جست‌وجوی استاد و پژوهشگر

• Cognitive Psychology
• Multivariate Statistics
• Neural Networks
• +۵ مورد دیگر

Hervé Abdi is a full Professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas. He holds a Ph.D. in Mathematical Psychology from the University of Aix-en-Provence (France, 1980). His career includes roles as an assistant and full professor in French universities, adjunct professor at UT Southwestern Medical Center, and visiting scholar at institutions worldwide, including Brown University and the University of Geneva. Abdi’s research focuses on computational models of cognition, multivariate statistical techniques (e.g., PCA, correspondence analysis, PLS regression), and neuroimaging data analysis. He explores face and odor perception, brain imaging methodologies, and sensory evaluation. His work bridges cognitive science, statistics, and neuroscience, with over 327 publications, including 12 books and 13 edited volumes. He has mentored numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry leadership roles. Key awards include two Fulbright Scholarships. His recent work emphasizes DISTATIS and STATIS methods for multi-table data, covSTATIS for network neuroscience, and applications in autism research and sensory analysis. Education: M.S. Psychology, University of Franche-Comté (1975) M.S. Economics, University of Clermond-Ferrand (1976) M.S. Neurology, University Louis Pasteur (1977) Ph.D. Mathematical Psychology, University of Aix-en-Provence (1980) Grants & Collaborations: Recipient of NIH-funded grants for autism neuroimaging studies and sensory evaluation projects. Collaborates internationally on brain-behavior relationships and multivariate statistical methodologies. Labs & Teams: Leads computational neuroscience and multivariate analysis research groups. Involved in the Face Lab at UTD, exploring face perception and cognitive modeling.

• Numerical Linear Algebra
• Model Order Reduction
• Matrix Equations
• +۵ مورد دیگر

Dr. Jens Saak is a leading researcher and team leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. He heads the Computational Methods in Systems and Control Theory research group and has held several leadership positions at the institute since 2010, including Head of the Scientific Computing Team (2010-2020), Head of the Matrix Equations Team (2013-2020), and currently serves as Head of the Data, Infrastructure, Software & Computing Team since 2021. Dr. Saak earned his undergraduate degree in Technomathematik from the University of Bremen (1996-2003), completed his PhD at TU Chemnitz (2003-2009), and held a postdoctoral position there (2009-2010) before joining the Max Planck Institute. His research spans computational mathematics with a focus on large-scale sparse matrix equations , model order reduction , and optimal control of complex systems. He specializes in developing efficient numerical algorithms for Lyapunov and Riccati equations, with applications in mechanical engineering (particularly machine tools), fluid dynamics, and thermal systems. His work bridges theoretical numerical analysis with practical high-performance computing implementations. Analysis of his recent publications reveals a clear trajectory toward energy-efficient computing solutions, sustainable research software practices, and increasingly sophisticated model reduction techniques for complex coupled systems. His work demonstrates a strong emphasis on practical implementations with industrial applications, particularly in mechanical engineering contexts. Dr. Saak has been instrumental in developing and maintaining research software infrastructure, with a growing focus on sustainability and reproducibility in computational science. His leadership extends to collaborative projects across the German mathematical community, particularly in research data management initiatives. His team actively contributes to open-source scientific computing tools, with significant work on the M-M.E.S.S. (Matrix Equations, Sparse Solvers) toolbox, which provides implementations of modern algorithms for matrix equations and model order reduction.

Thomas Zeiserمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Lattice Boltzmann Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Thomas Zeiser serves as Head of Systems & Services and Chief Operating Officer HPC at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich Alexander University Erlangen-Nuremberg. He leads the Systems & Services group of NHR@FAU and HPC4FAU since the end of 2020 and serves as deputy for NHR@FAU in the NHR Betreiberausschuss. His work focuses on transitioning from serving FAU only to national center operations, managing HPC systems, procuring new infrastructure, financial controlling of NHR@FAU's budgets, and supporting planning for a new data center building. Dr. Zeiser's research interests center around High-Performance Computing with specific expertise in Lattice Boltzmann Methods , large-scale simulations, evaluation of HPC hardware and software, and efficient operation of HPC systems. His work bridges the gap between theoretical computational methods and practical implementation in high-performance environments. He has implemented the first job-based job and performance monitoring for RRZE's HPC systems and has extensive experience in procurement of HPC infrastructure. Analysis of Dr. Zeiser's publication record reveals a consistent focus on practical applications of computational methods in HPC environments. His research demonstrates a progression from fundamental lattice Boltzmann method development toward increasingly complex system-level concerns including fault tolerance, performance monitoring, energy efficiency, and scalable implementations. The publications show strong collaboration patterns with researchers at FAU and other German institutions, particularly in the areas of computational fluid dynamics and parallel computing. Dr. Zeiser regularly serves as a reviewer for various journals and compute time commissions of different HPC centers. He is also one of the local organizers for the Ferienakademie of TUM, FAU, and Universität Stuttgart held in Sarntal. His work with NHR@FAU positions him at the forefront of national high-performance computing infrastructure in Germany. At NHR@FAU and RRZE (Regional Computing Center Erlangen), Dr. Zeiser leads teams responsible for operating some of Germany's most powerful academic supercomputing resources. His group plays a critical role in supporting computational research across multiple disciplines at FAU and increasingly at the national level through the NHR initiative.

Markus Steinbergerمشاهده پروفایل

دانشیار

• GPU Computing
• Computer Graphics
• Parallel Processing
• +۳ مورد دیگر

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

Peter Weinbergمشاهده پروفایل

استاد

• Cardiovascular Mechanics
• Biomedical Engineering
• Atherosclerosis
• +۷ مورد دیگر

Peter Weinberg is a Professor of Cardiovascular Mechanics in the Department of Bioengineering at Imperial College London, Faculty of Engineering. He is based at the Royal School of Mines on the South Kensington Campus and can be contacted at p.weinberg@imperial.ac.uk. His research is centered on the biomechanics of cardiovascular diseases, particularly atherosclerosis and heart failure. He leads a research group focused on fluid dynamics, endothelial function, and advanced ultrasound imaging techniques. Education: Natural Sciences, University of Cambridge (Scholarship recipient) DIC, MSc, PhD in Physiological Flow Studies, Imperial College London Lady Davis Postdoctoral Fellowship, Technion – Israel Institute of Technology His research interests lie at the intersection of biomedical engineering, cardiology, and biomechanics . He investigates how hemodynamic forces such as wall shear stress influence endothelial permeability and atherosclerosis development. A major focus is on transcytosis of LDL , disturbed blood flow patterns , and non-invasive detection of heart failure using B-mode ultrasound and wave intensity analysis. His lab develops novel ultrasound imaging methods, including super-resolution techniques using nanodroplets and microbubbles, and coherence-based beamforming for 3D vascular mapping. His recent publications (2021–2025) demonstrate a strong trend toward advanced ultrasound diagnostics and molecular mechanobiology . The articles span from computational beamforming improvements to in vivo validation of endothelial activation pathways. Key themes include ultrasound velocimetry , macromolecule transport , shear stress modeling , and early disease detection . The work combines engineering innovation with deep biological inquiry, aiming to translate biomechanical insights into clinical tools. Scientific Awards and Honors: Fellow, Royal Microscopical Society Ordinary Member, The Physiological Society Member, British Atherosclerosis Society Committee Member, London Microcirculation Group Committee Member, British Society for Cardiovascular Research Committee Member, British Atherosclerosis Society Lady Davis Fellow Peter Weinberg has held key leadership roles in the Department of Bioengineering, including Director of Postgraduate Studies (Research) , Director of Research , and Academic Line Manager . He led the department’s efforts in the Research Assessment Exercise 2008 and Research Excellence Framework 2014. He founded and served as president of the Bioengineering Society (now BioMedEng), and was Associate Editor of the journal Atherosclerosis . He has organized major conferences such as the joint British Society for Cardiovascular Research and British Atherosclerosis Society meeting, and chaired BioMedEng18, attracting over 500 delegates. He has secured research grants, though specific details are not listed in the text. He leads a research laboratory in the Department of Bioengineering at Imperial College London, focusing on cardiovascular mechanics . The lab website details ongoing projects in ultrasound imaging, endothelial mechanobiology, and atherosclerosis modeling. The team uses a combination of computational modeling, in vitro bioreactors, and in vivo imaging to study vascular function and disease progression.

Meng Wangمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Meng Wang is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where she was promoted to Full Professor in June 2025. She received her B.S. and M.S. degrees (both with honors) in Electrical Engineering from Tsinghua University, China, in 2005 and 2007, respectively, and her Ph.D. in Electrical and Computer Engineering from Cornell University in 2012. After a postdoctoral position at Duke University, she joined RPI in December 2012 as an Assistant Professor, was promoted to Associate Professor with Tenure in 2019, and then to Full Professor in 2025. Her research spans machine learning and artificial intelligence, high-dimensional data analytics, power system monitoring, signal processing, and optimization methods. She has made fundamental contributions in sparse signal recovery and monitoring and control of smart grid using high frequency data from phase measurement unit (PMU). More recently, she has collaborated with IBM to produce theoretical guarantees of modern AI architectures such as graph neural networks and transformers used in large language models (LLMs). Wang's recent publications (2023-2025) reveal a strong focus on theoretical foundations of deep learning, particularly transformer architectures and graph neural networks. Her work bridges theoretical guarantees with practical applications in power systems, demonstrating how fundamental insights in machine learning can solve real-world energy challenges. She has increasingly focused on the intersection of AI and energy systems, developing methods for building-level load forecasting, energy disaggregation, and smart grid monitoring with behind-the-meter solar integration. AFOSR Young Investigator Program (YIP) Award (2019) Army Research Office (ARO) YIP Award (2017) James M. Tien '66 Early Career Award and Grant for Faculty (2022) School of Engineering Research Excellence Award (2018) IEEE Signal Processing Society Best Reviewer Award (2018) Professor Wang has mentored numerous Ph.D. students who have gone on to successful careers in academia and industry, including HongKang Li (now postdoc at University of Pennsylvania), Yi Ming (postdoc at University of Michigan), and Shuai Zhang (Assistant Professor at New Jersey Institute of Technology). Her research has been supported by multiple grants from the National Science Foundation, Air Force Office of Scientific Research, Army Research Office, and industry partners including IBM. She is actively involved with research centers including the Center for Future Energy Systems (CFES) and the Center for Materials, Devices, and Integrated Systems (CMDIS), where her group develops cutting-edge methods for power system monitoring and control. Her recent work has increasingly focused on the theoretical foundations of large language models and their applications to energy systems, positioning her at the forefront of AI for critical infrastructure.

• Random Matrix Theory
• Sparse Data Analysis
• Community Detection
• +۳ مورد دیگر

Yizhe Zhu is an Assistant Professor of Mathematics at the University of Southern California , specializing in theoretical and applied aspects of high-dimensional data analysis. His research bridges mathematics, computer science, and statistics, with a focus on random matrix theory, sparse data structures, and algorithmic analysis for machine learning and privacy-preserving data methods. Research Interests Yizhe Zhu’s work addresses fundamental questions in: Random Matrix Theory : Spectra of sparse and structured matrices, including outlier detection and universality. Graph and Hypergraph Analysis : Community detection, spectral properties, and non-backtracking algorithms for complex networks. Privacy and Data Synthesis : Theoretical frameworks for differentially private synthetic data generation. Tensor Completion : Efficient algorithms for recovering low-rank tensors from sparse observations. Publications Trends His recent research (2024–2025) emphasizes spectral analysis of random structures, optimization in non-convex settings, and privacy-preserving machine learning. Key themes include the interplay between sparsity, spectral theory, and algorithmic robustness in high-dimensional regimes.

• Numerical Linear Algebra
• Randomized Algorithms
• Genomics
• +۵ مورد دیگر

Petros Drineas is the Department Head and Professor of Computer Science at Purdue University's College of Engineering. His research focuses on randomized numerical linear algebra (RandNLA), algorithms for large-scale data analysis, computational genomics, and machine learning. He leads the ENIGMA-TS initiative for global neuroimaging genetics collaboration and has developed tools like TeraPCA for tera-scale genotype analysis. Drineas has held leadership roles in NSF and BSF-funded projects, including studies on Tourette syndrome, Alzheimer's disease, and algorithmic efficiency. His work bridges theoretical computer science with applications in genetics, neuroscience, and optimization. Key contributions include matrix sketching frameworks, randomized algorithms for PCA, and methods for GWAS and polygenic risk score analysis. His grants include funding for RandNLA applications in inverse problems, fast least squares solvers, and collaborative research on genetic variation. He has pioneered methods for handling massive datasets in genetics, such as the MaSk-LMM framework for linear mixed models and Patch2Self2 for self-supervised denoising.

Frederike Dümbgenمشاهده پروفایل

استادیار

• Robotics
• Optimization
• Machine Learning
• +۱۲ مورد دیگر

Frederike Dümbgen is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, starting in Spring 2026. She is currently a researcher with the Willow team at Inria Paris, focusing on optimization for robotics, and previously served as a postdoctoral fellow at the University of Toronto's Robotics Institute. Education: Ph.D. in Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2021) M.Sc. in Mechanical Engineering, EPFL (2016) B.Sc. in Mechanical Engineering, EPFL (2013) Her research centers on improving the efficiency and safety of robots operating in the physical world through principled optimization and machine learning methods. She emphasizes certifiable and globally optimal algorithms to build reliable foundations for next-generation robotics in domains such as autonomous vehicles, assistive technology, and manufacturing. Her work bridges robotics, control systems, and artificial intelligence, with a strong focus on mathematical rigor and scalability. The 15 most recent publications highlight a consistent trajectory in robotics-focused optimization, particularly in state estimation, SLAM, pose estimation, and data-driven methods. These works frequently employ semidefinite programming, convex relaxations, and Koopman-based linearization, demonstrating a deep integration of theoretical optimization with practical robotic applications. Keywords across these papers include robotics, optimization, machine learning, and estimation, with subfields like certifiable algorithms, global optimality, and sensor fusion recurring throughout. Dr. Dümbgen has not yet had scientific awards listed in the provided text. She has not yet advised any named students in the provided materials, and there is no mention of grants or funding sources. However, her research trajectory and publication record suggest active involvement in competitive research environments. Her experience includes internships at Disney Research and ABB, and her master’s thesis was completed at ETH Zürich’s Autonomous Systems Lab. She is currently affiliated with the Willow research team at Inria Paris, a group known for foundational work in computer vision, machine learning, and robotics. This team emphasizes mathematical rigor in algorithm design, aligning closely with her focus on certifiable and globally optimal methods.

Andrew Wynnمشاهده پروفایل

دانشیار

• Control and Optimization
• Fluid Mechanics
• Aeroservoelastic Control
• +۷ مورد دیگر

Dr. Andrew Wynn is an Associate Professor in the Department of Aeronautics at Imperial College London, Faculty of Engineering. His research focuses on developing computational optimization methods to improve fluid mechanical systems, including active flow control, data-driven modelling, aeroservoelasticity, and semi-algebraic optimization techniques. He leads a research group addressing challenges in fluid-structure interactions, turbulence, and control systems. Wynn's affiliations include the Flow Control Research Group and the Aeroelastics Group. His work intersects interdisciplinary fields such as mechanical engineering, applied mathematics, and aerospace engineering. Key research topics include bluff body drag reduction, scaling laws for fluid flows, and estimator design for high-dimensional systems. Publications highlight contributions to fluid mechanics, optimization algorithms, and control theory. His group employs methods like semidefinite programming and dynamic mode decomposition to analyze and control complex flows. Ongoing projects involve wind farm optimization, global stability of viscoelastic fluids, and Bayesian optimization for experimental fluid dynamics. Education: Not explicitly stated in the provided text. Grants & Awards: Affiliated with Imperial College Research Fellowships (ICRF) and President's PhD Scholarships programs. Labs/Teams: Leads the Wynn Research Group, collaborating with the Flow Control and Aeroelastics Groups.

Armeen Taebمشاهده پروفایل

استادیار

• Selective Inference
• Multiple Testing
• Graphical Models
• +۷ مورد دیگر

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.

Victor Hugo Lachosمشاهده پروفایل

استاد

• Mixed-effects models
• Censored regression models
• Time series analysis
• +۷ مورد دیگر

Dr. Victor Hugo Lachos is a Professor in the Department of Statistics at the University of Connecticut. His research focuses on advanced statistical methodologies for handling complex data structures, including censored regression models, mixed-effects models, and heavy-tailed distributions. He has contributed extensively to Bayesian inference, EM algorithms, and software development for statistical analysis. Multivariate Student-t and skew-normal distributions Longitudinal and spatial data modeling Regularization techniques for high-dimensional data Software packages for censored data analysis His recent publications emphasize robust modeling of censored and irregularly observed data, with applications in medical research (e.g., HIV longitudinal studies) and environmental modeling (e.g., acid rain analysis). His work integrates theoretical advances in distribution theory with practical computational tools in R packages like ‘StempCens’ and ‘mixsmsn’. These contributions are complemented by methodological innovations in EM algorithm applications, influence diagnostics, and semiparametric regression. Dr. Lachos' research has been applied to diverse fields such as medical data analysis, environmental science, and educational measurement. While no explicit awards or student advisement details are listed, his prolific output in top-tier journals and software development underscores his active academic engagement.

Siqi Wuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Siqi Wu is a quantitative researcher affiliated with the Department of Statistics at the University of California, Berkeley. He earned a B.S. from the University of Hong Kong in 2010 and completed his Ph.D. in Statistics at Berkeley in 2016 under the guidance of Professor Bin Yu. Education: B.S. (University of Hong Kong, 2010), Ph.D. (UC Berkeley, 2016) Current Role: Quantitative Researcher at Citadel Securities His research focuses on machine learning and computational biology, particularly dictionary learning, spatial gene expression analysis, and nonnegative matrix factorization. His work bridges theoretical statistics with practical applications in high-dimensional data and biological systems. Key trends include methods for local identifiability in optimization and stability-driven approaches to gene network construction. Key Contributions: Development of DataLab for data management, theoretical analysis of l1-minimization dictionary learning, and spatial gene expression modeling for Drosophila development.

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.