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Lora RamunnoView profile

Professor

• Theoretical Photonics
• Nanophotonics
• Nonlinear Optics
• +3 more

Lora Ramunno is a Full Professor in the Department of Physics at the University of Ottawa, where she leads the Computational Nanophotonics Lab. She holds affiliations with the Max-Planck-UOttawa Centre for Extreme and Quantum Photonics, the NRC-UOttawa Joint Centre for Extreme Photonics, and the uOttawa Centre for Research in Photonics. She obtained her PhD in Physics from the University of Toronto in 2002, followed by a postdoctoral fellowship at uOttawa. Her research focuses on theoretical and computational approaches to photonics, with specialized interests in: Nanophotonics and nano-optics Intense laser-matter interactions Nonlinear optical microscopy High-performance computing applications Photonic device development Industrial collaborations in optical technologies Recent publications (2019-2025) demonstrate strong trends in machine learning applications for photonic design, quantum light sources, plasmonic metasurfaces, and biomedical imaging techniques. Her work frequently bridges fundamental physics with engineering applications in optical communication and sensing. Significant scientific recognitions include: Canada Research Chair in Computational Nanophotonics (2007-2017) Early Researcher Award (2007) She mentors graduate students and postdoctoral researchers through her lab and has established multiple international research collaborations. Her team utilizes advanced computational facilities for modeling light-matter interactions at nanoscales.

• Exploration Geophysics
• Artificial Intelligence
• Compressed Sensing
• +3 more

Jianwei Ma is a Professor at the School of Mathematics, Harbin Institute of Technology, leading the Center of Geophysics and the Institute of Artificial Intelligence. His expertise spans exploration geophysics, artificial intelligence, and compressed sensing. Ma holds a Ph.D. in Solid Mechanics from Tsinghua University (2002) and a BS in Engineering Mechanics from Dalian University of Technology (1998). His research focuses on improving seismic exploration through mathematical methods, including sparse transforms, compressed sensing, and deep learning applications. Notable contributions include methodologies for seismic noise reduction, data interpolation, and full waveform inversion. Ma has held visiting positions at institutions like UCLA, UC Los Angeles, and the University of Texas at Austin. He has received prestigious awards such as the NSFC Distinguished Young Scholar (2016) and World's Top 2% Scientists (2020-2024). Ma actively contributes to professional societies, including SEG and IEEE, and has organized international workshops on mathematical geophysics and AI-driven geoscience. His team includes faculty members and over 30 graduate students, reflecting his commitment to training future researchers in geophysics and AI. Collaborations with global leaders in geophysics and mathematics, such as Prof. Stanley Osher (UCLA) and Prof. Gerlind Plonka (University of Goettingen), highlight his interdisciplinary approach. Key publications include advancements in deep learning for geophysical inversion and sparse representation techniques for seismic data processing.

Daniele SalvatiView profile

Associate Professor

• Audio Signal Processing
• Acoustic Signal Processing
• Computer Audition
• +13 more

Daniele Salvati is an Associate Professor in the Department of Mathematics, Computer Science and Physics at the University of Udine, Italy. He is actively involved in research, teaching, and editorial roles in leading signal processing journals. Associate Professor, University of Udine Ph.D. in Acoustic Source Localization, University of Udine (2012) Senior Member, IEEE Associate Editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing Handling Editor, Signal Processing His research focuses on audio and acoustic signal processing , particularly in the areas of source localization, speaker identification, and computer audition. He integrates traditional signal processing with modern machine learning and deep learning techniques to develop robust systems for real-world environments. His recent publications demonstrate a strong trend toward end-to-end deep learning models using raw waveforms, beamforming optimization (especially diagonal unloading), and applications on mobile and drone-based platforms . His work spans both journal and conference venues, with a focus on IEEE and ISCA publications. Scientific recognition includes: National Scientific Qualification as Full Professor (IINF-05/A) Senior Member of IEEE Editorial board roles in top journals He mentors doctoral students as part of the Ph.D. program in Computer Science and Artificial Intelligence and collaborates widely with researchers such as Carlo Drioli and Gian Luca Foresti. His research is supported by publications in high-impact journals and participation in international conferences. He leads work in the G.L. Foresti's Lab, focusing on intelligent systems and signal processing. His work includes developing microphone array-based interfaces for music and human-computer interaction, acoustic scene analysis, and automated sensor configuration. Future directions likely involve further integration of AI with physical sensor systems, especially in autonomous and mobile contexts.

Christian GroßeView profile

Professor

• Non-Destructive Testing
• Civil Engineering
• Materials Science
• +5 more

Prof. Christian U. Große is a full Professor at the Chair of Non-Destructive Testing (ZfP) at the Technical University of Munich (TUM), part of the TUM School of Engineering and Design and the Department of Materials Engineering. His research focuses on non-destructive testing (NDT) methods applied to civil engineering, materials science, and cultural heritage preservation. He leads the ZfP team, which develops innovative techniques such as air-coupled ultrasound and structural health monitoring systems. Education: Studied geophysics at Karlsruhe University, earned his doctorate and habilitation at Stuttgart University. Served as a visiting scholar at UC Berkeley (2005-2006). Joined TUM in 2010 with a joint appointment across civil, mechanical, and geo-environmental engineering faculties. Since 2011, he has held the full professorship at ZfP and manages the Center for Building Materials and Materials Testing (cbm). Research Interests: Develops NDT methods for infrastructure monitoring, lightweight materials (CFRP), biomedical applications, and archaeological projects like the ScanPyramids initiative in Egypt. Collaborates internationally, including with Cairo University and the National University of Oil and Gas in Ukraine on structural safety assessments. Publications: Over 150 peer-reviewed articles, including key works on ultrasonic imaging, pyramid corridor detection, and material characterization. Recent work focuses on AI-driven defect detection and cross-entropy optimization for Lamb wave analysis. Awards: Von Humboldt Prize (2025), Deutsches Museum Board membership (since 2018), Cairo University Award for pyramid research. Grants/Projects: Leads EU-funded BrückenCT, PROVILAS (laser scanner models), and TUM-CU cultural heritage preservation initiatives. Labs/Teams: Directs ZfP’s labs specializing in ultrasound, thermography, and industrial CT. Supervises a team of researchers including Benedikt Maier, Olga Popovych, and Ana Eugenia Menéndez Orellana.

• Artificial Intelligence and Image Processing
• Biomedical Engineering
• Electrical and Electronic Engineering
• +3 more

Dr. Carlos Cueto Mondejar is a Lecturer in AI and Fluid Flow Imaging at the Department of Earth Science & Engineering, Faculty of Engineering, Imperial College London. His research focuses on translating seismic technology to medical ultrasound imaging of brain structure, particularly using full-waveform inversion (FWI). He leads the development of Vector Field Inversion (VFI), enabling high-resolution 3D blood flow imaging in the brain. He is the lead developer of Stride, an open-source library for high-resolution ultrasound tomography. Affiliations: Royal School of Mines (RSM 3.21), South Kensington Campus Outreach: "Seeing sound" project collaborates with Canterbury Christ Church University to communicate ultrasound imaging principles through art Research interests include AI-driven medical imaging innovations, with applications in stroke and neuropathology diagnosis. His work bridges engineering and clinical sciences through interdisciplinary approaches. Awards: EPSRC Doctoral Prize Fellowship (2022) He actively engages in public outreach initiatives like Imperial Lates and the ARTxScience festival, emphasizing accessible science communication.

• Geophysics
• Earth Science
• Planetary Science
• +3 more

Harriet Lau , Manning Assistant Professor of Earth, Environmental, and Planetary Sciences at Brown University , specializes in geodynamics, mantle rheology, and planetary evolution. Her research bridges seismic, geodetic, and geological observations to model Earth and planetary internal processes. Research Interests : Earth's mantle and core interactions Transient rheology and viscoelastic deformation Sea-level rise and glacial isostatic adjustment Planetary migration and Kuiper Belt dynamics Seismic tomography and normal mode analysis Geodynamic implications for tectonics and climate Article Trends : Her recent work spans mantle convection, ice sheet collapse, and planetary dynamics, with methodological advances in adjoint sensitivity, tidal tomography, and rheological modeling. Key themes include Earth-Moon co-evolution, deep mantle buoyancy, and transient deformation during deglaciation. Teaching : EEPS 0820 - Dynamic Earth EEPS 1470 - Sea Level Rise (And Fall)

Birsen YaziciView profile

Professor

• Statistical signal processing
• Inverse problems in imaging
• Image reconstruction
• +7 more

Birsen Yazici is a Full Professor in the Department of Electrical, Computer and Systems Engineering and the Department of Biomedical Engineering at Rensselaer Polytechnic Institute. She has made significant contributions to statistical signal processing, inverse problems in imaging, and radar remote sensing. Her work spans theoretical developments in microlocal analysis for imaging applications to practical implementations in synthetic aperture radar and biomedical optics. Her educational background includes: Ph.D in Electrical Engineering from Purdue University (1994) MS in Mathematics from Purdue University (1990) Dual BS degrees in Electrical Engineering and Mathematics from Bogazici University, Istanbul, Turkey (1988) Professor Yazici's research focuses on statistical signal processing, inverse problems in imaging, and image reconstruction techniques. She has pioneered work in synthetic aperture radar imaging, particularly with ultra-narrowband waveforms and passive radar systems. Her biomedical imaging research includes advances in diffuse optical tomography and fluorescence imaging for cancer detection. She has made significant contributions to understanding artifacts in imaging systems and developing robust reconstruction algorithms that account for noise and clutter. Her recent publications demonstrate a clear trajectory toward integrating deep learning with traditional signal processing techniques for phase retrieval and phaseless imaging. This represents a significant shift in her research focus toward leveraging machine learning to solve challenging inverse problems in radar and medical imaging where phase information is unavailable or corrupted. Her scientific achievements have been recognized with several prestigious awards: Elevated to IEEE Fellow (2020) Distinguished Lecturer of the IEEE Aerospace and Electronics Systems Society (2020-2022) Rensselaer Polytechnic Institute School of Engineering Research Excellence awards (2007, 2013) 2nd Best paper award from IEEE Transactions in Industrial Applications Professor Yazici has advised numerous graduate students throughout her career, with current Ph.D. students including Samia Binte Kazemi, Sean Thammakhoune, and Nazia Afroz Choudhury. Her research has been generously supported by multiple federal agencies including NSF, DARPA, NIH, and various defense research organizations (AFOSR, AFRL, ONR, NRL). She has served as an associate editor for several prestigious journals including IEEE Transactions on Computational Imaging, IEEE Transactions on Geoscience and Remote Sensing, SIAM Journal on Imaging Science, and IEEE Transactions on Image Processing. Her laboratory focuses on developing advanced signal processing algorithms for radar imaging systems and biomedical applications. The research group maintains strong collaborations with government laboratories and industry partners to translate theoretical advances into practical imaging systems.

Rie NakataView profile

Assistant Professor

• Seismology
• Geophysics
• Computational Geophysics
• +3 more

Rie Nakata is a Research Scientist at LBL (2021–present) and Assistant Professor at the Earthquake Research Institute of the University of Tokyo. She holds a PhD in seismic full waveform inversion from the University of Western Ontario and has held postdoctoral and research positions at institutions including the University of Western Australia (2013–2018), University of Oklahoma (2018), and MIT (2019). Her research spans seismology , geophysics , and computational geophysics , with a focus on seismic imaging and waveform inversion techniques to study Earth structure and earthquake dynamics.

• Seismology
• Applied Geophysics
• Marine Geophysics
• +4 more

Laura Gaßner is a Researcher at the Geophysical Institute (GPI) of the Karlsruhe Institute of Technology (KIT) , specializing in Applied Geophysics . Her work bridges seismic inversion techniques, wind turbine-induced ground motion analysis, and marine geophysics. Education : Master's and PhD (2019) from KIT, with a dissertation on full-waveform inversion for gas-hydrate imaging. Research Interests : Modeling and inversion of seismic wave fields for subsurface exploration Seismic and acoustic emissions from wind turbines in low mountain ranges Marine seismics for gas-hydrate and deep-sea fan characterization Ground motion analysis and its societal impacts Integration of multidisciplinary data for environmental impact studies Computational geophysics with finite-difference and inversion methodologies Scientific Trends : Her publications focus on wind turbine seismo-acoustic emissions, gas-hydrate imaging via full-waveform inversion, and fluid-induced microseismicity. Key keywords include Seismology , Environmental Geophysics , Marine Geophysics , and Computational Modeling , with sub-fields like Wind Turbine Seismology , Gas Hydrate Systems , and Fault Reactivation Analysis .

Gabriel Fabien-OuelletView profile

Assistant Professor

• Applied Geophysics
• Environmental Geophysics
• Seismic Inversion
• +7 more

Gabriel Fabien-Ouellet is an Assistant Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. His research focuses on developing advanced geophysical methods using computational approaches, including seismic modeling, full waveform inversion, and machine learning applications for geophysical data processing. Education: Postdoctoral training in Geophysics - Stanford University Ph.D. in Earth Sciences - INRS M.Sc. in Earth Sciences - Université Laval B.Sc. in Physics - Université Laval B.Sc. in Physical Engineering - Université Laval His primary research interests include multi-parameter full waveform inversion for subsurface characterization, automated seismic processing using deep learning, and seismic attenuation analysis for permafrost characterization. His work bridges geophysical theory with high-performance computing implementations on GPU architectures. Recent publications demonstrate trends in machine learning applications for geophysical data processing, multi-method geophysical analysis for environmental monitoring, and advanced seismic techniques for climate-related permafrost studies. His research consistently integrates computational innovation with practical geophysical applications. He leads the development of SeisCL , an open-source software for seismic modeling and inversion on heterogeneous computing platforms. His research is supported by the Canada Foundation for Innovation and industrial partnerships, including an NSERC Industrial Chair in geothermal energy integration for institutional buildings.

Giorgio KaniadakisView profile

Professor

• Statistical Physics
• Theoretical Physics
• Complex Systems
• +4 more

Giorgio Kaniadakis is a Full Professor at the Department of Applied Science and Technology (DISAT) of Politecnico di Torino. His research focuses on generalized statistics, theoretical physics, and complex systems, with a strong emphasis on κ-statistics and power-law distributions. Research interests include: κ-Statistics, Power-law Distributions, Quantum Computation, Nonlinear Kinetics, Statistical Physics of Complex Systems His recent publications (2024-2016) span urban scaling laws, epidemiological models, predator-prey dynamics, and quantum statistical mechanics, often applying κ-statistics to interdisciplinary problems in physics, biology, and economics. He has organized major conferences like SigmaPhi2014, SigmaPhi2011, and SigmaPhi2008 as Program Chair. He actively teaches courses in statistical mechanics and physics at both PhD and undergraduate levels. Key teams/groups: Institute of Fundamental Physics and Materials for Nanotechnology (DISAT), Generalized Statistical Mechanics of Complex Systems

• Medical Imaging
• Biomedical Engineering
• Image Reconstruction
• +3 more

Mark A Anastasio is a Donald Biggar Willett Professor of Bioengineering, Professor at the Siebel School of Computing and Data Science, and holds joint appointments in Electrical and Computer Engineering, Biomedical and Translational Sciences, and Clinical Sciences. He leads the Bioengineering department and is affiliated with the Coordinated Science Lab and Beckman Institute. His research focuses on advanced medical imaging technologies, including photoacoustic computed tomography, ultrasound computed tomography, and deep learning-driven image reconstruction methods. Anastasio has contributed to over 400 publications, including seminal works on improving imaging resolution and accuracy through computational techniques. He has been honored with prestigious fellowships from AIMBE, IEEE, and SPIE, and his work bridges engineering, biology, and clinical applications. Key research areas include developing novel algorithms for image reconstruction, optimizing imaging systems using Bayesian methods, and integrating AI for medical diagnostics. His datasets, such as 2D acoustic breast phantoms and clinical imaging trials, are widely used in computational studies. Anastasio collaborates across disciplines to advance imaging for cancer detection, neuroimaging, and functional tissue analysis. His awards reflect recognition for pioneering contributions to biomedical optics and computational imaging. Recent projects include optimizing MRI reconstruction with learned ideal observers and evaluating generative models for medical image statistics. Anastasio actively contributes to academic leadership roles and editorial boards, ensuring scientific rigor and innovation in imaging science.

Tristan van LeeuwenView profile

Professor

• Imaging Reconstruction
• Inverse Problems
• Scientific Computing
• +4 more

Tristan van Leeuwen is a Professor of Computational Inverse Problems at Utrecht University's Faculty of Science, within the Mathematical Institute's Department of Mathematical Modeling. He holds a MSc in Computational Science (2006) and a PhD in Geophysics (2010). His career includes postdoctoral roles at the University of British Columbia and Centrum Wiskunde & Informatica (CWI), followed by faculty positions at Utrecht University and group leadership at CWI. His research focuses on inverse problems, scientific computing, imaging reconstruction, and computational methods for geophysics and medical imaging. Key areas include wave-equation inversion, tomographic reconstruction, and uncertainty quantification. Notable contributions span seismic inversion techniques, convex optimization frameworks for tomography, and deep learning applications in imaging. His publications emphasize methodologies like wavefield reconstruction inversion, convex programming for shape sensing, and Bayesian approaches for uncertainty analysis. He collaborates with institutions like CWI and the University of British Columbia, contributing to open-source tools like Tomosipo for tomography. His work bridges theoretical mathematics with practical applications in geophysics, medical diagnostics, and industrial inspection.

Graham KentView profile

Professor

• Seismology
• Geophysics
• Fault Hazard Analysis
• +5 more

Dr. Graham Kent is a Professor in the Department of Geological Sciences and Engineering at the University of Nevada, Reno (UNR), within the Mackay School of Earth Sciences and Engineering. He holds a Ph.D. from Scripps Institution of Oceanography (1992) and has extensive postdoctoral experience at Woods Hole Oceanographic Institution. His research focuses on seismic hazards, fault dynamics, volcanic systems, earthquake early warning systems (e.g., Alert Wildfire), tsunami risk, and geophysical imaging of tectonic structures. Key projects include mapping fault hazards in the Walker Lane, assessing the San Andreas Fault's recurrence intervals, leading the $15M offshore Southern California fault hazard mapping project, and developing the ALERTWildfire camera network for wildfire detection. He spearheaded Nevada’s Great ShakeOut earthquake drills and has contributed to global efforts in marine geophysics, including studies at Axial Seamount on the Juan de Fuca Ridge. Scientific contributions include over 150 publications on seismic imaging, fault mechanics, and volcanic processes. He has received recognition as a NASA Astronaut Finalist (1996) and Valedictorian of his undergraduate class (San Diego State University, 1985). Current initiatives involve expanding ALERTWildfire to cover Western U.S. wildfire-prone regions, integrating seismic and wildfire monitoring networks, and advancing understanding of continental rifting in the Salton Trough. His work bridges geophysical research with practical applications in disaster preparedness and infrastructure resilience.

Gregory L. DaviesView profile

Researcher

• Geophysics
• Mechanical Engineering
• Materials Science
• +3 more

Gregory L. Davies is a Postdoctoral Research Associate in the Theoretical & Computational Seismology group at Princeton University, focusing on Geophysics. He joined the group in December 2018. Education: Gregory holds a Ph.D. in Mechanical and Aerospace Engineering from Princeton University. Research Interests: His work centers on the application of ultrasound as a characterization tool for battery energy storage systems. He investigates ultrasound imaging techniques and employs geoscientific methods like full waveform inversion at the mm-scale. Davies also develops computational algorithms to interface experimental ultrasonic data and create models for ultrasonic hardware, including transducers and material characterization. Advising and Grants: Gregory does not currently have listed advisees or grants associated with his profile. Labs/Teams: Gregory is part of the Theoretical & Computational Seismology group at Princeton University, where he collaborates on research involving advanced geophysical modeling and computational seismology.