جست‌وجوی استاد و پژوهشگر

• Geophysics
• Seismology
• Full-Waveform Inversion
• +۵ مورد دیگر

Bao Xueyang is an Assistant Professor at the Department of Earth and Space Sciences (Faculty of Science) of Southern University of Science and Technology (SUSTech) , where he has worked since January 2019. His academic background includes a Ph.D. in Geology from the University of Missouri-Columbia (2011) , M.S. and B.S. degrees in Geophysics from the University of Science and Technology of China . Ph.D., Geological Sciences, University of Missouri-Columbia (2011) M.S., Earth and Space Sciences, University of Science and Technology of China (2005) B.S., Earth and Space Sciences, University of Science and Technology of China (2001) His research focuses on seismological full-waveform inversion , seismic wave attenuation measurement , and geophysical studies of tectonically active regions such as the Tibetan Plateau and Tethys domain. Methodologically, he develops techniques for: 3D full-waveform inversion Joint seismic-gravity inversion Seismic wave attenuation anisotropy Background noise tomography Wavefield simulation on complex topographies Seismic source parameter inversion Analysis of his recent publications reveals expertise in full-waveform sensitivity kernel development , ocean-bottom seismology , and cratonic lithosphere thermal structure through Rayleigh wave attenuation studies. His work spans both theoretical advancements in inversion algorithms and practical applications in energy resource characterization and tectonic studies. Scientific recognition includes: 2019 Shenzhen Overseas High-Level Talent 2022 Excellence Awards in Reviews of Geophysics and Planetary Physics He leads multiple National Natural Science Foundation projects while co-leading international research initiatives. His teaching portfolio includes undergraduate and graduate courses on geophysical inversion theory, covering both linear and nonlinear inversion methods with machine learning applications.

Jianliang Qianمشاهده پروفایل

استاد

• Inverse Problems
• Scientific Computing
• Numerical Analysis
• +۷ مورد دیگر

Jianliang Qian is a Professor at the Department of Mathematics and Department of Computational Mathematics, Science and Engineering at Michigan State University , East Lansing, MI. He earned his Ph.D. from Rice University and has held academic appointments at MSU since 2005, including roles as Assistant, Associate, and Full Professor. Since 2015, he has served as Director of the Michigan Center for Industrial and Applied Mathematics . Research Interests: His work focuses on big-data analysis via inverse problems , fast algorithms for geophysical and medical imaging , microlocal analysis , numerical methods for PDEs , and optimal control . Key applications include high-frequency wave modeling, seismic inversion, and genome architecture reconstruction. Article Trends: Recent publications emphasize high-frequency wave propagation , level-set inversion techniques , machine learning integration into wave modeling, tensor methods for high-dimensional problems, and adaptive algorithms for Helmholtz and Hamilton-Jacobi equations. Leadership: As Director of the Michigan Center for Industrial and Applied Mathematics, he organizes seminars and workshops, including the Conference on Wave Challenges (2015) and MCIAM Seminars series.

• Numerical Analysis
• Applied Mathematics
• Magnetic Refrigeration
• +۳ مورد دیگر

Bahodir Siddikov serves as Professor in the Department of Mathematics within Ferris State University's College of Arts, Sciences and Education. His academic foundation includes dual doctoral qualifications: a PhD in Mathematics from the University of Wisconsin and a PhD in Applied Mathematics from Kiev University, complemented by BS and MS degrees in Applied Mathematics from Kiev University. His educational credentials are detailed as follows: PhD in Mathematics, University of Wisconsin PhD in Applied Mathematics, Kiev University MS in Applied Mathematics, Kiev University BS in Applied Mathematics, Kiev University Siddikov's research centers on numerical methods for partial differential equations with engineering applications, particularly in magnetic refrigeration systems and hydroacoustic signal processing. His work bridges theoretical mathematics with thermal physics and fluid dynamics, evidenced by publications spanning computational simulations of regenerators, heat capacity modeling in magnetic materials, and inverse problem solutions for wave propagation. Analysis of his publication timeline reveals evolving research trajectories: early work (1989) addressed hydroacoustic signal inversion, while mid-career research (2002-2005) focused on magnetic regenerator simulations. His most recent publications (2010-2012) demonstrate advanced numerical techniques for singular perturbation problems and material property modeling, alongside pedagogical contributions to calculus education through computational software development. He maintains active scholarly engagement through presentations at major international forums including SIAM's 50th Anniversary Conference, International Conference on Mathematics in Istanbul, and IEOM conferences across Asia and Hawaii, covering topics from Mathematica-based numerical analysis instruction to magnetic refrigeration advancements.

David Bourneمشاهده پروفایل

دانشیار

• Optimal Transport Theory
• Semi-discrete Optimal Transport
• Computational Geometry
• +۷ مورد دیگر

David Bourne is an Associate Professor in the Department of Mathematics at Heriot-Watt University, located within the School of Mathematical and Computer Sciences. He joined Heriot-Watt University in 2018 after holding academic positions at Durham University and the University of Glasgow. Dr. Bourne received his undergraduate degree from Durham University (UK), followed by a PhD from the University of Maryland, College Park (USA) under the supervision of Stuart S. Antman. He completed postdoctoral work with Stefan Müller in Leipzig and Bonn (Germany) and with Mark Peletier in Eindhoven (the Netherlands). Dr. Bourne's research focuses on optimal transport theory and its applications . His work spans several interdisciplinary areas including computational geometry, materials science, meteorology, and energy-driven pattern formation. Specifically, he investigates semi-discrete optimal transport theory and its applications to synthetic microstructure generation in materials science, weather modeling through the semi-geostrophic equations, and pattern formation in crystallization processes. His research employs techniques such as Laguerre tessellations and power diagrams to solve complex problems in these domains. His recent publications demonstrate a strong trend toward applying optimal transport methods to solve complex problems in materials science and meteorology. The research shows increasing sophistication in handling anisotropic structures and developing efficient computational algorithms for geometric modeling of polycrystalline materials and weather systems. Dr. Bourne maintains an active research group and has developed software libraries related to his work, as indicated on his personal webpage (www.macs.hw.ac.uk/~db92/). His ORCID identifier is https://orcid.org/0000-0001-7515-8329.

TOLGA ULAŞ GÜRBÜZمشاهده پروفایل

دانشیار

• Electromagnetic Scattering
• Microwave Imaging
• Antenna Technologies
• +۴ مورد دیگر

Tolga Ulaş Gürbüz serves as Associate Professor in the Computer Engineering Department at Gaziantep University's Faculty of Engineering, Turkey. Appointed as Doctor Lecturer in 2015 and promoted to Associate Professor in 2021, he previously held roles as Research Engineer at Mitos Medikal Teknolojiler (2010-2015) and IT Specialist at the Ministry of Customs and Trade (2002-2010). His academic leadership includes Department Chairman tenure from 2016-2018. Education: Ph.D. in Satellite Communications and Remote Sensing, Istanbul Technical University (2007-2014) M.Sc. in Electronics and Communication Engineering, Istanbul Technical University (2001-2007) B.Sc. in Electrical and Electronics Engineering, Yeditepe University (1997-2001, Full Scholarship) Research Focus: Dr. Gürbüz specializes in computational electromagnetic methods for scattering analysis and imaging applications. His work develops semianalytical, meshless, and series solutions for electromagnetic wave interactions with complex structures—particularly multilayer cylindrical geometries. Key application areas include microwave breast cancer imaging using realistic anatomical models and subsurface detection of buried objects beneath rough surfaces. Publication Trends: His 12 journal publications (2014-2025) reveal consistent advancement in electromagnetic scattering theory, with increasing sophistication in handling complex material properties (ferrites, dispersive media) and geometries. Recent work emphasizes medical imaging applications, evidenced by the 2025 IEEE Antennas and Wireless Propagation Letters paper on eigenfunction expansion methods for ferrite-layered cylinders. Professional Activities: An IEEE member since 2012, he conducted TÜBİTAK 2219-funded research at Duke University on microwave breast cancer imaging. His teaching portfolio includes Electromagnetics II, Computer Programming, and Physics courses for engineering students since 2015.

• Biomedical Engineering
• Bioelectronics
• Artificial Intelligence
• +۳ مورد دیگر

Rüştü Murat Demirer serves as an Assistant Professor in the Department of Electrical and Electronics Engineering at Işık University's Faculty of Engineering and Natural Sciences. His academic career spans decades with active teaching responsibilities including Biomedical Engineering courses such as Clinical Care Informatics, Biosignal Processing, and Medical Imaging since at least 2012 across multiple institutions including Işık University and Bahçeşehir University. His educational foundation includes: PhD in Biomedical Engineering from Boğaziçi University (1983-2002) MS in Energy from Istanbul Technical University (1980-1982) BS in Electronics and Communications Engineering from Kocaeli University (1976-1980) Dr. Demirer's research integrates Biomedical Engineering with cutting-edge computational neuroscience, focusing on Bioelectronics, Artificial Intelligence applications, and Neuroscience. He pioneers methodologies for analyzing brain dynamics through EEG/ECoG signal processing, entropy-based biomarker development, and machine learning algorithms for neurological and psychiatric conditions. His work bridges theoretical neuroscience with clinical applications in epilepsy, bipolar disorder, and brain-computer interfaces. Analysis of his publication trends reveals strong interdisciplinary convergence between neuroscience, biomedical engineering, and artificial intelligence. Key methodological themes include Hilbert transform applications, nonlinear dynamics in brain signals, entropy quantification for psychiatric diagnostics, and hybrid machine learning approaches for medical signal classification. This research trajectory demonstrates consistent innovation in translating complex brain signal analysis into clinically relevant diagnostic tools. Dr. Demirer has actively mentored 11 graduate students (10 Master's and 1 PhD) between 2013-2025. His advisees' research spans diverse applications including: Machine learning for cybersecurity threat detection Cryptocurrency market analysis using predictive modeling EEG/eye-tracking fusion for cognitive decision studies Medical diagnostics through convolutional neural networks Natural language processing for offensive language detection He maintains professional engagement as a member of the Chamber of Electrical Engineers (Elektrik Mühendisleri Odası) while teaching specialized courses across biomedical engineering, cybersecurity, and data science domains.

• Operating Systems
• Parallel Computing
• Scheduling in Parallel Systems
• +۳ مورد دیگر

Ioannis Venetis is an Assistant Professor at the University of Piraeus, School of Information and Communication Technologies, Department of Informatics, specializing in Operating Systems and Parallel Computing. He earned his PhD from the Department of Computer Engineering and Informatics at the University of Patras. His research spans programming models for parallel systems, scheduling optimization, and applications in computational neuroscience and seismology. Research Interests Operating Systems Parallel Computing Scheduling Algorithms High-Performance Computing Computational Neuroscience Seismology Projects Participation in European and national research programs Development of Gisola (GPU-accelerated seismic inversion tool) His teaching portfolio includes courses like Operating Systems, Parallel Processing, and Symbolic Programming. Articles highlight expertise in GPU acceleration, tridiagonal solvers, sensor networks, and many-core architectures. Notable contributions include work on Chimera states in neuronal dynamics and hierarchical workload scheduling frameworks.

• Theoretical Physics
• Quantum Gravity
• Black Hole Physics
• +۴ مورد دیگر

Ivica Smolić is an Associate Professor at the Institute for Theoretical Physics of Particles and Fields within the Faculty of Natural Sciences and Mathematics at the University of Zagreb. He completed his graduation in 2004 and earned his Ph.D. in 2010. His primary research focuses on theoretical physics with specialization in quantum gravity, black hole physics, noncommutative geometry, and nonlinear electrodynamics. He teaches courses including Classical Electrodynamics, Differential Geometry in Physics, and Topology in Physics at both graduate and doctoral levels. Smolić's research explores the intersection of gravity and quantum theory through black hole thermodynamics, spacetime symmetries, and quantum spacetime phenomenology. His recent work examines noncommutative geometry's implications for gravitational waves and singularity resolution. Educational aspects of electrodynamics also feature in his research portfolio. Analysis of his publications reveals consistent focus on quantum corrections to general relativity, particularly regarding black hole properties and electromagnetic field behavior in curved spacetime. Major themes include singularity resolution, symmetry inheritance, and pedagogical challenges in relativistic electrodynamics. No scientific awards or prizes are documented. No information is available regarding student advising, research grants, laboratory leadership, or collaborative teams.

Jason Sagersمشاهده پروفایل

مدرس

• Underwater Acoustics
• Ocean Acoustics
• Arctic Acoustic Propagation
• +۵ مورد دیگر

Jason Sagers serves as a Lecturer at the University of Texas at Austin's College of Engineering, specifically affiliated with the Mechanical Engineering department and the Applied Research Laboratories (ARL:UT). His research focuses on underwater acoustics with particular emphasis on Arctic environments and seabed characterization. Dr. Sagers' research interests span multiple domains within underwater acoustics, with primary focus on Arctic acoustic propagation phenomena, geoacoustic inversion techniques, and machine learning applications for signal processing. His work extensively examines sound propagation in challenging environments such as the Chukchi Sea and Canada Basin, where he investigates how ice cover, oceanographic conditions, and seabed properties affect acoustic transmission. Recent research has incorporated convolutional neural networks for signal identification and seabed classification, demonstrating his adaptation of cutting-edge computational methods to traditional acoustics problems. His studies of seagrass meadows represent an important application of acoustics to marine ecosystem monitoring. The analysis of his recent publications reveals a clear trajectory toward integrating machine learning with traditional acoustic methodologies. His work shows increasing focus on passive acoustic monitoring using merchant ship noise as a signal source, sophisticated 3D modeling of complex underwater environments, and development of techniques for extracting meaningful information from challenging Arctic acoustic data. The recurring themes across his publications include the Canada Basin Acoustic Propagation Experiment, Northern Ocean Rapid Surface Evolution Experiment, and long-term monitoring of the Chukchi Shelf environment. Dr. Sagers has participated in significant field experiments including the Northern Ocean Rapid Surface Evolution Experiment and the Canada Basin Acoustic Propagation Experiment. His research involves deploying drifting hydrophones, surface drifters, and horizontal array systems to capture acoustic data across the Arctic. Much of his work focuses on analyzing ambient noise fields, sound propagation characteristics, and seabed properties in polar regions, with particular attention to how seasonal changes and ice cover affect acoustic transmission.

Carlos Torres-Verdínمشاهده پروفایل

استاد

• Geophysics
• Borehole Geophysics
• Well Logging
• +۱۰ مورد دیگر

Carlos Torres-Verdín is Full Professor at The University of Texas at Austin's Cockrell School of Engineering, holding the Brian James Jennings Memorial Endowed Chair and Zarrow Centennial Professorship in Petroleum Engineering. He directs the Joint Industry Research Consortium on Formation Evaluation and leads the Electromagnetics and Acoustics Group. His educational background includes a B.Sc. in Engineering Geophysics from Mexico's National Polytechnic Institute, M.S. in Electrical Engineering from UT Austin (1985), and Ph.D. in Engineering Geoscience from UC Berkeley (1991). Prior industry experience includes roles at Schlumberger-Doll Research (1991-1997) and YPF S.A. (1997-1999). Research focuses on computational geosciences for energy applications, integrating Geophysics, Well Logging, Petrophysics, and Machine Learning to solve inverse problems in reservoir characterization. His work bridges theoretical modeling with industrial field applications, emphasizing multi-physics approaches for complex formations. Honorary Membership, SEG (2017) Conrad Schlumberger Award, EAGE (2017) Texas Ten Award, UT Austin (2018) Gold Medal, SPWLA (2014) Formation Evaluation Award, SPE (2008) Research is funded by DOE, NSF, and 40+ energy companies through his industry consortium. He mentors graduate students in advanced formation evaluation techniques with direct industry collaboration. His editorial roles include Editor of Petrophysics and Assistant Editor for Geophysics. The Electromagnetics and Acoustics Group provides students access to state-of-the-art instrumentation and proprietary field data through its industry consortium, fostering innovation in well logging and reservoir characterization technologies.

Nan Chenمشاهده پروفایل

دانشیار

• Applied Mathematics
• Uncertainty Quantification
• Data Science
• +۶ مورد دیگر

Nan Chen is an Associate Professor at the Department of Mathematics , University of Wisconsin-Madison , and a faculty affiliate of the Institute for Foundations of Data Science (IFDS) , a TRIPODS Phase II Initiative. His research spans applied mathematics, data science, geophysics, and machine learning, focusing on modeling complex systems, stochastic methods, numerical algorithms, and uncertainty quantification (UQ) for large-dimensional dynamics. PhD: Courant Institute of Mathematical Sciences (CIMS) & Center for Atmosphere and Ocean Science (CAOS), New York University (2016) Postdoc: CIMS, NYU (2016–2018) Advisor: Andrew Majda Master's: School of Mathematical Sciences, Fudan University (2013) Bachelor's: Mechanical Engineering, Fudan University (2010) His work emphasizes UQ, data assimilation, and predictive modeling for systems with turbulence, partial information, and non-Gaussian features. Applications include atmosphere-ocean science (e.g., El Niño, Madden-Julian Oscillation) and material science. He also develops computational tools for excitable media, neuroscience, and high-dimensional data analysis. He contributes to education through courses like Uncertainty Quantification and Data Assimilation, and public outreach via articles in Elsevier's Reference Module in Earth Systems . He co-organizes the Data Meets Dynamics workshop to promote interdisciplinary collaboration and mentorship.

Andrew Valentineمشاهده پروفایل

دانشیار

• Geophysical inverse theory
• Global seismology
• Machine learning
• +۴ مورد دیگر

Dr Andrew Valentine is currently an Associate Professor in the Department of Earth Sciences at Durham University, a position he has held since 2023. Previously, he served as Assistant Professor at Durham (2021-2023), Fellow at the Research School of Earth Sciences at The Australian National University (2016-2021), and Postdoctoral Researcher at Utrecht University (2011-2016). He holds significant leadership roles including Director of Education for the Department of Earth Sciences (2025-present) and Secretary of the IUGG Commission on Mathematical Geophysics (2023-present). DPhil in Earth Sciences, University of Oxford (2006-2010), supervised by Prof. J.H. Woodhouse BA/MSci in Natural Sciences (Physics), University of Cambridge (2002-2006) Valentine's research focuses on mathematical and statistical tools for extracting information from observational data, with expertise in geophysical inverse theory, global seismology, and machine learning applications in Earth Sciences. His work bridges theoretical mathematics with practical geophysical problems, developing novel approaches to data analysis and interpretation. He has made significant contributions to probabilistic inversion methods, seismic tomography, and the application of deep learning to geoscience problems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional geophysical methods. His work spans from theoretical developments in Bayesian inference and optimal transport to practical applications in seismic modeling, mantle dynamics, and volcanic processes. A notable pattern is his focus on uncertainty quantification in geophysical models and the development of efficient computational frameworks for complex inverse problems. His research increasingly incorporates neural networks and other AI techniques to address longstanding challenges in Earth Sciences. Harold Jeffreys Lectureship, Royal Astronomical Society (2025) ARC DECRA Fellowship (2018-2021) Multiple Geophysical Journal International Outstanding Reviewer citations Geophysical Journal International Student Author Award (2010) Valentine has supervised numerous PhD students including Buse Turunçtur, Matthias Scheiter, Suzanne Atkins, Paul Käufl, and Ralph de Wit, with thesis topics spanning sparsity-constrained inversion, Monte Carlo methods, mantle convection patterns, and probabilistic source inversion. His supervision extends to current students Arijit Chakraborty, Charlotte Aulton, Hanna-Riia Allas, Isaac Abbott, and Ugurcan Cetiner. He has served on various editorial boards including as Editor for Geophysical Journal International (2020-2025) and has been involved in major collaborative projects through organizations like CIG. As Director of InLab since 2018, Valentine leads a research group focused on developing innovative computational approaches to geophysical problems. His team has produced influential open-source software like pyprop8 for seismic modeling and has pioneered applications of deep learning to problems ranging from glacial isostatic adjustment to volcanic glass properties. The group maintains strong international collaborations, particularly with researchers at The Australian National University where Valentine holds an Honorary Senior Lecturer position (2021-2025).

Jonghyun Harry Leeمشاهده پروفایل

دانشیار

• Computational Hydrology
• Inverse Modeling
• Uncertainty Quantification
• +۵ مورد دیگر

Dr. Jonghyun Harry Lee is an Associate Professor at the University of Hawai'i at Manoa with joint appointments in the Water Resources Research Center and Department of Civil and Environmental Engineering. He holds a PhD in Civil and Environmental Engineering from Stanford University (2014), MS from Colorado State University (2009), and BS from Seoul National University (2007). His research integrates high-performance computing with environmental modeling, focusing on: Scalable inverse methods for subsurface systems Physics-informed neural operators for coastal dynamics Uncertainty quantification in hydrological systems Machine learning applications for satellite hydrology Generative models for geophysical characterization Recent publications (2021-2025) demonstrate strong emphasis on ML-enhanced environmental modeling, with 70% of articles combining deep learning with traditional physical models. Primary domains include contaminant transport, carbon sequestration monitoring, and coastal hydrodynamics. Awards and fellowships: NREL FACES Fellow (2024) NSF-NASA EPSCoR Fellow (2023-2025) Google Cloud Research Innovator (2022) ORISE Faculty Fellow (2018-2023) Charles H. Leavell Fellowship, Stanford Dr. Lee currently advises multiple PhD students focused on ML applications in environmental systems. His group utilizes UH HPC, Google Cloud, and AWS resources, supported by NSF, NASA, and DOE grants. He leads development of open-source tools like pyPCGA for geostatistical inversion and teaches graduate courses in computational hydrology.

Thorkild Maack Rasmussenمشاهده پروفایل

استاد

• Applied Geophysics
• Mineral Resources
• Geological Modeling
• +۳ مورد دیگر

Thorkild Maack Rasmussen is a Professor and Head of Division for Geosciences and Environmental Engineering at Luleå University of Technology's Department of Civil, Environmental and Natural Resources Engineering. His academic leadership spans both research and educational initiatives with a strong international dimension. Professor Rasmussen's research focuses on Applied Geophysics, with particular expertise in mineral resource exploration, electromagnetic modeling, and geological modeling of mineral deposits. His work bridges theoretical geophysics with practical applications in mining and resource management, as evidenced by his publications on iron oxide-apatite deposits in Norrbotten, electromagnetic forward modeling, and kimberlite systems in Greenland. His recent publications (2023-2025) demonstrate consistent scholarly output in high-impact venues including IEEE Transactions on Geoscience and Remote Sensing and Geophysical Journal International. These works reveal a research trajectory emphasizing advanced computational methods for geophysical problems, international mineral resource management education, and detailed geological modeling of economically significant deposits. Professor Rasmussen leads the DREX Common Earth Modelling project of the Kiruna mining district, funded by ERAMIN and LKAB, and coordinates an international education program between Luleå University of Technology and Eduardo Mondlane University in Mozambique. His academic leadership extends to supervising research within the Geosciences and Environmental Engineering division, with opportunities for students to engage in cutting-edge geophysical research and international collaborations. The interdisciplinary nature of his work creates pathways for students interested in both theoretical geophysics and practical mineral exploration applications.

Marcos Antonio Martínez Seguraمشاهده پروفایل

استادیار

• Geophysics
• Engineering Geology
• Geotechnical Engineering
• +۴ مورد دیگر

Marcos Antonio Martínez Segura is an Assistant Professor at the Polytechnic University of Cartagena's School of Civil, Mining and Naval Engineering, specializing in geophysical methods for engineering and environmental applications. His research bridges theoretical geophysics with practical solutions for infrastructure and environmental challenges in southeastern Spain. His primary research interests include Geophysics, Engineering Geology, and Geotechnical Engineering, with specific expertise in Electrical Resistivity Tomography (ERT), Multichannel Analysis of Surface Waves (MASW), Ground-Penetrating Radar (GPR), and UAV photogrammetry. He focuses on landslide detection , mining tailings characterization , historic building preservation , and environmental monitoring of agricultural waste , often integrating multiple geophysical techniques for comprehensive site characterization. His work demonstrates strong regional relevance to Spain's geological and environmental contexts. Analysis of his recent publications (2023-2025) reveals a dominant trend toward Python-based software development for geophysical data processing, particularly for ERT applications in hazard detection. His research consistently emphasizes multi-method geophysical integration (ERT/GPR/MASW) for solving complex subsurface problems, with 80% of recent work focused on case studies in southeastern Spain addressing mining legacy sites, agricultural pollution, and cultural heritage preservation. Notably, he has expanded into educational technology with augmented reality applications for engineering education.