theory. Her academic career spans decades of contributions to subdivision methods and computational mathematics, with a consistent focus on both theoretical foundations and practical applications in computer graphics and image processing. Research Interests Professor Dyn's primary research areas include Geometric Modeling , Subdivision methods , and Multivariate approximation theory , with significant contributions to Computer-Aided Geometric Design (CAGD) and Image Compression. Her current work centers on Nonlinear subdivision schemes and the Approximation of set-valued functions , representing cutting-edge extensions of classical approximation theory to handle complex geometric structures and uncertain data. These interests form a cohesive research program that bridges pure mathematical analysis with computational applications, particularly in handling geometric data through innovative subdivision techniques. Publication Trends Analysis of her recent publications reveals a strong emphasis on advancing subdivision methodologies beyond linear frameworks, with increasing focus on nonlinear schemes capable of handling complex geometries and set-valued data. Her work demonstrates consistent progression from foundational subdivision theory toward practical applications in image compression and geometric modeling, with notable contributions to metric-based approximation techniques. The publications showcase interdisciplinary reach spanning mathematics, computer science, and engineering applications, while maintaining rigorous mathematical foundations in approximation theory. Professional Activities While specific advising relationships and grant information aren't detailed in the available materials, Professor Dyn's extensive publication record in top-tier journals indicates active research leadership. Her collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern geometric modeling research. The absence of explicit laboratory or team information suggests her work may be primarily theoretical or conducted through collaborative networks rather than a dedicated physical research space.

• Ionospheric Physics
• Space Weather
• Earthquake-Ionosphere Interactions
• +۳ مورد دیگر

Murat Canyılmaz serves as an Associate Professor in the Physics Department at Firat University, Turkey, a position held since March 2016. His academic foundation includes doctoral studies in Physics at the same institution since 2008. His research centers on ionospheric physics and space weather phenomena , with particular expertise in very low frequency (VLF) signal analysis , solar-terrestrial interactions , and earthquake-ionosphere coupling . Key methodologies include wavelet-based denoising techniques, correlation analysis of Schumann resonances with geomagnetic indices, and investigation of chemical reaction dynamics in ionospheric regions. Analysis of his 15 most recent publications reveals dominant trends in seismo-ionospheric precursors (TEC anomalies preceding earthquakes), solar flare impacts on VLF propagation , and fundamental ionospheric chemistry (O+ reactions). His work bridges atmospheric physics, space science, and geophysical hazard prediction through rigorous observational and modeling approaches. Collaborative research dominates his publication record, with frequent co-authorship with Esat Guzel, Mehmet Yasar, and Emrah Yalcin across multiple institutions. His experimental work leverages SuperSID monitoring systems, ionosonde data, and international reference models like IRI. While no formal awards or students are documented in available records, his sustained publication output since 2004 demonstrates active research engagement. Current investigations focus on machine learning applications for earthquake precursor detection and high-resolution analysis of ionospheric disturbances during celestial events like solar eclipses.

• Neural Networks
• Electroencephalography
• Epileptic Seizure Detection
• +۴ مورد دیگر

Dimitrios Liarokapis serves as a Senior Lecturer in Cyber Security and Networks at Glasgow Caledonian University, United Kingdom. His primary affiliation is with the Department of Cyber Security and Networks within the university's engineering or computing school structure, though the specific school name isn't explicitly stated in available materials. His research spans three interconnected domains: Biomedical AI applications using Random Neural Networks for epileptic seizure detection and fall prevention through EEG signal analysis Telecommunications innovation focusing on millimeter wave optimization for 5G cellular networks Sustainable logistics through data-intelligence-driven digital twin frameworks These areas demonstrate consistent application of neural network architectures to solve domain-specific problems with real-world impact. Analysis of his publication trends since 2011 reveals increasing focus on healthcare and logistics applications, with recent high-impact work in Applied Sciences (2024) and Sensors (2022). His eight research outputs show growing citation impact, particularly in medical AI applications. Dr. Liarokapis actively secures industry partnerships, currently leading the Advanced Logistics Data Integration project with JOHN G. RUSSELL (TRANSPORT) LIMITED. This Knowledge Exchange initiative develops bespoke data pipelines for logistics operations, demonstrating strong industry-academia collaboration. His supervision record includes at least one research student, with opportunities likely available in his interdisciplinary projects. His laboratory environment emphasizes practical AI implementation across healthcare monitoring systems and sustainable logistics solutions, leveraging neural network architectures to process complex datasets from EEG signals to supply chain operations.