جستجوی استاد راهنمای دانشگاهی

Peter Oswaldمشاهده پروفایل

استاد

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +۷ مورد دیگر

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.

Nils Sturmaمشاهده پروفایل

پژوهشگر ارشد

• Causal Inference
• Graphical Models
• High-dimensional Statistics
• +۱ مورد دیگر

Nils Sturma is a Research Fellow at the Chair of Mathematical Statistics (Technical University of Munich), working under Prof. Mathias Drton. His research spans Causal Inference , Graphical Models , High-dimensional Statistics , and Algebraic Statistics . PhD in Mathematical Statistics (2024, TU Munich, advisor: Mathias Drton) Master in Mathematical Finance and Actuarial Science (2021, TU Munich) Bachelor in Mathematics (2018, University of Freiburg) His work applies algebraic geometry to statistical problems, particularly in causal discovery and model identifiability . Recent publications focus on testing polynomial constraints, latent variable models, and multi-domain causal learning. Notable collaborations include Caroline Uhler (MIT/Broad Institute), Chandler Squires (MIT), and Dennis Leung. He will join EPFL in September 2025 as a postdoc under Mats Stensrud and Victor Panaretos.

• Digital Holography
• Image Processing
• Deep Learning
• +۵ مورد دیگر

Bruno Cornelis is a postdoctoral researcher at Vrije Universiteit Brussel's Electronics and Informatics school, with a focus on biomedical signal processing, digital holography, and deep learning applications. His work spans both fundamental and applied research projects. Education : Master's thesis on Quantization-based Watermarking (2007) and Digital Holographic Image Processing (2007) Research Interests include: Deep Learning for Image Restoration and Biomedical Applications Sparse Signal Representations in Holography and Anomaly Detection Photoplethysmography (PPG) Signal Analysis Art Classification via Mathematical Signal Models Recent Publication Trends show a shift towards real-time traffic data disaggregation, PPG-based health monitoring, and deep learning architectures for image reconstruction. His work often bridges theoretical signal processing with practical biomedical/transportation applications. Honors : BARCO/FWO Master Thesis Award (2007) Collaborations involve EU-funded initiatives like GEAR and Tech4Health, with cross-disciplinary teams in Belgium and China. He has presented at IEEE conferences on transportation systems (ITSC 2022) and medical imaging (DATE 2025).

Patrick L. Combettesمشاهده پروفایل

استاد

• Numerical Analysis
• Optimization
• Convex Analysis
• +۳ مورد دیگر

Patrick L. Combettes is a Professor in the Department of Mathematics at North Carolina State University , where he was hired in 2016 as part of the Chancellor’s Faculty Excellence Program in Data-Driven Science . His work centers on numerical nonlinear analysis and optimization , particularly applications to data science , signal processing , and image recovery . Prior to NC State, he held positions at the City University of New York (1990–1999) and Université Pierre et Marie Curie in Paris (1999–2016), where he achieved the rank of Professeur de Classe Exceptionnelle . Education : PhD in Mathematics (1989) from NC State, Habilitation from Université Paris Sud (1996) Research Interests : Convex optimization, proximal algorithms, monotone operator theory, and their applications to high-dimensional data analysis, signal/image processing, and inverse problems. Scientific Awards : SIAM Fellow (2024) for contributions to convex optimization IEEE Fellow (2005) for signal/image processing Best Paper Award (IEEE Signal Processing Society, 1993) Grants and Leadership : Founding director of the CNRS research consortium MOA (2009–2013), focusing on mathematical optimization and applications. His recent publications highlight advancements in proximal methods , stochastic iterations , and monotone operator splitting , with applications to image decomposition , signal reconstruction , and statistical modeling . Collaborations span institutions in France, the U.S., and Chile, emphasizing cross-disciplinary approaches to data science and computational mathematics .

Enric Boixمشاهده پروفایل

استادیار

• AI Theory
• Neural Networks
• Machine Learning Foundations
• +۷ مورد دیگر

Enric Boix is an Assistant Professor at the University of Pennsylvania , affiliated with The Wharton School and the Statistics and Data Science Department . His research focuses on the mathematical foundations of deep learning, including neural network training dynamics, inductive bias, adversarial robustness, and AI safety. Undergraduate: Princeton University (advised by Emmanuel Abbe) PhD: MIT EECS (advised by Guy Bresler and Philippe Rigollet) Postdoctoral: MIT Mathematics & Harvard CMSA Current work investigates theoretical aspects of AI, such as: Feature learning in neural networks Model distillation and fine-grained expert architectures Chain-of-thought reasoning in LLMs Inductive bias in ResNets and Transformers NTK approximation validity and optimization dynamics Adversarial prompt vulnerabilities His publications span top conferences like NeurIPS, ICLR, and EMNLP, emphasizing mathematical rigor in understanding deep learning phenomena. Awards include: NSF Graduate Research Fellowship Siebel Fellowship Apple AI/ML Fellowship He has collaborated extensively with researchers including Mikhail Belkin, Emmanuel Abbe, and Philippe Rigollet, focusing on theoretical advancements to improve AI efficiency and trustworthiness.

Dawit Aberraمشاهده پروفایل

دانشیار

• Mathematical Analysis
• Computational Biology
• Applied Mathematics
• +۳ مورد دیگر

Dawit Aberra is an Associate Professor in the Department of Mathematics and Computer Science at Fort Valley State University's College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of Arkansas, a Post Graduate Diploma in Mathematics from ICTP (Italy), and M.S. and B.S. degrees from Addis Ababa University. His research spans mathematical analysis, computational biology, and applied statistics. Research Interests: Dr. Aberra's work focuses on advanced mathematical theories and their computational applications. Key areas include: Computational biology for gene regulatory network analysis Mathematical imaging and pattern recognition Harmonic function theory and differential equations Statistical modeling for biomedical applications His recent publications highlight algorithm development for hyperspectral imaging and noise analysis in biological systems. Earlier works contributed to geometric analysis and mathematical education reform at HBCUs. Education: Dr. Aberra earned his Ph.D. from the University of Arkansas, Post Graduate Diploma from ICTP (Italy), and M.S./B.S. from Addis Ababa University, all in Mathematics. Academic Roles: He has served as Interim Chair of the Mathematics and Computer Science Department since 2012 and as Associate Professor since 2008. Previously, he presented research at mathematical conferences on pre-calculus pedagogy and critical thinking enhancement.

Asoke Nandiمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Wireless Communications
• +۷ مورد دیگر

Professor Asoke Nandi serves as Professor of Electronic & Computer Engineering at Brunel University of London, where he has been Head of Electronic and Computer Engineering since April 2013. Previously, he held the David Jardine Chair of Signal Processing at the University of Liverpool, where he established and led the Signal Processing and Communications Research Group. His academic journey began with a PhD from the University of Cambridge, followed by positions at prestigious institutions including Rutherford Appleton Laboratory, CERN, Queen Mary University of London, the University of Oxford, Imperial College London, and the University of Strathclyde. Professor Nandi's research spans theoretical developments in signal processing and machine learning, with applied contributions across multiple domains. His work encompasses wireless communications (including automatic modulation recognition and equalization), biomedical signal processing (breast cancer detection, electrocardiogram extraction, retinal image processing), machine condition monitoring, image forensics, time series predictions, genomic signal processing (gene clustering), brain signal processing (music, EEG, and fMRI data processing), and Big Data analytics. These diverse research interests demonstrate his interdisciplinary approach to information engineering problems. His recent publications reveal a strong focus on deep learning applications across multiple domains, with emphasis on medical imaging (particularly cancer diagnosis and segmentation), remote sensing change detection, fault diagnosis in mechanical systems, and innovative approaches to image processing. The research shows a clear trajectory toward more sophisticated neural network architectures including attention mechanisms, diffusion models, and multimodal fusion techniques applied to real-world engineering and medical challenges. IEEE Transactions on Radiation and Plasma Medical Sciences Best Paper Award (2025) Foreign Member of Chinese Society for Vibration Engineering (2025) Member of Academia Scientiarum et Artium Europaea (2023) Member of Academia Europaea (2023) Fellow of Royal Academy of Engineering, U.K. (2014) IEEE Distinguished Lecturer (EMBS, 2018-2019) Finland Distinguished Professor Award (2010-2014) Co-discoverer of W+, W-, and Z0 particles (1983) Professor Nandi has supervised numerous PhD students and research projects across his career, though specific student names aren't listed in the provided materials. His work has been supported by various research grants that have enabled his prolific output of over 650 technical papers, including more than 320 in quality international journals, with an impressive h-index of 92 according to Google Scholar. His research group IEHS at Brunel focuses on information engineering and health systems, reflecting his interdisciplinary approach. Professor Nandi maintains active collaborations through his visiting professorships at institutions including Xi'an Jiatong University (China), Universite d'Orleans (France), and Tongji University (China), as well as his previous adjunct professorship at the University of Calgary (Canada). These international connections enrich his research environment and provide opportunities for cross-cultural scientific exchange.

• Algebraic Geometry
• Commutative Algebra
• Enumerative Geometry
• +۳ مورد دیگر

Dr. Andreas Kretschmer is a scientific collaborator in the Algebraic Geometry group led by Prof. Gavril Farkas at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. Previously, he spent five months as a member of the Nonlinear Algebra group at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig. He completed his PhD in September 2024 at Otto von Guericke University Magdeburg (OvGU) under the supervision of Professors Benjamin Nill and Thomas Kahle, where he was part of the MathCoRe research training group. His academic journey includes a master's degree from Bonn University with Georg Oberdieck and a bachelor's degree from Friedrich-Alexander University Erlangen-Nuremberg (FAU). Dr. Kretschmer's research focuses on the intersection of algebraic geometry, commutative algebra, and combinatorics. His work addresses fundamental problems in enumerative geometry, particularly concerning cubic hypersurfaces, as well as the study of lattice polytopes through their local h*-polynomials. He has made significant contributions to algebraic statistics, exploring generalizations of Gaussian graphical models. His research demonstrates a strong connection between theoretical mathematics and practical applications, bridging abstract algebraic concepts with concrete combinatorial structures. His publication record shows a consistent trajectory of high-quality research in top mathematics journals, with a clear focus on the structural properties of algebraic varieties and their combinatorial representations. Recent work demonstrates increasing sophistication in handling complex algebraic structures while maintaining connections to geometric intuition. Dr. Kretschmer is actively engaged in the mathematical community, regularly presenting his work at international conferences including SIAM Conference on Applied Algebraic Geometry, MEGA conferences, and specialized workshops at institutions like Oberwolfach. He has also supervised research interns through the RISE Germany program of the DAAD, demonstrating commitment to mentoring the next generation of mathematicians. His current work within the Algebraic Geometry group at HU Berlin continues to explore deep connections between algebraic structures and geometric phenomena, with particular emphasis on the combinatorial aspects that underlie complex algebraic varieties.

Zhiyuan Liuمشاهده پروفایل

دانشیار

• Large Language Models
• Natural Language Processing
• Knowledge Graphs
• +۴ مورد دیگر

Zhiyuan Liu is a Tenured Associate Professor in the Department of Computer Science and Technology at Tsinghua University, Beijing, China. He has held this position since December 2021, having progressed through the ranks from Assistant Professor (2016-2017), Tenure-track Associate Professor (2017-2021), and Assistant Researcher (2013-2016). His academic journey at Tsinghua began with his undergraduate studies (2002-2006), continued through his PhD (2006-2011), and included a postdoctoral fellowship (2011-2013). Dr. Liu's research centers on Large Language Models , Natural Language Processing , Knowledge Graphs , and Social Computing . His work spans knowledge-guided NLP, representation learning, and practical applications in domains including legal intelligence and multimodal systems. He has made significant contributions to knowledge representation learning, network embedding techniques, and few-shot natural language processing methodologies. His publication record demonstrates a clear evolution toward large-scale language models and knowledge-integrated NLP systems. Recent work focuses on efficient pretraining and fine-tuning methods for LLMs, multimodal LLMs, tool learning with LLMs, and domain-specific large models for scientific intelligence. His research shows strong interdisciplinary applications, particularly in legal intelligence and social computing, with several papers receiving prestigious recognition including F5000 awards and conference best paper honors. First Prize of Qian Weichang Chinese Information Processing Science and Technology Award (2024) World Internet Conference Awards for Pioneering Science and Technology (2023, 2022) Wu Wenjun Artificial Intelligence Science and Technology Award (2023) First Prize of the State Natural Science Award (2022, 2020) MIT Technology Review 35 Innovators Under 35, China (2018) National Young Talents Program (2020) Dr. Liu has mentored over 20 PhD students and numerous Master's students, many of whom have received top honors including Tsinghua Outstanding PhD Theses, Beijing Outstanding Graduate awards, and Siebel Scholarships. His research is supported by major grants including the Key Program of the National Natural Science Foundation of China for 'Theories and Methods of Few-shot Natural Language Processing' (2023-2027), and the Beijing Municipal Science & Technology Commission project on 'Sparse Modular Large Model Technology' (2023-2025). He leads research in the Natural Language Processing Laboratory at Tsinghua University, where his team develops cutting-edge LLM infrastructure, knowledge computing systems, and applications in legal intelligence. The lab maintains strong industry collaborations with Tencent, Baidu, and AMD, and has contributed to several successful startups emerging from his research.

Pedro Gamallo Fernándezمشاهده پروفایل

پژوهشگر

• Process Mining
• Deep Learning
• Business Process Monitoring
• +۶ مورد دیگر

Pedro Gamallo Fernández is a predoctoral researcher at the University of Santiago de Compostela (USC) , affiliated with the research center CiTIUS . He holds an FPI Research Fellowship and focuses on applying deep learning techniques to process mining problems. Education BSc in Computer Engineering, University of Santiago de Compostela (2021) MSc in Artificial Intelligence Research, AEPIA & UIMP (2022) Research Interests include process mining, predictive process monitoring, concept drift detection, contextual embeddings, and generative models. His work explores how deep learning can enhance conformance checking metrics and contextual representation in complex processes. Publication Trends show a focus on improving predictive accuracy through contextual embeddings, benchmarking deep learning models, and detecting gradual drifts in business processes. Key methodologies include autoencoders, synthetic datasets, and conformance metrics. Scientific Awards FPI Research Fellowship Laboratory : Works in Laboratorio P1 at CiTIUS, USC. His research contributes to reproducibility in deep learning and predictive monitoring frameworks like VERONA.

Ahmed Alkhateebمشاهده پروفایل

دانشیار

• Wireless Communications
• Communication Theory
• Signal Processing
• +۵ مورد دیگر

Ahmed Alkhateeb is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, machine learning, and sensing technologies, with a focus on 6G networks and beyond. He leads the Wireless Intelligence Lab and has contributed to datasets like DeepSense 6G and DeepMIMO. Ph.D., Electrical Engineering, University of Texas-Austin (2016) M.S., Electrical Engineering, Cairo University (2012) B.S., Electrical Engineering (with distinction), Cairo University (2008) Research Interests: Machine learning for wireless communication Integration of sensing and communication for 6G Multi-modal sensing (LiDAR, radar, cameras) for channel optimization AI-based wireless sensing and perception Recent publications emphasize digital twin applications, robust beamforming, and real-world testing of reconfigurable intelligent surfaces (RIS), with a focus on sim-to-real transfer and hardware constraints. His work spans vehicular/drone networks, OTFS modulation, and terahertz communication. Scientific Awards: 2012 MCD Fellowship (University of Texas-Austin) 2016 IEEE Signal Processing Society Young Author Best Paper Award NSF CAREER Award (2021) His lab develops tools like DeepSense 6G and ViWi datasets, advancing AI-driven wireless system design. He explores decentralized interference management and cell-free MIMO architectures for next-gen networks.

Amine Ammarمشاهده پروفایل

استاد

• Multiscale Modeling
• Rheology
• Numerical Simulation
• +۴ مورد دیگر

Amine Ammar is a full Professor at Arts et Métiers University , where he serves as Deputy Director of the LAMPA laboratory and Scientific Director of the Doctorate at ENSAM. His academic career spans multiple institutions including Joseph Fourier University of Grenoble and ENS de Cachan. PhD in Mechanical Engineering (ENS de Cachan, 2001) Authorization to Direct Research (University Joseph Fourier, 2006) DEA in Solids & Structures (Paris VI, 1998) Engineering Degree (ENSAM Lille/Paris, 1998) A leading expert in multiscale computational modeling , his research focuses on rheological flows , dimensional reduction techniques , and digital twin technologies for polymer processing and composite materials. He pioneered the application of Proper Generalized Decomposition (PGD) in viscoelastic flow simulations and microstructure modeling. His scientific contributions have been recognized with the ESAFORM 2009 Scientific Award and Jean Mandel Prize . With an H-index of 28 (Scopus), he has supervised key research projects on polymer crystallization and fiber suspension dynamics. Co-author of Springer monograph on Stream-Tube Method (2021) Editor of Publibook on PGD for Materials Modeling (2012) As National Facilitator for Disciplinary Teaching at ENSAM, he has developed innovative courses on computational mechanics and rheology, mentoring students like S. Montésino and E. Prulière in their thesis work on polymer microstructuring.

• Machine Learning
• Computer Vision
• Neural Networks
• +۳ مورد دیگر

Riccardo Mereu is a Doctoral Researcher affiliated with the Department of Computer Science at Aalto University. His research focuses on machine learning, computer vision, and neural network architectures, particularly addressing challenges in 3D scene reconstruction, sequential learning, and sparse representations. Recent publications highlight his work on uncertainty quantification in deep learning for computer vision tasks, function-space parameterization of neural networks, and sparse modeling techniques for improved computational efficiency. These contributions span conferences like ECCV and ICLR, emphasizing robustness and optimization in visual and sequential data processing. Dr. Mereu's collaborations include researchers from Aalto University, with co-authors such as Arno Solin and Joni Pajarinen. His work bridges theoretical advancements in neural network modeling with practical applications in computer vision and sequential learning frameworks.

• Matrix and Tensor Factorizations
• Riemannian Optimization
• Cancer Classification
• +۵ مورد دیگر

Max Pfeffer is an Assistant Professor at the Institute for Numerical and Applied Mathematics within Georg-August-Universität Göttingen (since 2023). He previously held research and adjunct positions at TU Chemnitz, SimulaMet Oslo, Johannes-Gutenberg-Universität Mainz, and MPI MiS Leipzig. His work bridges numerical mathematics with data science applications. Current affiliations: Universität Göttingen (Junior Professor), TU Chemnitz (Adjunct Professor) Collaborators: Martin Stoll (TU Chemnitz), Evrim Acar Ataman (SimulaMet), Markus Bachmayr (Mainz), Bernd Sturmfels (MPI MiS) Research Focus Matrix/Tensor factorizations for high-dimensional data Riemannian optimization on manifolds Cancer classification through machine learning Quantum chemistry numerical methods for matrix product states Parametric PDE solutions for biomedical applications Recent Publications Highlight His 2023-2025 publications demonstrate cross-disciplinary impact: tensor decompositions for temporal data analysis, Gaussian process acceleration with tensor structures, and biomedical applications in melanoma gene selection. The work intersects numerical mathematics, machine learning, and quantum computing. Academic Background PhD in Mathematics (2018), M.Sc. (2014), B.Sc. (2011) from TU Berlin DFG-funded project on constrained matrix/tensor factorizations (2021-2023)

Dan Xuمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Deep Learning
• +۸ مورد دیگر

Dr. Dan Xu is a Postdoctoral Researcher at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford. His research focuses on computer vision, machine learning, and deep learning, particularly for 2D/3D scene understanding tasks including depth estimation, object detection, and image generation. Ph.D. in Computer Science (2018), University of Trento Research Assistant, Chinese University of Hong Kong Dr. Xu's research spans computer vision and deep learning , with specific interests in scene depth prediction , visual SLAM , object contour detection , and generative adversarial networks . Recent work explores 3D Gaussian splatting , diffusion models , and multi-task learning . Key research trends include 3D scene reconstruction , controllable video generation , and multi-modal alignment . His publications emphasize neural radiance fields , attention mechanisms , and generative models for advanced visual tasks. Best Paper Award Nominee at ACM Multimedia 2018 Best Scientific Paper Award at ICPR 2016 Student Travel Grant (SIGMM/ACM Multimedia 2016) Dr. Xu contributes to open-source projects and provides training/testing code for his research. He actively reviews for premier journals and conferences including CVPR , NeurIPS , and TPAMI .