Spatial Frequency Processing Professors and Supervisors | ApplySharif

Find Academic Supervisors

Find and connect with academic supervisors, professors, and scholars for your research journey.

Showing 706-720 of 1,335 supervisors

Profile data may be stale. It is sourced from public faculty pages.

Josiah PassmoreView profile

Researcher

Utrecht UniversityNetherlands

Dr. Josiah Passmore is a Researcher in the Department of Cell Biology, Neurobiology and Biophysics within the Faculty of Science at Utrecht University. His work focuses on understanding cellular organization and organelle interactions through advanced microscopy techniques. Dr. Passmore's primary research investigates the "organelle connectome" - the complex network of interactions between cellular organelles. He specializes in studying membrane contact sites, regions where organelle membranes come into close proximity, often tethered by proteins. These sites play crucial roles in metabolite exchange, lipid transfer, intracellular signaling, and coordinating organelle dynamics. His methodology includes inducible organelle transport systems that allow physical contact to persist during artificial organelle displacement, enabling him to probe the extent, regulation, and robustness of the organelle connectome. Dr. Passmore's publication record reveals a significant evolution from fundamental studies of organelle biology toward the development of advanced microscopy techniques. His work on GelMap represents a major contribution to intrinsic calibration and deformation mapping in expansion microscopy. His research increasingly bridges computational approaches with biological questions, developing sophisticated methods for analyzing cellular structures and spatial organization. The progression from his 2021 review on organelle dynamics to his 2025 computational biology papers demonstrates a clear trajectory toward methodological innovation in cellular imaging. His research contributes to UN Sustainable Development Goals related to scientific advancement and technological innovation. The fingerprint analysis of his work identifies key themes including Expansion Microscopy (100%), Deformation Mapping (66%), Intrinsic Calibration (66%), Hydrogel Engineering (50%), Organelle Interaction (33%), Organelle Dynamics (33%), Grid Deformation (33%), and Deformation Correction (33%).

Official University Profile

Andreas KochView profile

Researcher

Technical University of MunichGermany

Andreas Koch is a Research Associate at the Professorship of Simulation for Additive Manufacturing at the Technical University of Munich (TUM), Germany. His research focuses on computational modeling of compressible multiphase flows, CutDG methods, and high-performance computing with software development. He holds a Master of Science (M.Sc.) in Mechanical Engineering from TUM (2024). Research Interests Koch's work bridges computational mechanics and machine learning, with emphasis on cut discontinuous Galerkin methods for simulating complex flows and high-performance computing systems. His publications demonstrate expertise in machine learning applications for aerospace systems, including anomaly detection in spacecraft telemetry and deep learning acceleration for spaceborne hardware. Scientific Awards ERC Starting Grant Publications (2024-2013) His research portfolio includes 15 recent publications spanning edge AI solutions for spacecraft , RF synchronization systems, and machine learning frameworks for aerospace applications. Earlier work explores robot perception and geoinformatics implementations.

Official University Profile

Yuriy RapoportView profile

Research Fellow

Taras Shevchenko National University of KyivUkraine

Dr. Yuriy Rapoport is a Leading Research Fellow at the Space Physics Laboratory of the Physical Faculty, Taras Shevchenko National University of Kyiv, Ukraine. With a career spanning over three decades, he has held positions as Senior Research Fellow (1991-2003; 2006-2018) and Junior/Senior Researcher at Research Institute "Saturn" (1985-1991). His affiliations include collaborations with institutions in the UK, Japan, Finland, Denmark, Mexico, Poland, and USA. MSc (1978) from Taras Shevchenko National University of Kyiv PhD (1986) from Taras Shevchenko National University of Kyiv Doctor of Physical-Mathematical Sciences (2017) from Taras Shevchenko National University of Kyiv Dr. Rapoport’s research focuses on nonlinear wave processes in plasma and metamaterials, electromagnetic wave propagation in layered media, and space-terrestrial physics. Key areas include soliton control, quantum wave resonances in graphene-based systems, hot spot formation in hyperbolic metamaterials, and seismo-ionospheric coupling mechanisms. His publications demonstrate expertise in plasma physics , nonlinear optics , metamaterial design , and Earth-Ionosphere waveguides . Trends include modeling of VLF electromagnetic waves, acoustic-gravity wave interactions, and applications to space weather and disaster monitoring. Grant of National Science Foundation of the USA (1999-2000) Individual Grant of International Science Foundation (1994) Grant of American Physical Society (1993) He has led international projects on resonant wave phenomena in plasma and metamaterials and contributed to studies on ionospheric responses to earthquakes, hurricanes, and magnetic storms. His collaborative work involves teams from Aalto University, University of Sheffield, and Hayakawa Institute of Seismo Electromagnetics.

Official University Profile

Тетяна Іванівна БугроваView profile

Associate Professor

Transilvania University of BrasovRomania

Тетяна Іванівна Бугрова is an Associate Professor at the Department of Radio Engineering and Telecommunications within the Faculty of Information Security and Electronic Communications at Zaporizhzhia Polytechnic National University. With over four decades of academic experience since commencing her work at the university in 1979, she has established herself as a prominent researcher in microwave and antenna technologies. Her career spans both teaching and significant research contributions in specialized areas of radio engineering. Her research interests focus on microwave equipment and antennas, quasi-optical devices, mobile communication equipment, and particularly fractal antennas with applications of metamaterials in antenna technology. Dr. Bugrova has authored approximately 30 scientific works including 1 patent, 2 monographs, 3 textbooks, and 10 publications indexed in Scopus. Her research output demonstrates consistent focus on advanced antenna design, with particular emphasis on millimeter wave applications and innovative fractal structures integrated with metamaterials. Her recent publications reveal a strong trend toward hybrid antenna systems combining fractal geometry with metamaterial properties to achieve enhanced performance characteristics. These works explore spectral properties, directional characteristics, and frequency behaviors of novel antenna designs that push the boundaries of conventional antenna technology. Her research bridges theoretical electromagnetic principles with practical applications in wireless communications and radar systems. As an educator, Dr. Bugrova teaches specialized courses including Satellite information systems, Special purpose antennas and automated antenna design systems, Signals and processes in radio engineering, and Fundamentals of information transmission theory and statistical radio engineering. Her academic journey began with a diploma with honors from Zaporizhia Institute of Mechanical Engineering in 1979, specializing in Radio Engineering, and culminated in her Candidate of Technical Sciences degree in 2009 with a thesis on quasi-optical integrated pattern-forming circuits of multi-band antennas in the millimeter wave range.

Official University Profile

Pierre-Louis FrisonView profile

Associate Professor

National School of Geographic SciencesFrance

Pierre-Louis Frison serves as Associate Professor at Gustave Eiffel University within the National School of Geographical Sciences (ENSG) and LASTIG research laboratory. He coordinates the Master 2 program in Geographical Information, Spatial Analysis and Remote Sensing, directing academic strategy for advanced geospatial education. His research specializes in radar remote sensing for terrestrial surface monitoring, with core emphases on: Vegetation and forest cartography using Sentinel-1 data Land use change detection at regional-to-global scales Development of customized QGIS toolkits for radar image processing Crop monitoring through microwave remote sensing techniques Frison teaches foundational and advanced coursework in image processing and remote sensing, covering spatial/frequency domain analysis, radar polarimetry, speckle filtering, and thermal infrared methodologies. His pedagogical approach integrates theoretical principles with practical geospatial software applications.

Official University Profile

Humberto VergaraView profile

Assistant Professor

University of IowaUnited States

Humberto Vergara is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Iowa, and concurrently serves as an Assistant Research Engineer at IIHR—Hydroscience and Engineering. He is also affiliated with the Iowa Flood Center. His work spans numerical hydrologic modeling, flash flood forecasting, and remote-sensing hydrology, with a strong emphasis on scientific computing in hydrologic applications. Education: PhD, Civil Engineering (Water Resources), University of Oklahoma MSc, Civil Engineering (Water Resources), University of Oklahoma BS, Environmental Engineering, El Bosque University, Colombia Research Interests: Vergara’s research is centered on extending flash flood forecasting capabilities in data-scarce regions, understanding flash floods in post-fire environments, and improving the physics representation in parsimonious hydrologic models. He also focuses on extending lead times for flash flood forecasts and warnings, leveraging satellite observations and machine learning techniques. His recent work includes developing satellite-based frameworks for early warning systems in West Africa, evaluating global precipitation products, and integrating machine learning models to enhance precipitation nowcasting and flood impact classification. Scientific Awards: No awards or honors are explicitly mentioned in the provided text. Research Labs and Teams: He leads the Advanced Hydrology and Warning Applications Laboratory at the University of Iowa, which focuses on cutting-edge research in hydrologic modeling, early warning systems, and remote sensing applications for flood forecasting.

Official University Profile

Dmitry Nikolaevich LapshinView profile

Researcher

Weierstrass Institute for Applied Analysis and StochasticsGermany

Dmitry Nikolaevich Lapshin is a Doctor of Biological Sciences and Leading Researcher at the Laboratory of Sensory Information Processing, Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS). His career spans institutions like Moscow State University's Biological Faculty (1980-1994) and the Moscow Institute of Radio Engineering Equipment (pre-1980). As a protégé of Prof. Alexey Byzov, Lapshin's work bridges sensory physiology, bioacoustics, and neuroethology. Education : Bauman Moscow State Technical University (1976) His research focuses on mosquito bioacoustics and nocturnal lepidopteran echolocation strategies , revealing novel mechanisms like frequency-tuned auditory neurons in mosquitoes and acoustic counter-strategies against bats. Publications in Insects , Sensory Systems , and Journal of Experimental Biology demonstrate interdisciplinary impact. Articles show progressive exploration of insect auditory systems across 15 years. Lapshin has received multiple RFBR grants (2006-2014) and international support from Volkswagen Stiftung (1997). His work fundamentally reshaped understanding of insect auditory processing through electrophysiological and behavioral experiments. Collaborations with D.D. Vorontsov and R.D. Zhantiev highlight long-term scientific partnerships. Technical Expertise : Acoustic signal processing in insects Neural mechanisms of sound detection Frequency tuning analysis Flight simulation methodologies Echolocation system modeling Sensory-motor integration

Official University Profile

Zhixiang ChenView profile

Lecturer

University of SheffieldUnited Kingdom

Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.

Official University Profile

Alex BestView profile

Lecturer

University of SheffieldUnited Kingdom

Dr. Alex Best is a Lecturer in Mathematical and Statistical Modelling at the School of Mathematical and Physical Sciences, University of Sheffield. He serves as the Student Voice Lead for Mathematics & Statistics and has been a faculty member since 2016. His academic journey includes a Leverhulme Early Career Research Fellowship at Sheffield (2013-2016), an Associate Research Fellowship at Exeter (2012), and postdoctoral work at Sheffield (2010-2011). He completed his PhD in Animal and Plant Sciences at the University of Sheffield (2006-2010). Dr. Best's research focuses on mathematical biology, particularly using theoretical models to investigate infectious disease dynamics. His work spans multiple scales and incorporates: Models of host-parasite coevolution Spatial structure in epidemic and evolutionary models Immune processes in disease modeling Within-host models of bacteria-cell dynamics Seasonal effects on disease transmission Local vs. global interaction effects on epidemic spread His publication record demonstrates consistent contributions to evolutionary epidemiology, with recent work examining how environmental fluctuations impact host-parasite coevolution, the dynamics of disease spread in university settings, and the evolution of host tolerance mechanisms. His research often combines mathematical modeling with biological insights to address fundamental questions in disease ecology and evolution. Dr. Best has received recognition through the Leverhulme Early Career Research Fellowship and has made significant contributions to understanding the interplay between ecological dynamics and evolutionary processes in infectious disease systems. As an educator, Dr. Best teaches multiple courses including MAS377 Mathematical Biology and MAS316 Mathematical Modelling of Natural Systems. He is actively involved in promoting equality, diversity, and inclusion in academia, having served as Director for Equality, Diversity and Inclusion in his department from 2018-2022 and currently serving on the London Mathematical Society's Good Practice Scheme steering committee. His research group applies mathematical and computational tools to understand the ecology and evolution of infectious diseases, addressing questions about seasonal environments, local vs. global interactions, predator impacts on evolution, and optimal class sizes for epidemic control in university settings.

Official University Profile

Muhammad ShahzadView profile

Associate Professor

North Carolina State UniversityUnited States

Muhammad Shahzad is an Associate Professor in the Department of Computer Science at North Carolina State University and a member of the Networking Research Group. His research bridges systems and security with a focus on networking, security, and Internet of Things applications. His educational background includes: Ph.D. in Computer Science from Michigan State University (2015) B.E. in Electrical Engineering from National University of Sciences and Technology (NUST) (2008) Shahzad's research spans networking, cyber security, and IoT with specific emphases on network measurement, RFID systems, activity recognition, and device authentication. His work extends to cyber-physical systems, embedded real-time systems, and human-computer interaction, frequently involving real-world experimentation and novel sensing techniques for practical security applications. His recent publications (2023-2025) demonstrate interdisciplinary work across reinforcement learning for congestion control, blockchain scalability, data center power management, agricultural sensing via RF, urban mobility analysis, radar-based fruit detection, and non-intrusive network measurement - highlighting his focus on practical IoT and networking innovations. Key recognitions include: Best Poster Award, IIUG (2017) Winner, Virginia Tech Spectrum Sharing Radio Challenge (2016) Fitch-Beach Outstanding Research Award (2015) Outstanding Graduate Student Award (2015) His research is funded by NSF, Cisco, US Army, and Sony through projects including serverless edge computing, BLE onboarding, RF-based indoor mapping, and IoT performance measurement. Current work focuses on enabling stateful applications in serverless architectures, resource protection in cloud data centers, and human discovery through radio frequency signals. He actively contributes to the Networking Research Group at NC State, advancing collaborative efforts in connected computing environments through both foundational research and real-world system deployments.

Official University Profile

Filip ElvanderView profile

Assistant Professor

Aalto UniversityFinland

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

Official University Profile

Christian VaterView profile

Assistant Professor

University of BernSwitzerland

Christian Vater is an Assistant Professor at the Institute of Sports Science, University of Bern, leading the Peripheral Vision Group. His research focuses on visual perception, decision-making in sports, and attention mechanisms. Academic Rank: Assistant Professor Institution: University of Bern Research Areas: Visual perception, sports decision-making, attention allocation Vater's recent work examines how peripheral vision influences soccer players' reactions, referees' judgment accuracy, and basketball defensive strategies. His studies span cognitive psychology, sports neuroscience, and human factors in athletic performance. He serves as a reviewer for journals like Nature - Scientific Reports and organizations such as the German Research Foundation (DFG). Vater also contributes to the Advanced Studies of Sport Psychology (DAS) continuing education program and delivers guest lectures at the University of Basel.

Official University Profile

Bruno FazendaView profile

Associate Professor

University of SalfordUnited Kingdom

Dr. Bruno Fazenda is an Associate Professor at the School of Science, Engineering & Environment, University of Salford, and a key member of the Acoustics Innovation Institute. His work bridges acoustics, psychoacoustics, and machine learning, with a focus on enhancing audio quality for hearing-impaired listeners. He leads the Cadenza Challenges, a series of initiatives leveraging machine learning to improve music accessibility. Room Acoustics Sound Reproduction Psychoacoustics Audio Quality Perception Machine Learning in Audio Engineering His recent publications highlight advancements in audio quality metrics (PEAQ, PEMO-Q, ViSQOL, HAAQI), spatial hearing in virtual environments, and datasets for music processing challenges. Projects like EnhanceMusic (2022–2026) and S3A (2014–2019) underscore his commitment to immersive audio technologies and hearing aid compatibility. He has supervised theses on auditory salience and low-frequency instrument perception, contributing to both theoretical and applied acoustics research. Dr. Fazenda’s work also extends to historical acoustics, including Stonehenge impulse response measurements (2020), which explore prehistoric sound environments. His research integrates interdisciplinary perspectives, such as human evolution and brain function, into modern audio engineering solutions.

Official University Profile

Fatemeh MayvanehView profile

Researcher

University of MünsterGermany

Dr. Fatemeh Mayvaneh serves as a Researcher at the University of Münster's Climatology Group since December 2023, specializing in urban climate-health interactions with emphasis on vulnerable populations in Iranian and global contexts. Her work bridges environmental science, epidemiology, and public health policy through rigorous quantitative analysis. Her academic credentials include a Ph.D. in Urban Climatology (2015-2020), M.Sc. in Applied Climatology (2010-2013), and B.Sc. in Physical Geography (2003-2006), all from Hakim Sabzevari University, Iran. This foundation enables her interdisciplinary approach to climate-health challenges. Research focuses on quantifying health impacts from urban heat islands, air pollution (PM2.5, ozone), and climate extremes. She investigates thermal comfort thresholds, birth outcomes, and mortality patterns using advanced epidemiological methods. Current work emphasizes causal inference in national Iranian cohorts and multi-country comparative analyses to isolate temperature-pollution synergies. Recent publications (2023-2025) reveal dominant trends in mortality-birth outcome studies using large-scale datasets (>4 million records), with 60% focusing on Iranian populations. Key themes include heat-night mortality associations, greenness-health interactions, and temporal shifts in temperature-mortality relationships under climate change. Methodologically, she employs distributed lag models, causal inference frameworks, and multi-stage modeling. As part of the University of Münster's Climatology Group, she contributes to research on surface-atmosphere exchange processes, aerosol particles, urban climate systems, and air pollution dynamics. The group maintains active weather monitoring infrastructure and collaborates internationally on climate-health projects.

Official University Profile

Karolos GrigoriadisView profile

Professor

University of HoustonUnited States

Karolos Grigoriadis is the Moores Professor of Mechanical Engineering at the University of Houston, where he also serves as Department Chair of Mechanical and Aerospace Engineering and Director of the Aerospace Engineering Program. His research spans robust control, parametric uncertainty analysis, subsea engineering, and biomedical control systems, with applications in aerospace, petroleum engineering, and medical device development. Systems Optimization and Uncertainty Analysis Robust and Fault Tolerant Control Subsea Systems Diagnostics and Optimization Biomedical Modeling and Control His recent publications focus on linear parameter varying (LPV) systems, multiphase flow modeling, and secure control strategies for industrial and aerospace applications. He has developed specialized tools for automated blood pressure regulation and subsea pipeline integrity analysis. Grigoriadis' work integrates theoretical control systems research with practical applications in energy systems, robotics, and biomedical engineering. He leads the University of Houston's Subsea Engineering Academic Advisor office and collaborates on advanced modeling projects across multiple domains.

Official University Profile