جست‌وجوی استاد و پژوهشگر

Abdul-Lateef Haji-Aliمشاهده پروفایل

دانشیار

• Monte Carlo Methods
• Stochastic Differential Equations
• Importance Sampling
• +۳ مورد دیگر

Abdul-Lateef Haji-Ali serves as Associate Professor in the Actuarial Mathematics & Statistics department at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. His research focuses on computational methods for stochastic systems with applications in finance and engineering, maintaining active supervision of PhD students. His primary expertise spans Monte Carlo methodologies, particularly multilevel techniques and importance sampling for rare events in stochastic differential equations. Current work integrates machine learning with traditional Monte Carlo approaches, emphasizing McKean-Vlasov systems and partial differential equations under uncertainty. Recent publications (2024-2025) reveal concentrated advancement in rare-event simulation for complex stochastic models, with growing interdisciplinary connections to generative AI through diffusion models. Key innovations include antithetic schemes for non-commutative noise systems and double-loop importance sampling frameworks. His scientific recognition includes: Leslie Fox Prize (2nd place, June 2019) Dr. Haji-Ali actively recruits PhD candidates for projects on Adaptive Monte Carlo Methods for Stochastic Differential Equations. His collaborative network spans international institutions, with recent co-authorship including Tempone, Pereyra, and Zygalakis, indicating robust research funding through sustained high-impact publication output.

• Nonparametric Statistics
• High-dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

Cristina Butucea is a Full Professor of Statistics at ENSAE, Institut Polytechnique de Paris (IP Paris), and a Permanent Member of CREST (Center for Research in Economics and Statistics). She specializes in nonparametric and high-dimensional mathematical statistics, with research interests spanning inverse problems, quantum statistics, privacy of data, and machine learning. Her academic career includes faculty positions at several prestigious French institutions including Université Paris-Est Marne-la-Vallée and Université des Sciences et Technologies de Lille. Her educational background includes a post-doc at Humboldt University, Berlin (1998-1999), followed by an Assistant Professor position at Université Paris Nanterre (1999-2007). She was promoted to Professor at Université des Sciences et Technologies de Lille 1 (2007-2010), then at Université Paris-Est Marne-la-Vallée (2010-2016), and currently holds her position at ENSAE, IP Paris (2016-present). Professor Butucea's research focuses on theoretical statistics with applications in modern data science challenges. Her work on differential privacy has established fundamental limits and optimal procedures for statistical estimation under privacy constraints. She has made significant contributions to quantum statistics, particularly in quantum state estimation. Her research on high-dimensional statistics addresses variable selection, sparse structures, and nonparametric estimation in complex settings. She has also contributed to the theory of inverse problems and the analysis of locally stationary processes. Her recent publications demonstrate a strong focus on the intersection of statistics with privacy concerns, quantum information, and high-dimensional data analysis. She has published in top statistical journals including Annals of Statistics, Bernoulli, and Electronic Journal of Statistics. Her work often addresses fundamental questions about optimal rates of convergence, phase transitions in estimation problems, and the theoretical limits of statistical procedures under various constraints. Nominated IMS Fellow in 2019 CO-organizer of the Seminar of Statistics CREST-CMAP Associate Editor of ALEA (Latin American Journal of Probability and Mathematical Statistics) Organizer of several conferences in mathematical statistics and machine learning (Fréjus 2018, Luminy 2019, 2020, Oberwolfach 2021) Professor Butucea has received multiple research grants including ANR HIDITSA (2017-2021), ANR SPADRO (2013-2017), and ANR DIONISOS (2012-2016). She was the Principal Investigator of an ANR project on "Statistics for quantum physics" (2007-2008). She has also been awarded research stays at CIRM Luminy and MFO Oberwolfach. She is actively involved in the academic community as a member of the IMS (Institute of Mathematical Statistics) and the Bernoulli Society. She is also a member of the Institut des Actuaires as an Actuary ISUP.

Sait Erkovanمشاهده پروفایل

استادیار

• Inverse Problems
• Partial Differential Equations
• Mathematical Modeling
• +۳ مورد دیگر

Asst. Prof. Dr. Sait Erkovan is currently affiliated with Yeditepe University's Faculty of Computer and Information Sciences, Department of Information Systems and Technologies. He has previously held academic positions at Halic University (2020), Halic University's Faculty of Engineering, Department of Software Engineering (2018), Piri Reis University (2010), and Gebze High Technology Institute (2010). PhD in Mathematics from Gebze High Technology Institute (2019) MSc in Mathematics from Sakarya University (2010) and Gebze High Technology Institute (2012) BSc in Mathematics from Ataturk University (2009) His research focuses on inverse problems in heat equations, bioheat transfer modeling, and orthogonal polynomials. He has published extensively on numerical methods for determining unknown coefficients in heat equations, with applications in thermal analysis and biomedical engineering. The published works highlight his expertise in theoretical and computational approaches to inverse problems, Fourier series analysis for bioheat transfer, and spherical orthogonal polynomials in interpolation. His research bridges mathematical theory with practical engineering and biomedical applications. He has supervised Master’s theses, including TUTKU DOH’s 2022 study on machine learning algorithms for network traffic analysis at Halic University. He has also served as Vice Department Head (2022) and Acting Department Head (2022) at Halic University.

Amit Ashokمشاهده پروفایل

استاد

• Inverse Problems
• Bayesian Inference
• Information Theory
• +۸ مورد دیگر

Amit Ashok is a Professor of Optical Sciences at The University of Arizona's Wyant College of Optical Sciences and an Assistant Professor in the Department of Electrical and Computer Engineering. His research focuses on computational sensing and quantum-inspired imaging. Education: Ph.D. (2008), University of Arizona; M.S. (2001), University of Cape Town; B.Sc. (1998), University of Swaziland Professional Affiliations: Senior Member of The Optical Society (OSA), Member of IEEE, SPIE Senior Member His research leverages physical optics, machine learning, and information theory to design optical imagers and sensors. Key areas include inverse problems, compressive imaging, and quantum-limited resolution. The Intelligent Imaging and Sensing Lab (I2SL), led by Professor Ashok, develops computational sensing frameworks using reconfigurable optics and parallel computing architectures for applications in high-dimensional imaging and X-ray anomaly detection. Scientific Awards: SPIE Best Paper Award (2010), University of Cape Town Scholarships (1999, 2000), University of Swaziland Dean's, Vice-Chancellor, and Sino-Swazi awards (1995-1998)

• Medical Imaging
• Ultrasound Tomography
• Image Registration
• +۴ مورد دیگر

Torsten Hopp is a Research Associate at the Institute for Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT). His work focuses on advanced medical imaging technologies, particularly 3D ultrasound computer tomography (USCT) and its integration with MRI and X-ray modalities for breast cancer diagnosis. Research Interests: Ultrasound tomography system development Image registration algorithms Machine learning applications in medical imaging Biomechanical and computational modeling GPU-accelerated image reconstruction Multimodal imaging for clinical workflows Publication Trends: Torsten Hopp has contributed extensively to the evolution of 3D USCT, with recent work emphasizing deep learning integration , refraction correction , and patient-specific modeling . His research bridges hardware innovation (e.g., PtQube device) with computational methods for clinical translation. Labs & Teams: Affiliated with KIT's Institute for Data Processing and Electronics , he collaborates on projects like the High Data Rate Processing and Analysis Initiative , advancing ultrasound tomography for breast cancer screening.

• Computational Electromagnetics
• Inverse Problems in Electromagnetism
• Bioimpedance and Medical Diagnostics
• +۳ مورد دیگر

Alice Reinbacher-Köstinger is an Associate Professor at the Institute for Fundamentals and Theory of Electrical Engineering, Graz University of Technology, Faculty of Electrical Engineering. Her research focuses on computational electromagnetics, inverse problems, and bioimpedance analysis. Role: Research and Teaching Research Areas: Transformer modeling, NFC systems, aortic dissection detection, and surrogate-based optimization Email: alice.koestinger@tugraz.at Her work combines advanced modeling techniques with machine learning, particularly in magnetic material characterization and bioimpedance signal analysis. Recent publications emphasize physics-informed neural networks, harmonic balance methods, and Bayesian experimental design. Current projects include optimizing sensor placement for magnetic property measurements and developing simulation tools for medical diagnostics. Her research bridges electrical engineering with biomedical applications through multiphysics simulations and data-driven methods.

Jovita Lukasikمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Neural Architecture Search
• +۴ مورد دیگر

Jovita Lukasik is a Ph.D. candidate in Computer Science at the University of Mannheim , affiliated with the Data and Web Science Group and the Computer Vision and Machine Learning Group at the Max Planck Institute for Informatics . Supervised by Prof. Dr.-Ing. Margret Keuper , her research focuses on Neural Architecture Search (NAS) and its applications in computer vision and graph-based learning systems. Education Ph.D. (2019–present), Computer Science, University of Mannheim M.Sc. (2016–2019), Mathematics in Business and Economics, University of Mannheim B.Sc. (2012–2015), Mathematical Finance, University of Konstanz Lukasik's research bridges generative NAS , graph neural networks , and inverse problem solutions . She has developed novel approaches for variational graph embeddings and stability analysis in NAS frameworks, contributing to the democratization of efficient deep learning architecture discovery. Her publication trends reveal expertise in automated machine learning with specific attention to search space optimization , graph-based NAS , and stability in differentiable architecture search . She has presented at premier venues including European Conference on Computer Vision and International Conference on Learning Representations . Lukasik actively contributes to academic communities through workshop organization (NAS @ ICLR 2021) and virtual seminar leadership on AutoML advancements.

Keun-Hwa Leeمشاهده پروفایل

استاد پژوهشی

• Underwater Acoustics
• Acoustic Signal Processing
• Porous Media
• +۳ مورد دیگر

Keun-Hwa Lee is a Research Professor in the Department of Defense Systems Engineering at Sejong University, South Korea. He holds a Ph.D. (2006) and B.S. (2000) from Seoul National University, with prior research professorship at Seoul National University (2006-2014) and a Visiting Scholar position at UCSD Marine Physical Laboratory (2011-2012). His career spans over 15 years in underwater acoustics and defense systems research. Education: Ph.D., Seoul National University, 2006 B.S., Seoul National University, 2000 Research Focus: Dr. Lee specializes in physical/data-driven acoustic signal modeling , fluid flow noise around submerged objects , porous medium/sediment acoustics , and target scattering signal processing . His work integrates fluid dynamics, machine learning, and acoustics to solve defense-related underwater sensing challenges, evidenced by major achievements in high-frequency porous medium experiments, 2D/3D acoustic propagation modeling, and ocean noise inversion techniques. Publication Trends: Recent work (2024-2025) demonstrates a decisive shift toward physics-informed neural networks and deep learning for ocean acoustics. Key themes include spectrogram-based target detection, modal wavenumber estimation, and propeller noise source localization, reflecting interdisciplinary convergence of signal processing, AI, and underwater acoustics for defense applications.

• Computational Neuroscience
• Computer Graphics
• Rendering Techniques
• +۳ مورد دیگر

Gustavo Ariel Patow is a Full Professor (Catedrático) at the Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Spain, where he leads the Geometry and Graphics Group (GGG) within the ViRVIG Research Center. He earned his PhD from the Universitat Politècnica de Catalunya, supervised by Xavier Pueyo and Alvar Vinacua, with a focus on Inverse Rendering Problems in Computer Graphics. Following a major health event in 2017 (cardiac arrest), he transitioned his research focus from Computer Graphics to Computational Neuroscience, leveraging his computational expertise to study brain function and disease. His research spans Computational Neuroscience (whole-brain dynamics, Alzheimer's modeling, brain disorders) and Computer Graphics (rendering techniques, procedural modeling). Recent work demonstrates strong interdisciplinary integration between these domains, particularly in: Whole-brain network modeling of neurological conditions Biophysical simulations of Alzheimer's pathology Advanced rendering for medical visualization Computational frameworks for cognitive states He actively advises doctoral candidates including Facundo Roffet, Albert Juncà, David Aquilué Llorens, and Kenneth Alonso Muñoz, and has supervised over 20 Master's/PhD students. As coordinator of UdG's Videogame Development and Design degree, he bridges academic research with applied technology training.

• Solid State Physics
• Organic Electronics
• Charge Transport
• +۴ مورد دیگر

Karin Zojer is an Associate Professor at the Institute of Solid State Physics, Graz University of Technology (TU Graz). Her research spans organic electronics, charge transport modeling, and porous materials analysis, with a focus on physics-informed computational approaches. Research interests include Interfacial engineering in organic semiconductors Computational modeling of charge/volatile transport μ-CT-based material characterization Kinetic Monte Carlo simulations Pore structure-property relationships Recent work emphasizes Physics-informed neural networks for transport phenomena Green hydrogen purification materials Multi-scale analysis of paper porosity Reactive diffusion in cellulose systems Quantification of descriptor correlations in porous media Her publications from 2020-2025 highlight interdisciplinary methods bridging theoretical physics, computational modeling, and industrial applications.

Vegard Antunمشاهده پروفایل

پژوهشگر ارشد

• Applied Mathematics
• Inverse Problems
• Imaging
• +۸ مورد دیگر

Vegard Antun is a Postdoctoral Fellow at the Department of Mathematics, University of Oslo , specializing in applied mathematics with a focus on inverse problems, imaging, and deep learning. Education: PhD (2020), Master's (2016), and Bachelor's (2013) degrees from the University of Oslo. Research Interests: Stability and accuracy in AI algorithms, compressive sensing, signal recovery, and mathematical paradoxes in deep learning. Key Projects: Supervised a 2022 interdisciplinary project on deep learning observables for partial differential equations. His work explores the theoretical limitations of AI, particularly the instability of neural networks in inverse problems and their implications for scientific computing, as highlighted in his research on mathematical paradoxes and Smale’s 18th problem. His publications span topics such as binary sampling , wavelet reconstruction , and data-efficient neural networks , emphasizing the tension between AI accuracy and robustness. He has contributed to understanding implicit regularization , existence of optimal decoders , and hybrid concept-based models for scientific applications.

Felix Abramovichمشاهده پروفایل

استاد

• Statistical Learning
• High-dimensional Inference
• Sparsity
• +۵ مورد دیگر

Felix Abramovich is a Professor in the Department of Statistics and Operations Research at Tel Aviv University. His research focuses on theoretical foundations of statistical and machine learning, specializing in high-dimensional inference, sparsity, model selection, and nonparametric estimation techniques. Professor Abramovich's work spans statistical learning theory, classification methods, wavelet-based estimation, and inverse problems. His research develops minimax-optimal procedures for sparse high-dimensional data and explores the interplay between Bayesian methods and frequentist optimality. Key themes include adaptive estimation under sparsity constraints, theoretical analysis of classification algorithms, and wavelet applications in nonparametric statistics. Analysis of Abramovich's recent publications reveals a strong emphasis on classification methodologies, high-dimensional regression, and deep learning theory. His work consistently develops minimax-optimal procedures, examines sparsity patterns in complex data structures, and bridges Bayesian and frequentist frameworks. The research demonstrates increasing focus on modern machine learning challenges including neural network theory and multiclass classification. Professor Abramovich actively recruits students and post-doctoral researchers with strong mathematical statistics backgrounds to work on fundamental problems in statistical learning theory and high-dimensional inference.

Vijaya Raghavendra Bollapragadaمشاهده پروفایل

استادیار

• Nonlinear Optimization
• Stochastic Optimization
• Distributed Optimization
• +۳ مورد دیگر

Raghu Bollapragada is an Assistant Professor in the Operations Research and Industrial Engineering program within the Mechanical Engineering department at The University of Texas at Austin. He is affiliated with the Machine Learning Laboratory, the Oden Institute for Computational Engineering and Sciences, and the Center for Dynamics and Controls of Material (an NSF MRSEC), focusing on algorithmic solutions for large-scale optimization challenges in machine learning and computational physics. His educational background includes: PhD in Industrial Engineering and Management Sciences, Northwestern University MS in Industrial Engineering and Management Sciences, Northwestern University Visiting Researcher at INRIA, Paris during graduate studies Bollapragada's research centers on nonlinear optimization, with expertise in constrained, stochastic, and distributed optimization frameworks. He develops algorithms that leverage modern computational infrastructure to solve complex problems in logistics, control systems, and machine learning, emphasizing scalability and hardware-aware efficiency. His work bridges theoretical convergence guarantees with practical implementation. Recent publications reveal dominant trends in decentralized optimization (gradient tracking, network pruning) and stochastic methods (adaptive sampling, finite-difference estimation), with strong connections to machine learning applications. Key themes include communication-computation tradeoffs, non-convex optimization, and second-order acceleration techniques across 10+ high-impact publications (2024-2025). His scientific awards include: IEMS Nemhauser Dissertation Award for best dissertation IEMS Arthur P. Hurter Award for outstanding academic excellence McCormick terminal year fellowship Walter P. Murphy Fellowship Bollapragada mentors PhD students (including Cem Karamanli, recent thesis defense) and undergraduate researchers (Marissa Llamas, STARS Conference presenter), with research funded by NSF grant DMS-2324643, Argonne National Laboratory, and Lawrence Livermore National Laboratory. His SANDOPT and ZOAdaQN GitHub repositories provide open-source implementations of novel optimization algorithms. He leads the Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems research group within UT Austin's Machine Learning Laboratory ecosystem, collaborating with the Center for Scientific Machine Learning on cross-disciplinary projects.

• Topological Dynamical Systems
• Ergodic Theory
• Classical and Quantum Stochastic Processes
• +۳ مورد دیگر

Artur Siemaszko is a Professor in the Faculty of Mathematics and Computer Science at the University of Warmia and Mazury in Olsztyn. He serves as a promoter for doctoral candidates and specializes in topological dynamical systems, ergodic theory, and classical/quantum stochastic processes. Department: Department of Analysis and Differential Equations Email: artur@uwm.edu.pl Research Interests: Topological Dynamical Systems Ergodic Theory Classical and Quantum Stochastic Processes Group Theory (Modular Groups) Scientific Contributions: His research spans integrability in geometric structures, spectral quantization of random walks, and the interplay between algebraic and topological properties in dynamical systems. Articles highlight applications in mathematical physics, computer science, and epidemiology. Grants & Resources: He confirms funding availability and infrastructure support for doctoral projects through the Department of Analysis and Differential Equations.

• Magnetic Resonance Imaging
• Real-Time MRI
• Medical Imaging Technology
• +۴ مورد دیگر

Professor Martin Uecker is a faculty member at the University of Göttingen's Faculty of Medicine, specifically affiliated with the Department of Diagnostic and Interventional Radiology. He serves as a primary advisor and referee for doctoral candidates working on advanced Magnetic Resonance Imaging technologies and applications. His research interests focus on the development and application of cutting-edge MRI techniques, particularly in the areas of real-time imaging, deep learning for image reconstruction, and cardiac MRI applications. His work bridges medical imaging, computational physics, and artificial intelligence to address clinical challenges in diagnostics and patient monitoring. The recent publications from his advisees demonstrate a consistent research trajectory in advancing MRI technology, with particular emphasis on real-time capabilities, computational reconstruction methods, and clinical applications across multiple organ systems. This work represents the intersection of medical physics, computer science, and clinical medicine. Professor Uecker has successfully guided nine doctoral students to completion between 2019-2025, indicating an active and productive mentorship role within the academic community at Göttingen. His advisees have produced work spanning from fundamental MRI physics to clinical applications in cardiology, neurology, and respiratory medicine.