جست‌وجوی استاد و پژوهشگر

Volker Michelمشاهده پروفایل

استاد

• Geomathematics
• Inverse Problems
• Gravimetry
• +۴ مورد دیگر

Prof. Volker Michel is a Professor of Mathematics in Engineering at the Department of Mathematics, University of Siegen, leading the Geomathematics Group. He holds a PhD and habilitation from the University of Kaiserslautern. His research focuses on applying advanced mathematical techniques to geoscience problems, including inverse problems, gravimetry, and medical imaging. Michel has organized major conferences and secured numerous DFG grants. He serves as editor-in-chief of the International Journal on Geomathematics and editorial roles in other journals. Notable achievements include a prize for his PhD thesis and international collaborations with institutions like the Isaac Newton Institute and NASA's JPL. His academic leadership includes heading the Department of Mathematics at Siegen from 2014–2016. Education: Industrial Mathematics Diploma, University of Kaiserslautern (1995) PhD in Mathematics, University of Kaiserslautern (1999) Habilitation in Mathematics, University of Kaiserslautern (2002) Research Interests: Geomathematics, multiscale approximation methods, inverse problems in geosciences, medical imaging techniques, numerical analysis, and functional analysis applications. His work bridges theoretical mathematics with practical geophysical and engineering challenges, emphasizing data-driven solutions for Earth sciences. Publications: His books synthesize cutting-edge methodologies, such as Geomathematics (2022), which addresses mathematical modeling in geodesy and geophysics, and Lectures on Constructive Approximation (2013), detailing wavelet and spline techniques across domains. These works reflect his expertise in translating complex mathematical theories into applicable geoscientific frameworks. Awards and Recognition: PhD Prize from Kreissparkasse Kaiserslautern (1999) Editorial leadership in top-tier journals International collaboration recognitions Grants and Advising: Secured multiple DFG-funded projects and advised numerous theses. His research group actively engages in third-party funded initiatives focusing on geomathematical problem-solving. Labs/Teams: Founded and directs the Geomathematics Group at Siegen, a hub for interdisciplinary research in mathematical geosciences.

Aanjaneya Mridulمشاهده پروفایل

دانشیار

• Computer Graphics
• Scientific Computing
• Programming Languages
• +۳ مورد دیگر

Aanjaneya Mridul is an Associate Professor in the Department of Computer Science at Rutgers University, where he joined in 2017. He is also a member of the Computational Biomedicine Imaging and Modeling Center (CBIM). His research spans computer graphics, scientific computing, programming languages, and robotics, with a focus on developing numerical methods in computational physics that leverage modern computing power. Dr. Aanjaneya completed his doctoral studies in Computer Science from Stanford University in 2013 under Ronald Fedkiw, followed by postdoctoral studies at the University of Wisconsin-Madison with Eftychios Sifakis. He received his undergraduate degree in Computer Science and Engineering from the Indian Institute of Technology Kharagpur in 2008. His research interests include: Computer graphics and visual simulation Scientific computing and numerical methods Programming languages for computational physics Robotics and differentiable physics engines Computational physics for interdisciplinary applications Dr. Aanjaneya's recent work has focused on applying numerical methods to robotics for learning unknown physical parameters, and designing correctly-rounded implementations of elementary functions for new floating-point variants. His long-term goal is to enable next-generation algorithms that facilitate interdisciplinary collaboration with researchers in engineering and medicine. He has received several prestigious awards including the Ralph E. Powe Junior Faculty Enhancement Award (2019) and the NSF CAREER Award (2023). Ralph E. Powe Junior Faculty Enhancement Award (2019), sponsored by Oak Ridge Associated Universities (ORAU) NSF CAREER Award (2023) At Rutgers, Dr. Aanjaneya directs the Laboratory for Interactive Virtual Environments (LIVE), where he mentors PhD students and postdoctoral researchers. He has secured multiple NSF grants including the CAREER award, an NSF NRI grant with Professors Boularias and Yu, and grants with Professor Santosh Nagarakatte for projects on formally certified low-dimensional linear programs and correctly rounded math libraries. Outside of his academic work, Dr. Aanjaneya enjoys running, rock climbing, hiking, watching movies, and playing piano.

Jeffrey Hoganمشاهده پروفایل

دانشیار

• Harmonic Analysis
• Signal Processing
• Wavelet Analysis
• +۵ مورد دیگر

Jeffrey Hogan is an Associate Professor in the School of Information and Physical Sciences (Mathematics), University of Newcastle. He holds academic appointments including Head of Mathematics Discipline and former Deputy Head of the School of Mathematical and Physical Sciences. His research focuses on harmonic analysis, wavelet theory, and applications in signal processing, including bandlimited signals, prolate spheroidal wave functions, and quaternion-valued wavelets. Hogan earned his BSc (Hons) and PhD from the University of New South Wales, followed by postdoctoral roles at institutions like the University of Texas and University of Arkansas before joining Newcastle in 2009. Education: Bachelor of Science, University of New South Wales PhD (Mathematics), University of New South Wales Research Interests: Jeffrey Hogan explores pure, applied, and computational harmonic analysis, including singular integrals, wavelet frames, Clifford-Fourier theory, and applications in mineral fractionation. His work emphasizes spatio-spectral limiting, multidimensional wavelet construction, and quaternionic signal processing. Key projects involve developing algorithms for EEG analysis, optimizing wavelet design via projection methods, and analyzing signals on graphs and hypercubes. Grant Funding: A$546,913: Enhanced Fractionation of Mineral Particles According to Density (ARC Discovery Project, 2020–2022) A$593,621: Relaxed Reflection Methods for Feasibility and Matrix Completion Problems (ARC Discovery Project, 2016–2018) Collaborations & Teams: Hogan collaborates with experts like Prof. Kevin Galvin (University of Newcastle), Prof. Joseph Lakey (New Mexico State University), and Dr. Matthew Tam (University of Göttingen). His work integrates interdisciplinary approaches, combining pure mathematics with applications in engineering and mineral processing. His lab focuses on computational harmonic analysis and optimization-driven wavelet design.

Ravi Pethiyagodaمشاهده پروفایل

مدرس

• Fluid Mechanics
• Computational Mathematics
• Free-Surface Flows
• +۳ مورد دیگر

Dr. Ravi Pethiyagoda is a Lecturer in Mathematics at the School of Information and Physical Sciences, University of Newcastle. His research focuses on fluid mechanics, computational mathematics, and free-surface flows, with particular emphasis on ship wave dynamics and tsunami propagation. He holds a PhD in Applied Mathematics from Queensland University of Technology (2016) and has conducted postdoctoral research on droplet impaction on plant leaves and nonlinear ship wave patterns. His work includes advancing numerical methods like Jacobian-free Newton-Krylov (JFNK) techniques to solve complex fluid dynamics problems. He has contributed to understanding the apparent wake angle, time-frequency analysis of ship wakes, and compressible ocean wave propagation. Current PhD supervision spans wave scattering, fluid dynamics with surface tension, and ocean wave problems. Key research areas include: numerical methods for nonlinear ship wave problems, analysis of wake patterns, spectrogram-based wave signal interpretation, and tsunami wave dynamics under variable bathymetry. His interdisciplinary studies bridge mathematics, physics, and engineering, with applications in oceanography and agricultural fluid dynamics.

Zhengyuan Zhuمشاهده پروفایل

استاد

• Spatial Statistics
• Survey Statistics
• Machine Learning
• +۵ مورد دیگر

Zhengyuan Zhu is a Professor of Statistics and Dean’s Professor in the College of Liberal Arts and Sciences at Iowa State University. He serves as Director of the Center for Survey Statistics and Methodology (CSSM). His expertise spans spatial statistics, survey statistics, time series analysis, machine learning, and applications in environmental and agricultural domains. He leads national surveys like the US National Resource Inventory and BLM-Managed Lands surveys, focusing on statistical data integration and spatio-temporal modeling. Education: PhD in Statistics from the University of Chicago; BS in Mathematics from Fudan University, Shanghai. Research interests include spatial sampling design, anomaly detection, statistical data integration, and applications in environmental monitoring, remote sensing, and transportation. He has over 60 publications and collaborates with multidisciplinary teams to address complex data challenges in natural resource management and public policy. His work emphasizes high-resolution global datasets (e.g., land surface temperatures, precipitation patterns) and methodological advancements in federated learning, distributed optimization, and sparse functional data analysis. Labs/Teams: Directs the CSSM, which provides statistical support for national longitudinal surveys and develops innovative methodologies for complex data systems.

Sachin Meenaمشاهده پروفایل

مدرس ارشد

• Image Segmentation
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Sachin Meena is a Senior Lecturer in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds a PhD in Computer Science from the University of Missouri (2016) and a Bachelor of Electrical Engineering Technology from the Indian Institute of Technology, Delhi (2002). His research focuses on interactive image segmentation techniques, medical imaging, and computer vision, with notable contributions in multiquadric splines and elastic interpolation methods. Professional recognition includes the 2023 Student Career Success Influencer Award for his work in career development for UGA graduates. His academic roles include teaching and research supervision in computational methods, though no formal advisees are listed. His publications span conferences like IEEE EMBC and SPIE, addressing challenges in vascular network segmentation and sparse point-based methodologies. Though no lab affiliations are explicitly stated, his work intersects computational imaging and biomedical applications.

Timothy Tzen Vun Yapمشاهده پروفایل

استادیار

• System Identification
• Blockchain Technology
• Data Engineering
• +۳ مورد دیگر

Dr. Timothy Tzen Vun Yap is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University Malaysia. Previously, he served as Senior Lecturer and Chair of the Center for Big Data and Blockchain Technologies at Multimedia University. He holds a PhD in Engineering from Multimedia University's System Identification and Control Group. His research focuses on blockchain technology, machine learning, data engineering, and system identification, with emphasis on consensus mechanisms, Sybil resistance in blockchain networks, and sentiment/aspect modeling. His research interests span technical domains including spyware detection, network security, educational data mining, and cryptocurrency efficiency. Notable contributions include advancements in Bitcoin block propagation, dataset curation for malware classification, and machine learning applications in intrusion detection systems. He has also explored the intersection of machine learning and healthcare, such as predictive modeling for breast cancer risk factors. His publications reflect a trend toward interdisciplinary solutions, blending cybersecurity with machine learning and applying data science to societal challenges like tourism and education. Collaborations often target practical implementations of theoretical models, such as optimizing blockchain protocols or improving real-time network traffic analysis. Though no formal awards are listed, his prolific output (59+ research outputs) and diverse collaborations underscore his active role in academic and applied research. His work contributes to UN Sustainable Development Goals through technological solutions addressing education equity, healthcare accessibility, and secure digital infrastructure.

Boris Levitمشاهده پروفایل

استاد

• Non-parametric estimation
• Semi-parametric models
• Optimal design in non-parametric regression
• +۵ مورد دیگر

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

Mladen Victor Wickerhauserمشاهده پروفایل

استاد

• Harmonic Analysis
• Wavelets
• Numerical Algorithms
• +۴ مورد دیگر

Mladen Victor Wickerhauser is a Professor of Mathematics and Biomedical Engineering at Washington University in St. Louis. He holds a B.S. from the California Institute of Technology and M.S./Ph.D. from Yale University. His research focuses on Harmonic Analysis, Wavelets, Numerical Algorithms, and their applications in Biomedical Engineering, Medical Imaging, and Financial Mathematics. He has over 1,000 publications, six U.S. patents—including a FBI fingerprint compression algorithm—and received the 2002 Wavelet Pioneer Award from SPIE. Education: B.S. with Honor in Mathematics, California Institute of Technology (1980) M.S. and Ph.D. in Mathematics, Yale University (1982, 1985) Research Highlights: Dr. Wickerhauser’s work bridges mathematical theory and practical applications. His contributions include entropy-based signal processing for medical imaging, wavelet algorithms for data compression, and financial mathematics textbooks. His patents cover medical treatment planning and signal enhancement techniques. Awards & Grants: Wavelet Pioneer Award (SPIE, 2002) NSF grants for computational harmonic analysis and wavelet algorithms Students & Collaborations: He has advised PhD students like David Meyer and Wojciech Czaja. Collaborators include experts in biomedical engineering, signal processing, and medical physics. His lab focuses on molecular imaging and entropy-based diagnostics.

• Numerical Analysis
• Geometric Modeling
• Approximation Theory
• +۳ مورد دیگر

Lucia Romani is a Full Professor in Numerical Analysis at the Department of Mathematics, University of Bologna. She holds a PhD from the University of Padua (2004) and a Master's Degree in Mathematics (summa cum laude) from the University of Bologna (2000). Her research focuses on geometric modeling, approximation theory, numerical linear algebra, and applications in computer-aided design and image processing. She has authored over 80 scientific publications and serves on editorial boards of journals like Computer-Aided Design and Computer Aided Geometric Design . She actively participates in SIAM and the Solid Modeling Association (SMA), holding leadership roles such as Vice Chair of the SIAM Activity Group on Geometric Design (2023-2024) and Program Co-Chair of SPM 2025. Her work includes organizing international conferences and supervising numerous theses across PhD, Master's, and Bachelor's levels. Her professional journey includes roles at the University of Milan-Bicocca (2007-2022) and postdoctoral fellowships at the University of Bologna (2003-2007) and the University of Cambridge (2002). She has conducted research visits at institutions like the University of Cambridge, EPFL, and ICTP Havana. She has also served on evaluation committees for doctoral theses and research projects in Italy and abroad. Research interests span subdivision schemes, Pythagorean Hodograph curves, and spline-based interpolation. Her recent work includes advancements in algebraic characterizations of curves, non-stationary subdivision algorithms, and applications in biomedical imaging and computer graphics.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +۵ مورد دیگر

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Muhammed Enes Atikمشاهده پروفایل

استادیار

• Geomatics Engineering
• Remote Sensing
• Deep Learning
• +۴ مورد دیگر

Muhammed Enes Atik is an Assistant Professor in the Department of Geomatics Engineering at Istanbul Technical University (ITU), affiliated with the Faculty of Civil Engineering. His academic roles include teaching and research supervision. He holds a PhD (2018), MSc (2016), and BSc (2011) in Geomatics Engineering from ITU. His research focuses on remote sensing, deep learning applications in geospatial data, UAV-based photogrammetry, and point cloud processing. Education: PhD in Geomatics Engineering, Istanbul Technical University, 2011–2018 MSc in Geomatics Engineering, Istanbul Technical University, 2016–2018 BSc in Geomatics Engineering, Istanbul Technical University, 2011–2016 Research Interests: Enes Atik specializes in integrating deep learning techniques with geomatics engineering to enhance photogrammetric applications, UAV data analysis, and point cloud segmentation. His work bridges remote sensing, GIS, and computer vision to address challenges in urban planning, disaster management, and infrastructure monitoring. Key areas include: 3D modeling and semantic segmentation of point clouds Optimization of UAV-based photogrammetric workflows Machine learning for remote sensing image classification Flood susceptibility analysis using GIS and AHP Cultural heritage documentation via laser scanning and photogrammetry Awards: IEEE GRSS Türkiye Thesis Competition Award (2022) Muhammed Enes Atik Prize for Student Excellence (2021) 3-Minute Thesis Competition Award (IEEE GRSS, 2024) Grants & Projects: Principal Investigator: 'Deep Learning-Based Disparity Map Estimation and DEM Generation from UAV Imagery' (2023–2025) Co-Investigator: '3D Modeling of Historical Instruments' (2021–2023) Lead Researcher: 'Flood Simulation Using Game Engines' (2024–ongoing) Teaching: Enes Atik instructs courses in programming fundamentals, photogrammetry, geomatics projects, and laser data engineering at both undergraduate and graduate levels.

Zafer Doganمشاهده پروفایل

استادیار

• Machine Learning
• Data Analytics
• Signal Processing
• +۵ مورد دیگر

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

Işın Ererمشاهده پروفایل

استاد

• Signal Processing
• Radar Systems
• Ground Penetrating Radar
• +۱۷ مورد دیگر

Işın Erer is a Professor at the Department of Electronics and Communication Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University (ITU). She is actively involved in research on radar signal processing, artificial intelligence, and image analysis, with a focus on ground-penetrating radar (GPR) and remote sensing applications. University: Istanbul Technical University School: Faculty of Electrical and Electronic Engineering Department: Department of Electronics and Communication Engineering Academic Rank: Professor Email: ierer@itu.edu.tr Her research interests span signal processing, radar systems, clutter removal, target detection, deep learning, vision transformers, U-Nets, and vital signs detection using stepped-frequency radar . She applies advanced machine learning techniques to enhance radar imaging and improve performance in challenging environments such as debris fields and outdoor conditions. The recent publication trends indicate a strong focus on integrating deep learning models (e.g., Vision Transformers, YOLOv5, U-Net) with radar signal processing for clutter removal, image restoration, and segmentation . Her work combines low-rank approximations, autoencoders, B-spline activation functions, and attention mechanisms to improve accuracy and robustness in GPR and remote sensing imagery. Applications include parcel boundary delineation, road segmentation, and life detection in search-and-rescue scenarios. She has received notable recognition for her academic mentorship: Best PhD Thesis Advisor in Telecommunications Engineering Program, 2018 She leads multiple active research projects funded by TÜBİTAK and ITU-BAP, focusing on real-time AI-based radar systems, through-wall vital sign detection, and clutter removal in GPR. She has supervised numerous graduate students, with 47 theses in progress or completed. Her research group works on both theoretical algorithm development and practical system implementation, bridging the gap between academia and real-world deployment. Current projects include developing integrated AI models for real-time GPR systems and ultra-wideband radar methods for behind-obstacle detection.

• Ophthalmology
• Neurosciences
• Biomedical Data Science
• +۴ مورد دیگر

Francisco González García is a Professor in the Department of Surgery and Medical-Surgical Specialties at the University of Santiago de Compostela's Faculty of Medicine and Dentistry. He holds affiliations with the Center for Research in Molecular Medicine and Chronic Diseases (CIMUS) and the Vision Research Group in Ophthalmology and Visual Sciences. His academic career includes doctoral degrees from both the University of Santiago de Compostela (1982) and the University of La Laguna (2001), focusing on visuomotor neuroscience and otolaryngology. Research interests span ophthalmology, neurosciences, and biomedical data science with recent emphasis on retinal physiology, intraocular devices, and stem cell therapies. His work bridges clinical practice and engineering through innovations in tonometry techniques and smart lens development. Notable contributions include studies on prostaglandin-associated periorbitopathy and neuroprotective roles of erythropoietin in retinopathy. Publications (2021–2025) reflect multidisciplinary activity, combining clinical case reports with methodological advancements in neuronal activity modeling and orbital imaging. Collaborative projects like the Green Link initiative demonstrate environmental engineering applications of medical expertise in desertification reversal using fog capture technologies. He has advised numerous studies on ocular pharmacokinetics and thyroid eye disease management, though specific student names aren't listed. His lab work integrates computational tools like P-spline algorithms with traditional microscopy techniques.