جستجوی استاد راهنمای دانشگاهی

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Ilaria Perugia is a University Professor (Univ.-Prof.) and Chair of Numerics of PDEs at the Department of Mathematics, Faculty of Mathematics, University of Vienna. She also serves as Deputy Head of the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. Her research focuses on numerical methods for partial differential equations with applications in computational physics and engineering. Professor Perugia's primary research interests include: Numerical methods for PDEs Finite element methods Discontinuous Galerkin methods Trefftz methods Virtual element methods Space-time methods Computational electromagnetics Wave propagation problems Nonlinear reaction-diffusion problems Her work spans theoretical analysis, algorithm development, and practical implementation of numerical methods for solving complex physical phenomena. Her recent publications demonstrate a strong focus on space-time methods, virtual element methods, and structure-preserving discretizations for wave equations, heat equations, and other PDEs. She has made significant contributions to the development of stable and efficient numerical schemes that preserve important physical properties of the underlying continuous problems, particularly in the context of wave propagation and computational electromagnetics. Professor Perugia leads a research group comprising several researchers and students including Mattia Corti, Matteo Ferrari, Monica Nonino, Andrea Scaglioni, Paul Stocker, Enrico Zampa, and Marco Zank. Her group actively collaborates on projects related to numerical analysis and scientific computing, with particular emphasis on developing novel discretization techniques for challenging PDE problems.

• Numerical methods for PDEs
• Finite element methods
• Discontinuous Galerkin methods
• +۶ مورد دیگر

David Eaton is a Professor and former NSERC/Chevron Industrial Research Chair in Microseismic System Dynamics at the University of Calgary's Department of Geoscience. He holds a PhD in Geophysics from the University of Calgary (1992) and has published the textbook 'Passive Seismic Monitoring of Induced Seismicity'. Educational Background: PhD Geophysics, University of Calgary, 1992 MSc Geophysics, University of Calgary, 1988 BSc Geology and Physics, Queen's University, 1984 His research focuses on induced seismicity characterization, microseismic monitoring technology development, distributed acoustic sensing applications, physics-informed machine learning approaches, and lithospheric structure analysis. Current projects investigate earthquake triggering mechanisms during hydraulic fracturing and geothermal energy development. Publications show consistent focus on induced seismicity source characterization, monitoring methodologies, and geophysical applications for energy resource development. Recent work integrates machine learning with seismic monitoring to understand geological controls on induced seismicity. Scientific Awards: NSERC Synergy Award for Innovation (2020) J. Tuzo Wilson Medal, Canadian Geophysical Union (2020) CSEG Distinguished Lecturer (2019) Schulich School of Engineering Distinguished Collaborator (2019) University of Calgary Great Supervisor Award (2016) He leads the CREATE-REDEVELOP program training future leaders in responsible resource development and directs the microseismic research laboratory.

• Induced Seismicity
• Microseismic Monitoring
• Distributed Acoustic Sensing
• +۲ مورد دیگر

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

• Statistical Modeling
• Biostatistics
• Biomedicine
• +۷ مورد دیگر

Rupert Klein is a Professor at Freie Universität Berlin in the Department of Mathematics and Computer Science , specializing in Geophysical Fluid Dynamics . His research spans atmospheric dynamics, numerical methods, and gas dynamics of combustion. Research Interests : Geophysical Fluid Dynamics and Atmospheric Modeling Multiscale Asymptotic Analysis Wave Propagation and Turbulence Combustion and Pressure Gain Combustion Climate Dynamics and Data Assimilation Scientific Awards : DRS Award for Excellent Supervision (2014) ECMWF Fellowship (renewed 2017) His recent work includes multiscale models for atmospheric flows, vortex dynamics, and combustion processes. Key collaborations involve DFG SPP 1276, CRC 1029 (TurbIn), and CRC 1114 (SCCS) projects. He contributes to numerical methods for low-Mach-number flows and geophysical simulations.

• Geophysical Fluid Dynamics
• Atmospheric Dynamics
• Numerical Methods
• +۳ مورد دیگر

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +۸ مورد دیگر

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +۶ مورد دیگر

Prof. Miles Simon is a University Professor at the Institute for Analysis and Numerical Analysis (IAN) at Otto von Guericke University Magdeburg. His research focuses on geometric flows, particularly Ricci flow and mean curvature flow, with emphasis on singular metric spaces, curvature estimates, and geometric evolution equations. He has held academic positions since 1998, including postdoctoral roles at Max Planck Institute, Humboldt University Berlin, and Albert Ludwigs University Freiburg before becoming a full professor in 2011. Education: Completed his PhD at Melbourne University (1997) with a thesis on Ricci flow pinching phenomena. He obtained his Habilitation at Freiburg (2007) and has extensive postdoctoral training across leading institutions. Teaching: Regularly instructs advanced courses in differential geometry, geometric evolution equations, and analysis. Recent courses include Analysis III, Funktionentheorie, Differentialgeometrie II, and seminars on geometric analysis topics. Research Contributions: Over 20 peer-reviewed articles since 2000, focusing on Ricci flow regularity, curvature estimates, soliton dynamics, and geometric limit spaces. Notable work includes extending Ricci flows in 4D with bounded curvature and analyzing singularities in 3D geometries. Leadership: Project leader in DFG's SPP 2026 'Geometry at Infinity' and organizer of international workshops on nonlinear geometric diffusion equations. Active in academic administration and mentoring PhD students. Lab/Team: Supervises graduate researchers including Dr. Florian Litzinger and M.Sc. Priyamvada Vishwamitra, focusing on geometric flow applications and singularity analysis.

• Differential Geometry
• Geometric Flows
• Ricci Flow
• +۴ مورد دیگر

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +۶ مورد دیگر

Halima Chahboune is the Executive Director of the Carol and Gene Ludwig Program at Yale School of Medicine, leading research in neuroimmunity and dementia. She holds a PhD in Biomedical Engineering from Université Claude Bernard Lyon 1 and completed postdoctoral training at Yale in Radiology and Biomedical Engineering. Her career includes roles as Assistant Director at the Center for Research on Interface Structures and Phenomena (CRISP) and the Center for the Physics of Biological Function (CPBF). Her research focuses on advancing multi-modal Magnetic Resonance Imaging (MRI) techniques for applications in neuroscience, molecular imaging, and regenerative medicine. Key interests include neurodevelopmental disorders, tissue engineering, and translational imaging strategies. Her work bridges engineering and medicine, with publications in journals like Magnetic Resonance in Medicine and Stroke . Her research trends emphasize interdisciplinary approaches, leveraging MRI innovations to address challenges in regenerative medicine, neurodegenerative diseases, and developmental neuroscience. While no specific awards are listed, her contributions to imaging methodologies have been widely cited. She collaborates with prominent researchers such as Douglas Rothman and Flora Vaccarino, advancing studies on brain development and injury mechanisms. Halima’s work is affiliated with Yale’s Ludwig Program for Neuroimmunity in Dementia and the Neuroscience department, reflecting her commitment to integrating advanced imaging with biomedical research. Her office is located at 100 College Street, New Haven, CT.

• Medical Imaging
• Biomedical Engineering
• Neuroscience
• +۳ مورد دیگر

Dr. Qingguo Hong is an Assistant Professor in the Department of Mathematics and Statistics at Missouri University of Science and Technology. His research focuses on numerical analysis, numerical PDEs, applied and computational mathematics, and deep learning. He holds a Ph.D. in Computational Mathematics from Peking University (2012), an M.S. from Xiangtan University (2008), and a B.S. in Information and Computational Science from Xiangtan University (2005). Prior to his current position, he held roles as a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (Austria), Postdoctoral Scholar at the University of Duisburg-Essen (Germany), and Assistant Research Professor at Pennsylvania State University (USA). His work emphasizes innovative numerical methods, including extended Galerkin techniques, neural network-driven PDE solvers, and robust approximation frameworks for complex physical models like superconductivity and poromechanics. Research Interests: Numerical methods for partial differential equations (PDEs) Discontinuous Galerkin methods and finite element analysis Deep learning applications in scientific computing Stability analysis of numerical algorithms Phase-field modeling and material science simulations Publications Trends: Dr. Hong's recent work (2021-2025) emphasizes advancements in numerical PDEs through hybrid approaches combining classical methods (e.g., Korn’s inequalities, Galerkin methods) with modern machine learning tools (neural networks). Key themes include: Development of efficient algorithms for time-dependent and coupled PDE systems Stability frameworks for perturbed saddle-point problems Applications in superconductivity, poromechanics, and elasticity Robust discretization techniques for fluid-structure interactions Grants & Advising: While specific grants are not listed, his academic trajectory reflects sustained support through postdoctoral and research roles. He currently advises students in numerical analysis and computational mathematics.

• Numerical Analysis
• Numerical PDEs
• Applied and Computational Mathematics
• +۳ مورد دیگر

Noa Ofen is a Professor at The University of Texas at Dallas, affiliated with the School of Behavioral and Brain Sciences and the Department of Psychology. Her research focuses on developmental cognitive neuroscience, human memory, and neuroimaging techniques such as fMRI and intracranial EEG. She examines spatiotemporal brain dynamics across the lifespan, including genetic and environmental influences on cognitive development. PhD in Cognitive Neuroscience from the Weizmann Institute of Science (2004) Postdoctoral training at MIT (2011) and Stanford University (2005) Her research interests include the neural correlates of recollection, hippocampal subfield development, and socioeconomic factors influencing brain structure. Recent work emphasizes intracranial EEG insights into cognitive mechanisms and lifespan hippocampal changes. Her NIH-funded project, 'Development of memory networks in children,' investigates neural mechanisms underlying memory formation in pediatric populations. She leads the Cognitive and Brain Development Lab, actively mentoring students and advancing interdisciplinary neuroscience research.

• Developmental Cognitive Neuroscience
• Human Memory
• Neuroimaging Techniques (EEG, fMRI)
• +۳ مورد دیگر

Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.

• AI Accelerators
• GPU Reliability
• Fault Tolerance
• +۷ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر