جست‌وجوی استاد و پژوهشگر

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

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

Jing Fuمشاهده پروفایل

مدرس

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

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

Hendra I Nurdinمشاهده پروفایل

مدرس ارشد

• Quantum Control
• Quantum Systems
• Systems Theory
• +۱۰ مورد دیگر

Dr. Hendra I Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been a faculty member since 2012. His academic background spans Electrical Engineering and Applied Mathematics with a focus on systems and control theory, with significant research intersections in quantum physics and power engineering. He serves as an Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) starting from January 2024. Dr. Nurdin's research spans quantum systems and control, nonlinear control of microgrids, and neuromorphic computing for nonlinear systems. His work in quantum control intersects with quantum physics while his research on microgrid control intersects with power engineering. His research has resulted in numerous publications across prestigious journals including Nature Communications, Physical Review Research, IEEE Control Systems Letters, and IEEE Transactions on Automatic Control. His recent publications (2020-2025) predominantly focus on quantum reservoir computing, quantum parameter estimation, control of non-Markovian quantum systems, and applications of control theory to energy systems. The research demonstrates a clear trajectory toward practical implementations of quantum computing concepts and advanced control methodologies for both quantum and classical systems. Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) from January 2024 Dr. Nurdin has supervised several PhD students to completion and currently mentors PhD candidate Wen Liu. His research group has produced significant work in quantum control systems, with former students including Wenxing Li, Yihuan Liao, Jiayin Chen, Jiacheng Li, Muhammad Ali, Zhan Shi, and Onvaree Techakesari. He actively seeks new students with strong academic backgrounds for research in his areas of interest, particularly in quantum systems and control. His laboratory work focuses on quantum systems control, quantum reservoir computing, and microgrid control systems. He has developed courses including ELEC9782 Special Topics in Electrical Engineering 2 (Quantum Control) and teaches ELEC4631 Continuous-Time Control System Design.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

• Fluid network dynamics
• Partial differential equation solvers
• Hydraulic network characterization
• +۴ مورد دیگر

Dr. Aaron Zecchin is a Senior Lecturer in Environmental Engineering at the University of Adelaide, affiliated with the School of Architecture and Civil Engineering under the Faculty of Sciences, Engineering and Technology. His research focuses on fluid network dynamics, hydraulic system modeling, and evolutionary algorithm applications in engineering problems. His expertise includes transient analysis in pipeline networks, fault detection using statistical methods, and optimization techniques for water distribution systems. He actively investigates fluid-structure interactions, Laplace-domain characterizations, and sparse linear solvers for steady-state simulations. His work integrates advanced computational methods with practical engineering challenges, emphasizing infrastructure resilience and sustainability. Research Themes: Hydraulic transients, pipeline condition assessment, system identification Key Tools: Method of characteristics, Kalman filtering, ant colony optimization Applications: Water distribution systems, flood mitigation, wildfire risk modeling Recent publications emphasize anomaly detection in pipe systems, burn probability prediction using dimensionality reduction, and optimizing municipal infrastructure rehabilitation. His work bridges theoretical modeling with real-world infrastructure challenges, contributing to safer and more sustainable engineering solutions.

Amalia Kokkinakiمشاهده پروفایل

دانشیار

• Groundwater Flow and Contaminant Transport
• Environmental Hydrogeology
• Contaminant Remediation
• +۳ مورد دیگر

Amalia Kokkinaki is an Associate Professor in the Department of Environmental Science at the University of San Francisco (USF), where she has been a full-time faculty member since 2016. She also serves as the Program Director and is actively involved in the university community, including advising the Environmental Engineering and Science Club and serving on the curriculum committee for the new engineering program. Her educational background includes a PhD and MASc in Civil and Environmental Engineering from the University of Toronto (2013 and 2007, respectively) and a BS and MS in Environmental Engineering from the Technical University of Crete (2005). Dr. Kokkinaki's research focuses on the intersection of environmental engineering and mathematical modeling, with expertise in groundwater flow and contaminant transport , contaminant hydrogeology , and statistical modeling and data assimilation . Her work addresses critical challenges in groundwater remediation , particularly for dense non-aqueous phase liquids (DNAPLs), and extends to carbon dioxide storage monitoring and biological treatment of organic pollutants . She employs advanced techniques such as Bayesian inverse modeling and hydrogeophysical methods to characterize subsurface systems and improve remediation strategies. Her recent publications (2013-2023) demonstrate a consistent focus on developing and applying innovative modeling approaches for subsurface characterization and contaminant transport. Key trends include the integration of machine learning (e.g., deep learning-based inversion) with traditional hydrogeological methods, advancements in data assimilation techniques for real-time monitoring, and the application of these methods to complex problems such as DNAPL source zone remediation and CO2 storage. Dr. Kokkinaki has received recognition for her contributions, including: Best reviewer award, Journal of Environmental and Engineering Geophysics (JEEG) 26(3), 2021 University of San Francisco, Mentor of the Year, 2020 She is a dedicated mentor, having advised numerous undergraduate and graduate students on projects ranging from PFAS sorption kinetics to urban forest air quality analysis. Her students have worked on diverse topics including PFOA sorption, uranium-VI modeling, residential electrification, and graphical user interfaces for water resources management. Additionally, she teaches undergraduate courses in environmental science and graduate courses in the master’s program (MSEM) and the Geospatial Analysis Lab (GsAL). Dr. Kokkinaki is an active member of the Geospatial Analysis Lab (GsAL) and contributes to interdisciplinary initiatives such as the development of USF's new engineering program.

Joseph Timoneyمشاهده پروفایل

استاد

• Audio Signal Processing
• Musical Sound Synthesis
• Digital Audio Watermarking
• +۳ مورد دیگر

Joseph Timoney is a professor at the Department of Computer Science , Maynooth International Engineering College , Maynooth University. He teaches undergraduate programs in Computer Science and Music Technology, with expertise in audio signal processing, musical sound synthesis, and digital modeling of analog subtractive synthesis. His research spans sound synthesis algorithms, audio watermarking, and ubiquitous music ecosystems.

Alex Baldwinمشاهده پروفایل

استادیار

• Computational Neuroscience
• Visual Psychophysics
• Binocular Vision
• +۶ مورد دیگر

Alex Baldwin is an Assistant Professor at McGill University within the Department of Ophthalmology & Visual Sciences. He serves as a Junior Scientist in the Brain Repair and Integrative Neuroscience (BRaIN) program at the Research Institute of the McGill University Health Centre. Supervisor in McGill's Integrated Program in Neuroscience (IPN) Supervisor in Quantitative Life Sciences program Laboratory located at Montreal General Hospital Research focuses span computational neuroscience, visual psychophysics, and binocular vision mechanisms , particularly examining: Amblyopia and spatial scrambling Aging-related changes in stereopsis Visual noise adaptation Contrast summation models Contour integration processes Digital therapeutic development His lab employs Matlab/Octave (Psychtoolbox), Python (PsychoPy), and Unity/C# for experimental design, with Python (Jupyter) and Matlab (Palamedes Toolbox) for data analysis. Collaborations include Wenzhou Medical University. Scientific recognition includes: NSERC Discovery Grant (2022-2027) HBHL Ignite Funding FRQ-S Vision Health Research Network Pilot Training opportunities available for undergraduate (PSYC-396/COGS-444/COGS-401) and graduate students , with current advisees working on topics including: Visual snow syndrome neural mechanisms Binocular imbalance in aging Contour integration modeling Adaptive strategies in amblyopia Dichoptic ebook therapy development

Radu Balanمشاهده پروفایل

استاد

• Applied Mathematics
• Harmonic Analysis
• Signal Processing
• +۶ مورد دیگر

Radu Balan is a Professor of Applied Mathematics in the Department of Mathematics at the University of Maryland, College Park. He is affiliated with the Center for Scientific Computation and Mathematical Modeling (CSCAMM) and the Norbert Wiener Center for Harmonic Analysis and Applications. His research bridges harmonic analysis, signal processing, and machine learning, with a strong focus on phase retrieval, frame theory, and deep neural network analysis. He earned his Ph.D. in Applied Mathematics from Princeton University in 1998, advised by Ingrid Daubechies, and holds B.Sc. degrees in Physics and B.E. in Electrical Engineering from Romanian institutions. His academic journey includes a postdoctoral stint at IMA and IBM, followed by research roles at Siemens before joining the University of Maryland faculty in 2006 as an Associate Professor, rising to full Professor in 2011. His research interests include harmonic analysis, statistical signal processing, machine learning, phase retrieval, and mathematical modeling. He has made significant contributions to the theory of frames, phaseless reconstruction, and the stability of deep neural networks. His recent work explores permutation-invariant representations, Lipschitz bounds in CNNs, and graph deep learning. His recent publications (2020–2024) reveal a strong trend toward theoretical machine learning and its mathematical foundations, particularly in the stability and invertibility of neural networks and representations. He frequently publishes in top-tier journals such as IEEE Transactions, SIAM Journal, and Foundations of Computational Mathematics, often on arXiv, indicating an active and current research program in mathematical signal processing and deep learning theory. Radu Balan has authored or co-authored over 60 peer-reviewed papers, edited five volumes of Excursions in Harmonic Analysis , and holds 25 U.S. and international patents in signal processing and source separation. His work has been supported by the National Science Foundation (NSF), Army Research Office (ARO), Simons Foundation, Siemens, and Verisk. He has advised numerous students and collaborators, with research projects spanning statistical signal processing, blind source separation, and deep learning. His lab and research group are associated with CSCAMM and the Norbert Wiener Center, fostering interdisciplinary collaboration in mathematical modeling and computation.

Tony F. Chanمشاهده پروفایل

استاد

• Mathematical Image Processing
• Computer Vision
• Computational Brain Mapping
• +۱۴ مورد دیگر

Tony F. Chan is currently President and Professor of Mathematics and Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He holds the title of Professor Emeritus in the Department of Mathematics at the University of California, Los Angeles (UCLA), where he previously served as Professor with joint appointments in Computer Science and Bioengineering. He was Dean of the Division of Physical Sciences at UCLA (2001–2006) and Assistant Director at the National Science Foundation (NSF) for Mathematics and Physical Sciences (2006–2009). President, HKUST Professor, Mathematics & Computer Science and Engineering, HKUST Professor Emeritus, Mathematics, UCLA Assistant Director, NSF (2006–2009) Dean, Division of Physical Sciences, UCLA (2001–2006) His research interests are centered around mathematical image processing, computer vision, computational brain mapping, and numerical algorithms. He has made seminal contributions to variational methods, total variation regularization, level set methods, and multiscale computational techniques. His work bridges pure mathematics with applications in biomedical imaging, VLSI design, and scientific computing. His recent publications focus on image segmentation, inpainting, brain surface mapping, and nonlocal filtering. These works demonstrate a strong trend toward geometric and variational models for image analysis, with increasing emphasis on medical and biological applications such as neuron tracking and cortical mapping. One of the most cited mathematicians (ISI Highly Cited) Chan has mentored over 25 PhD students and 15 postdoctoral fellows, contributing significantly to the training of next-generation researchers in applied mathematics and computational science. He has led major research initiatives including the Institute for Pure & Applied Mathematics (IPAM) and has been involved in numerous professional services at national and international levels. His work has been supported by major funding agencies including the NSF. He leads the Image Processing Group at UCLA and has been instrumental in advancing interdisciplinary research at the intersection of mathematics, engineering, and neuroscience.

Neal Smithمشاهده پروفایل

دانشیار

• Sports Biomechanics
• Non-linear Motion Analysis
• Soccer Biomechanics
• +۳ مورد دیگر

Dr Neal Smith is a Reader in Biomechanics at the University of Chichester, teaching undergraduate and postgraduate modules including Sports Injury and Prevention, Biomechanical Techniques, and MSc Ergonomics in Sport and Exercise while providing sports science support to elite divers and cricket bowlers. His academic credentials include: BSc (Hons) Sport and Exercise Science, Liverpool John Moores University (1993) MSc Ergonomics, Loughborough University (1994) PhD Sport Biomechanics, University of Southampton (1999) Smith's research centers on sports biomechanics with pioneering work in non-linear motion analysis for soccer and racket sports, kicking biomechanics across multiple sports, and ergonomic footwear design . His methodology integrates ground reaction force measurement, kinematic analysis, and practical sports applications to enhance athletic performance and prevent injuries. Analysis of his 15 most recent publications reveals dominant research themes in soccer biomechanics (particularly instep kicking dynamics across approach angles and velocities), ACL injury risk assessment through advanced signal processing, and expanding investigations into women's rugby breast health and concussion biomarkers in youth football. His work consistently bridges laboratory findings with real-world athletic contexts. Smith currently supervises 2 PhD candidates and serves as external examiner for BSc/MSc programs at Teesside and Portsmouth Universities. His collaborative research spans orthopaedics, sports medicine, and performance science teams across multiple institutions. His research operations involve interdisciplinary collaboration with sports scientists, orthopaedic specialists, and performance coaches, focusing on translating biomechanical insights into practical training modifications and injury prevention protocols across soccer, cricket, rugby, and cycling disciplines.

Kenle Chenمشاهده پروفایل

دانشیار

• Radio-frequency integrated circuits
• Millimeter-Wave systems
• 5G and beyond wireless communications
• +۳ مورد دیگر

Kenle Chen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), part of the College of Engineering and Computer Science (CECS). He holds a Ph.D. in Electrical Engineering from Purdue University (2013). His research focuses on radio-frequency (RF) and millimeter-wave integrated circuits, emphasizing energy-efficient, high-speed, wideband, and high-efficiency radio solutions for 5G-and-beyond wireless systems. Key projects include reconfigurable high-frequency circuits and interdisciplinary applications of radio technology. Dr. Chen leads the INSPIRE Lab, exploring innovative RF/mm-Wave circuits for wireless communications, IoT, and emerging technologies. He actively contributes to professional activities, including serving as an Associate Editor for IEEE Transactions on Microwave Theory and Techniques and Chair of the IEEE MTT-S/AP-S Orlando Chapter. His work has earned recognition, including 1st Place in IEEE IMS Student Design Competitions (2018, 2019) as an advisor and the IEEE MTT-S Doctoral Fellowship (2012). His research spans cutting-edge topics like load-modulated balanced amplifiers, Doherty architectures, and antenna design, addressing challenges in next-generation wireless systems. UCF’s strong engineering programs and Orlando’s tech corridor provide a robust environment for his interdisciplinary work.

Christian Vogelمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Analog/Digital Conversion (ADCs)
• Mixed-Signal Systems
• +۵ مورد دیگر

Christian Vogel is a Senior Lecturer affiliated with the Institute for Signal Processing and Speech Communication at TU Graz. His work focuses on advanced signal processing techniques, analog/digital conversion systems, and mixed-signal architectures. Key research areas include time-interleaved ADC error correction, digital predistortion for RF systems, and EVM estimation methodologies. Education: Holds a Dr.techn. (Doctor of Engineering) and Dipl.-Ing. (Diploma in Engineering). His research emphasizes practical applications in mixed-signal systems, including calibration techniques for high-resolution ADCs, nonlinear system compensation, and energy-efficient transmitter design. Recent work explores optimization-based quantization, crest factor reduction in envelope tracking, and adaptive filtering for digital predistortion. Publications span over 15 years, addressing topics ranging from PWM-based RF transmission to all-digital phase-locked loops. His contributions bridge theoretical signal processing with hardware implementation challenges in modern electronic systems. Teaching expertise includes analog and digital signal processing, with authorization to teach at TU Graz. Active in industry-academia collaboration, his work aims to improve system performance in telecommunication and embedded electronics.

Sasan Fathpourمشاهده پروفایل

استاد

• Integrated Photonics
• Nonlinear Optics
• Silicon Photonics
• +۳ مورد دیگر

Sasan Fathpour is a Professor at CREOL, The College of Optics and Photonics, University of Central Florida (UCF). He holds academic appointments in Optics & Photonics and Electrical & Computer Engineering. His research focuses on integrated photonics, nonlinear optics, and heterogeneous platforms operating across visible to mid-infrared wavelengths. He has authored/co-authored over 160 publications and founded Partow Technologies, LLC. He is an OSA Fellow, IEEE Senior Member, and SPIE Senior Member. Education: Ph.D. in Electrical Engineering, University of Michigan, Ann Arbor (2005) Postdoctoral Fellow, UCLA Electrical Engineering (2005–2007) Research Interests: Development of integrated photonic devices for linear/nonlinear applications, including electrooptic modulators, sensors, and quantum photonics. Recent work emphasizes thin-film lithium niobate for high-speed and broadband applications. Key Awards: NSF CAREER Award (2012) ONR Young Investigator Award (2013) UCF Reach for the Stars Award (2015) Grants & Advising: Active grants in integrated photonics. Advises a team of graduate students and postdocs focused on novel photonic device fabrication and applications. Alumni include leading researchers in industry and academia. Labs/Teams: Leads the Integrated Photonic Emerging Solutions lab, specializing in heterogeneous integration and nonlinear optics. Collaborates with industry partners for commercialization of photonic technologies.

• Semantic Audio
• Music Information Retrieval
• Semantic Web
• +۳ مورد دیگر

Dr. George Fazekas is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on semantic audio, music information retrieval, and deep learning applications in music technology. He is affiliated with the Centre for Multimodal AI and contributes to advancing technologies like semantic web music, emotion recognition, and differentiable signal processing. His teaching includes modules on research methods, responsible innovation, and the Semantic Web. Research interests span semantic audio analysis, music ontology development, and AI-driven music production tools. He has collaborated on projects such as the Open Multitrack Testbed and the Audio Commons Ontology. Notable contributions include work on handwritten music synthesis, dynamic range compressor control, and adaptive music systems like Mood Conductor. Publications emphasize machine learning for music tasks, including few-shot learning for historical manuscripts and neural synthesis techniques. His work bridges theoretical research with practical applications in audio engineering and music technology.