Physics-Based Signal Processing Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۶۹۱-۷۰۵ از ۵٬۵۰۳ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Andrew N. Jordanمشاهده پروفایل

استاد

University of RochesterUnited States

Professor Andrew N. Jordan is a Full Professor of Physics at the University of Rochester. He holds affiliations with the Center for Quantum Information, Center for Coherence and Quantum Optics, and Institute for Quantum Studies at Chapman University. He received his BS in Physics and Mathematics (1997) from Texas A&M University and his PhD in Theoretical Physics (2002) from the University of California, Santa Barbara. His postdoctoral work included a fellowship at the University of Geneva (2002–2005) and a Research Scientist position at Texas A&M (2005–2006). He joined the University of Rochester in 2006, advancing to Associate Professor (2012) and Full Professor (2015). His research focuses on quantum optics, condensed matter physics, and nanophysics, with emphasis on quantum measurement theory, quantum information, and statistical physics. Notable contributions include studies on superoscillations, weak measurements, and quantum thermodynamics. He co-authored the textbook Quantum Measurement: Theory and Practice (2024) and holds patents in quantum refrigeration and sensing technologies. Recent work explores superresolution imaging, optimal quantum control, and entanglement dynamics in open systems. His articles span foundational quantum theory and applied technologies, including radar ranging, quantum error correction, and AI-driven quantum systems. Awards and grants are not explicitly listed, but his research is supported by grants from institutions like the National Science Foundation. Collaborations include teams at Chapman University, Texas A&M, and international partners. His lab designs quantum sensors and refrigeration systems, advancing both theoretical and experimental quantum science.

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Sharon M Weissمشاهده پروفایل

استاد

Vanderbilt UniversityUnited States

Sharon M Weiss is the Cornelius Vanderbilt Professor of Engineering and holds joint appointments in Electrical Engineering, Materials Science & Engineering, and Physics at Vanderbilt University's School of Engineering. She directs the Vanderbilt Institute of Nanoscale Science and Engineering. Her research focuses on light-matter interaction, silicon photonics, porous silicon biosensors, and nanotechnology. She earned a B.S., M.S., and Ph.D. in Optics from the University of Rochester. Her work spans advanced photonic crystal designs, ultra-sensitive biosensors, and radiation-tolerant optical components for aerospace applications. Recent projects include photonic metacrystals for high-Q cavities and porous silicon sensors for rapid diagnostics. She has pioneered integration of phase-change materials like VO₂ in silicon photonics for ultrafast optical switching. Publications emphasize subwavelength photonics, biosensing innovations, and space-qualified optoelectronics. Her lab develops hybrid waveguides, nanobeam cavities, and AI-enhanced sensing systems. Collaborations include NASA and industry partners for biomedical and defense applications.

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James Westمشاهده پروفایل

استاد

Johns Hopkins UniversityUnited States

James West is a Professor of Electrical and Computer Engineering and Mechanical Engineering at Johns Hopkins University's Whiting School of Engineering. He is renowned globally as co-inventor of the foil electret microphone, foundational to 90% of modern microphones. With over 60 U.S. patents and 200+ foreign patents, his career spans Bell Laboratories (1962–2001), where he pioneered polymer foil electret technologies. At Johns Hopkins since 2001, he focuses on teleconferencing acoustics, wearable health sensors, and energy harvesting. His research emphasizes biomedical applications like lung sound monitoring and telemedicine integration. Education: B.S. in Physics from Temple University (1957). Awards include National Inventors Hall of Fame Induction. He actively mentors students, advocating for diversity in STEM. Key research areas include medical device innovation, acoustic transducers, and sustainable energy systems. Key Contributions: Electret microphone technology, lung sound sensors, and hybrid energy harvesters. Recent Focus: Acoustic-based disease detection (e.g., COVID-19 screening) and telehealth sensor integration. Labs/Teams: Healthcare Acoustics Research Team (HART), collaborating on medical acoustics and sensor development. His work bridges academic and industrial innovation, emphasizing interdisciplinary collaboration. Current projects address noise suppression in clinical settings and wearable health monitoring systems.

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Leslie Greengardمشاهده پروفایل

استاد

New York UniversityUnited States

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

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Riccardo Simionatoمشاهده پروفایل

پژوهشگر ارشد

University of OsloNorway

Riccardo Simionato is a Research Fellow in the Department of Musicology at the University of Oslo (UiO), affiliated with the Faculty of Humanities. He holds a MSc and BSc in Computer Science and Information Engineering from the University of Padova, Italy, and conducted research at Aalto University, Finland (2017–2018). His research focuses on nonlinear audio modeling using deep learning, particularly addressing low-latency interactive solutions for acoustic and electronic musical instruments/devices. Education: 2018: MSc in Computer Science Engineering, University of Padova 2015: BSc in Information Engineering, University of Padova Research Interests: Deep Learning , Audio Modeling , and Sound Synthesis . He explores how deep learning can approximate complex nonlinear phenomena in audio systems, balancing computational efficiency with interpretability. Recent work emphasizes hybrid neural-audio effects, time-variant systems, and physics-informed methods for piano modeling. Publications highlight advancements in optical compressor modeling, piano analysis, and tools for generating audio effect datasets. His work bridges machine learning and music technology, aiming for practical applications in real-time audio processing and electronic instrument emulation. Grants and advising: No specific grants or student advisees listed in the provided text. Collaborations include projects with Prof. Stefano Fasciani and teams at UiO’s Department of Musicology. Labs/Teams: Active within UiO’s Sound and Music Computing research group, focusing on interdisciplinary projects combining computer science and music technology.

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Andrew McPhersonمشاهده پروفایل

استاد

Queen Mary University of LondonUnited Kingdom

Andrew McPherson is a Professor of Musical Interaction at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science and the Centre for Multimodal AI. He leads the Augmented Instruments Laboratory, a research team focused on music technology and interdisciplinary collaboration. His work bridges electrical engineering, composition, and human-computer interaction, emphasizing the creation of new tools for musicians through hardware/software interfaces and expressive performance modeling. McPherson's research interests include augmented instruments, digital signal processing, and the design of intuitive musical interfaces. He has pioneered projects like the Bela embedded platform and the Magnetic Resonator Piano, emphasizing practical applications in traditional and experimental music venues. Collaborations with artists and industry inform his approach, ensuring research outputs are artistically relevant and accessible. Education & Expertise: Trained in electrical engineering and composition, McPherson combines technical proficiency with artistic sensibility. His lab’s projects, such as the TouchKeys and hackable instruments, highlight his focus on democratizing music technology. Grants & Awards: He holds a Royal Academy of Engineering Senior Research Fellowship (2021–2026) and an ERC Consolidator Grant (2023–2027). He co-leads the UKRI Centre for Doctoral Training in Artificial Intelligence and Music, fostering future researchers in AI-driven music innovation. Labs/Teams: The Augmented Instruments Lab collaborates across disciplines, with dual affiliation at Queen Mary and Imperial College London’s Dyson School of Design Engineering. The lab’s work spans from foundational research to industry partnerships, including spinout company Augmented Instruments Ltd, which supports maker communities and industry consultancy.

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Rasmus Waagepetersenمشاهده پروفایل

استاد

Aalborg UniversityDenmark

Rasmus Waagepetersen is a Professor in the Department of Mathematical Sciences at Aalborg University, affiliated with The Faculty of Engineering and Science. His research focuses on spatial statistics, quantitative genetics, and statistical methodology for spatial point processes. He leads and participates in interdisciplinary projects such as urbanLab (spatial data analysis for urban planning) and studies on microbiome interactions in agricultural systems. Key research areas include spatial point processes, Markov chain Monte Carlo methods, and statistical inference for complex ecological and biomedical data. His work frequently involves collaborations with environmental and biological scientists, as seen in projects analyzing root microbiota assemblies in legumes and climate data for building simulations. Waagepetersen has contributed to methodological advancements in spatial statistics, including goodness-of-fit tests, likelihood-based inference for log Gaussian Cox processes, and quasi-likelihood approaches for case-control point pattern data. His research has been supported by grants from institutions like the Villum Foundation. Key Projects: urbanLab, Klimadata til fugtsimuleringer, Nod factor signaling in plant microbiota. Grants: Multiple projects funded by the Villum Foundation and Danish research councils. His recent publications address topics such as space-time point processes, microbiome data analysis, and statistical modeling in education. Waagepetersen maintains an active research group and collaborates internationally on both theoretical and applied statistical problems.

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MATTHEW DZIECIUCHمشاهده پروفایل

پژوهشگر

University of California, San DiegoUnited States

Matthew Dzieciuch is a Researcher at the Institute of Geophysics and Planetary Physics, part of the Scripps Institution of Oceanography at the University of California San Diego. His work focuses on ocean acoustic tomography and climate monitoring through ocean acoustics. He holds degrees from the University of Michigan (B.S., M.S., PhD) and has contributed to major Arctic and global ocean acoustics research initiatives. Key research areas include acoustic propagation in polar regions, Arctic climate change impacts, and the integration of acoustic networks into ocean observing systems. He has led and participated in large-scale experiments like CANAPE (Canada Basin Acoustic Propagation Experiment) and CAATEX (Coordinated Arctic Acoustic Thermometry Experiment), focusing on tomographic arrays, glider-based acoustics, and long-range acoustic measurements. His publications emphasize acoustic travel-time analysis, sea ice dynamics, and the interplay between environmental conditions and sound transmission. Dzieciuch collaborates with institutions globally to advance acoustic-based oceanography and climate monitoring, leveraging autonomous platforms like gliders and buoyancy-driven vehicles.

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Luana Ruizمشاهده پروفایل

استادیار

Johns Hopkins UniversityUnited States

Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships

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Ulrich Parlitzمشاهده پروفایل

استاد مدعو

University of GöttingenGermany

Ulrich Parlitz is an Adjunct Professor of Physics at Georg-August-University Göttingen and a Scientist leading the Biomedical Physics Group at the Max Planck Institute for Dynamics and Self-Organization. His research focuses on nonlinear dynamics, chaos theory, and biomedical applications, particularly in cardiac dynamics and excitable media. He has held visiting positions at institutions like UC San Diego and the Santa Fe Institute. Education: 1987 PhD in Physics, Georg-August-University Göttingen 1984 Diploma in Physics, Georg-August-University Göttingen Research Interests: Analysis of nonlinear systems (neurons, lasers, oscillators) Bifurcation and chaos phenomena Data-based modeling and synchronization control Wave dynamics in excitable media (e.g., cardiac arrhythmias) Fractal dimension estimation and reservoir computing Labs/Teams: Leads the Biomedical Physics Group at MPI-DS and contributes to the IMPRS Program in Physics of Biological and Complex Systems.

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Christian Tetzlaffمشاهده پروفایل

استاد

University of GöttingenGermany

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

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Mikael Östlingمشاهده پروفایل

استاد

KTH Royal Institute of TechnologySweden

Mikael Östling is a Professor at KTH Royal Institute of Technology, holding a position in the Division of Electronics and Embedded Systems within the School of Information and Communication Technology. He earned his MSc (1980) and PhD (1983) in engineering physics from Uppsala University. Since 1984, he has been a faculty member at KTH, serving as Deputy President (2017–2022), Dean of the School of ICT (2004–2012), and Head of the Department of Microelectronics and Information Technology (2000–2004). He has held visiting roles at Stanford University and the University of Florida. His research focuses on silicon/silicon germanium devices, wide bandgap semiconductors (e.g., silicon carbide), and high-power/high-frequency applications. He has authored over 600 papers, 10+ book chapters, and a textbook. Key achievements include co-founding TranSiC (2005), securing the ERC Advanced Investigator Grant (2009), and serving as Editor-in-Chief of IEEE Journal of Electron Devices Society (2016–2019). He is an IEEE and ECS Fellow. Östling has supervised 50 PhD theses and contributed to innovations like high-temperature silicon carbide circuits and graphene-based sensors. His work spans academic leadership, industry collaboration, and global research advisory roles in EU frameworks and the European Research Council.

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Ali Wanis Ali Elshaariمشاهده پروفایل

دانشیار

KTH Royal Institute of TechnologySweden

Ali W. Elshaari is an Associate Professor at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He holds a B.S. in Electrical Engineering from the University of Benghazi (2007) and a Ph.D. in photonics from the Rochester Institute of Technology (2011). His postdoctoral research at TU Delft’s Kavli Institute of Nanoscience focused on quantum transport. Currently, he leads the Quantum Nano Photonics Group, pioneering work in topological and quantum integrated photonics to develop high-performance circuits for communication, sensing, and metrology. His research spans hybrid quantum photonics, strain-tunable systems, and superconducting detectors, with applications in quantum communication and quantum materials characterization. Elshaari's research interests include integrating single-photon emitters into CMOS-compatible platforms, exploring quantum phenomena like entanglement and coherence, and developing advanced photonic materials (e.g., hexagonal boron nitride and Cu₂O). He has contributed to on-chip single-photon generation/filtering, strain-tunable photonic circuits, and slow-wave superconducting detectors. His work bridges experimental and theoretical approaches, leveraging imaging techniques and phase retrieval algorithms. Elshaari is an editorial board member for Nature Portfolio - Scientific Reports , Wiley Advanced Quantum Technologies , and EPJ Quantum Technology . He teaches courses in quantum technology, electromagnetism, and optical physics. His funding includes grants from the Wallenberg Foundation, Swedish Research Council, Vinnova, and the European Research Council. His lab actively recruits students for bachelor’s and master’s projects in quantum photonics and nanophotonics.

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Juergen Pfingstnerمشاهده پروفایل

پژوهشگر

University of OsloNorway

Juergen Pfingstner is a Post-doctoral Fellow at the University of Oslo (since 2015) within the Department of Physics. His research focuses on advanced accelerator technologies, particularly at CERN's CLIC Test Facility (ATF2). Key areas include emittance preservation, ground motion mitigation, and free-electron laser (FEL) design. He holds a PhD from the Vienna University of Technology (2013) and a Master's in Electrical Engineering from Graz University of Technology (2008), specializing in control engineering and electromagnetic field computation. His academic journey includes a postdoctoral stint at CERN (2012–2014), where he investigated ground motion effects on CLIC performance. Collaborations span institutions like KEK (ATF2 facility) and the X-band FEL collaboration. Research interests bridge particle accelerator physics, control systems, and high-frequency radiation technologies. Publications emphasize CLIC final focus systems, wakefield suppression, and plasma wakefield acceleration. His work addresses both theoretical and experimental challenges in next-generation collider design, including THz radiation facilities and feedback control methodologies.

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Yichuang Sunمشاهده پروفایل

استاد

University of HertfordshireUnited Kingdom

Yichuang Sun is a Professor of Communications and Electronics at the University of Hertfordshire, leading the Communications and Intelligent Systems Research Group and the Electrical and Electronic Engineering Department. He holds a PhD from the University of York (1996) and has over 450 peer-reviewed publications, 5 authored books, and 40+ supervised PhD students. His research focuses on wireless communications (5G/6G, RIS-aided systems), RF/microwave circuits, neuromorphic computing, and machine learning applications. He serves as Editor or Guest Editor for 12+ IEEE/IET journals and chairs technical committees for IEEE conferences. Notably ranked in the World's Top 2% Scientists since 2019, he also advises global institutions like Oxford and the Royal Academy of Engineering. Education: PhD in Electronics, University of York, UK (1996) Research Interests: Wireless/Mobile Communications and Networks RF and Microelectronic Circuits Neuromorphic Computing and Machine Learning Memristor-based Systems Secure and Energy-Efficient Communications Grants & Projects (selected): Advanced mmWave MIMO 4D Radar for Traffic Detection (2024–2027) Wireless Power Transfer for Implantable Medical Devices (2023–2025) RFID System for Sample Management (2021–present) Labs/Teams: Leads the Communications and Intelligent Systems Research Group, collaborating on 6G, neuromorphic hardware, and IoT systems.

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