جستجوی استاد راهنمای دانشگاهی

Lea Blessingمشاهده پروفایل

پژوهشگر

• Bayesian Inverse Problems
• Porous Media
• Uncertainty Quantification
• +۳ مورد دیگر

Lea Blessing is a Research Assistant and PhD student at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation, within the Research Group Computational Methods for Uncertainty Quantification. She holds an M.Sc. in Simulation Technology and a B.Sc. in Simulation Technology from the same university. Her research focuses on Bayesian inverse problems, porous media, and uncertainty quantification, with prior work on regularization techniques in geophysical imaging. Education: PhD Student (2025–Present): Institute of Applied Analysis and Numerical Simulation, advised by Prof. Dr. Andrea Barth. M.Sc. Simulation Technology (2022–2025): Thesis on 'An Inverse Boundary Value Problem for the Porous Medium Equation - Theory and Numerical Methods,' advised by Prof. Dr. Andrea Barth. B.Sc. Simulation Technology (2018–2022): Thesis on 'Regularization Techniques for Full Waveform Inversion in Geophysical Imaging,' advised by Prof. Dr. Dominik Göddeke. Teaching: Advanced Numerics of PDEs (Summer Term 2025). Awards: Scholarship from the German Academic Scholarship Foundation (2021–2025). Professional Experience: Student Assistant at the Institute of Applied Analysis and Numerical Simulation and Department of Hydromechanics and Modelling of Hydrosystems (2020–2022). Student Assistant at the Stuttgart Center for Simulation Science (2022–2025).

• Nonlinear Partial Differential Equations
• Cross-Diffusion Systems
• Mathematical Modeling in Semiconductor Devices
• +۵ مورد دیگر

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations at TU Wien, Austria. He leads the Institute of Analysis and Scientific Computing within the Faculty of Mathematics and Geoinformation. His research focuses on mathematical modeling, analysis, and numerical simulation of complex systems, particularly in semiconductor physics, quantum hydrodynamics, and biological transport phenomena. Jüngel holds a PhD from TU Berlin (1994) and habilitation (1997), with prior academic roles at University of Konstanz and University of Mainz. His work spans nonlinear PDEs, cross-diffusion systems, entropy methods, and applications in semiconductor modeling (e.g., quantum drift-diffusion, Maxwell-Stefan systems). Notable contributions include rigorous derivation of energy-transport models, analysis of quantum fluid models, and development of entropy-dissipative numerical schemes. Awards include the ERC Advanced Grant (2021) and Tsungming-Tu Award (2011). Teaching emphasizes advanced mathematical topics: cross-diffusion systems, partial differential equations, and computational finance. Active in PhD supervision and collaborative projects, he co-authored books on transport equations, entropy methods, and semiconductor modeling. His team includes postdocs and PhD students working on topics like multi-species populations, tumor growth modeling, and stochastic cross-diffusion systems.

Clemens Heitzingerمشاهده پروفایل

دانشیار

• Machine Learning
• Reinforcement Learning
• Nanotechnology
• +۷ مورد دیگر

Prof. Clemens Heitzinger is an Associate Professor at TU Wien, affiliated with the Forschungsbereich Machine Learning (E194-06) . His research focuses on interdisciplinary applications of machine learning, computational methods, and nanotechnology. Key areas include reinforcement learning for healthcare optimization, stochastic modeling of PDE systems, and Bayesian inversion in sensor design. He leads the project PDE models for nanotechnology under the Scientific Computing and Modelling department (E101-03). His work spans medical imaging (e.g., electrical impedance tomography), nanopore sequencing, and nanoscale sensor development. Notable contributions include algorithms for corticosteroid therapy optimization in sepsis and superhuman performance in sepsis prediction via distributional reinforcement learning. He has advised over 13 PhD and Master's students, including Tobias Kietreiber, Sebastian Bittner, and Leila Taghizadeh. His research methodologies integrate machine learning with numerical analysis, emphasizing uncertainty quantification and PDE modeling. Recent trends in his publications highlight applications in personalized healthcare, industrial anomaly detection, and computational fluid dynamics in nanoscale systems. While no scientific awards are explicitly mentioned, his work has been published in high-impact journals and conference proceedings, reflecting his contributions to computational science and engineering.

• Computational Mechanics
• Numerical Methods (Meshfree/XFEM/Isogeometric)
• Mixed Variational Methods
• +۳ مورد دیگر

Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.

Herbie Leeمشاهده پروفایل

استاد

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +۷ مورد دیگر

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Sarah Eberle-Blickمشاهده پروفایل

مدرس ارشد

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +۷ مورد دیگر

Sarah Eberle-Blick is a Senior Lecturer at the Institute of Mathematics , Goethe University Frankfurt, Department of Computer Science and Mathematics. Her work bridges numerical methods, inverse problems, and wave propagation in elasticity. Research Focus : Numerical analysis of PDEs, monotonicity methods for inclusion detection, FEM-BEM coupling, multiscale seismic data processing. Key Contributions : DFG-funded projects on elastic wave reconstruction; development of stable integral formulations for acoustic and thermoelastic wave equations; implementation of 3D wave simulations. Article Trends : Recent papers emphasize inverse problems in linear elasticity, time-harmonic wave equations, and multiscale analysis using wavelets. Collaborative work spans geophysics, computational mechanics, and mathematical modeling. Projects : Includes monotonicity-based regularization, Lipschitz stability estimation, and FEM-BEM coupling with convolution quadrature. Contact: eberle@math.uni-frankfurt.de | Room 103, Institute of Mathematics, Frankfurt am Main, Germany.

Noemi Petraمشاهده پروفایل

دانشیار

• Large-scale inverse problems
• PDE-constrained optimization
• Uncertainty quantification
• +۴ مورد دیگر

Noemi Petra is an Associate Professor in the Department of Applied Mathematics at the University of California, Merced , with research focusing on large-scale inverse problems , PDE-constrained optimization , uncertainty quantification , and optimal experimental design . Her work bridges mathematical theory with practical applications in geophysics and energy systems. Current Role: Associate Professor at UC Merced Research Interests: Large-scale inverse problems, PDE-constrained optimization, uncertainty quantification Her research has produced significant contributions in Bayesian inference for ice sheet modeling, power system estimation , and scalable optimization algorithms . She has developed open-source software frameworks like hIPPYlib-MUQ for data-model integration. Key research trends include: Bayesian Methods for geophysical inverse problems Optimization Techniques for power systems and ice dynamics Scalable Algorithms for high-dimensional uncertainty quantification Software Development for large-scale inverse problems Scientific awards: NSF CAREER award (2017) for large-scale Bayesian inverse problems She actively collaborates on ice sheet dynamics and power grid modeling , with software tools adopted in academic and industrial applications.

Rui Zhuمشاهده پروفایل

استادیار

• Data-driven modeling
• Simulation of complex systems
• Optimization
• +۹ مورد دیگر

Rui Zhu is an Assistant Professor at the School of Industrial and Systems Engineering , The University of Oklahoma , specializing in data-driven modeling, simulation, and optimization of complex systems with applications in healthcare, manufacturing, and engineering education. She earned her Ph.D. in Industrial Engineering from The Pennsylvania State University (2021), M.S. in Human Factors in Design, and B.S. in Industrial Design from Harbin Institute of Technology (2014, 2017). Research Interests: Rui's work integrates deep learning-assisted simulation , predictive modeling under model uncertainty , and optimization of advanced sensing systems . She explores virtual reality (VR) simulation and eye-tracking data analytics in VR environments, with cross-disciplinary impact on health and medical systems and cyber-physical-social systems . Publication Trends: Recent work highlights AI/ML in healthcare diagnostics ( Bayesian survival analysis , cardiac electrophysiology ), VR applications in manufacturing and education ( problem-solving modeling , adaptive virtual assistants ), and sensor optimization for industrial systems. Her research bridges theoretical innovation (e.g., spatiotemporal regularization) with practical applications (e.g., noise quality evaluation). Scientific Awards: Best Track Paper, Work Systems & Services, IISE Annual Conference (2022) Best Track Paper, Human Factors & Ergonomics, IISE Annual Conference (2021) Honorable Mention, INFORMS Conference on Service Science Best Student Paper Competition (2020) Marcus Fellowship, Penn State University (2019) Honor Graduate, Harbin Institute of Technology (2016, 2014) Advising: Actively mentoring Ph.D. students including Weixuan Dong (Fall 2022–Present) and Yuning Cui (Fall 2021–Present). She teaches undergraduate and graduate courses like ISE 3293 Applied Engineering Statistics and ISE 4633 Probabilistic Systems Models . Labs & Teams: Rui leads a lab focused on virtual reality simulation and eye-tracking analytics, collaborating with institutions like Penn State University and Harbin Institute of Technology. Her team applies interdisciplinary methods to solve challenges in healthcare, manufacturing, and educational technologies.

Alberto Sorrentinoمشاهده پروفایل

دانشیار

• Numerical Analysis
• Inverse Problems
• Medical Imaging
• +۳ مورد دیگر

Alberto Sorrentino serves as an Associate Professor in the Department of Mathematics at the University of Genoa, actively teaching Mathematical Methods for Bioengineering, Numerical Calculation, and Reverse Problems courses for Mathematics and Bioengineering degree programs through the 2025-2026 academic year. His research specializes in Numerical Analysis and Inverse Problems , developing computational frameworks for Medical Imaging and Neuroscience applications. Key contributions include probabilistic source localization tools for M/EEG, validation methodologies for electrical source imaging, and Bayesian approaches to neuroimaging inverse problems under extreme conditions like space environments. Recent publications (2023-2024) reveal a consistent focus on uncertainty quantification in brain imaging, advanced EEG analysis pipelines for space medicine, and optimization of regularization techniques for MEG connectivity. His work bridges computational mathematics with biomedical challenges through interdisciplinary collaboration. Information regarding academic advising, research grants, laboratory facilities, or team structures is not documented in the available materials.

• Optimization
• Evolutionary Algorithms
• Inverse Problems
• +۴ مورد دیگر

Maciej Smołka, PhD hab., serves as a University Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. His office is located at D-17, Kawiory 21, room 2.25, and he actively contributes to academic governance through the Computer Science Discipline Council and College of the Faculty of Computer Science. His research centers on computational optimization, specializing in metaheuristics, evolutionary algorithms, and inverse problems. He addresses complex challenges in non-convex optimization, time-delay systems, and stabilization of forward solvers, with significant contributions to hierarchical memetic strategies including the pyHMS Python library. His interdisciplinary work extends to Music Informatics, where he investigates urban soundscapes as carriers of local identity and applies computational methods to musical harmonization. Recent publications (2022-2025) demonstrate advancements in auto-configured metaheuristics for engineering optimization, socio-cognitive approaches to time-delay control, and LLM-generated algorithm design. His work bridges computational intelligence with practical applications in thermal systems and Gaussian mixture modeling, while maintaining a parallel research thread on musicology and urban acoustic ecology.

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

• Digital Rock Physics
• Computational Pore-Scale Physics
• Tomographic Imaging
• +۹ مورد دیگر

Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.

• Signal Processing
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Dr. Saïd Moussaoui is a Professor in the Department of Automation and Robotics at École Centrale de Nantes , affiliated with the Nantes Digital Sciences Laboratory (LS2N) and the Signal, Image and Sound research team. His work spans machine learning, medical imaging, and signal processing, with a focus on EEG-based mental workload classification, PET reconstruction, and 4D Flow MRI optimization. Research Areas : Signal Processing, Medical Imaging, Machine Learning, Neuroscience, Biomedical Engineering Labs/Teams : LS2N Laboratory, Signal, Image and Sound Team Recent publications highlight trends in graph learning for EEG analysis, deep learning regularization in PET imaging, and super-resolution techniques in cardiovascular MRI. His research integrates physics-based models with data-driven approaches for applications in healthcare and industrial predictive maintenance. The LS2N laboratory provides a multidisciplinary environment for his work, combining advanced computational methods with real-world applications in biomedical engineering and robotics. Collaborations with institutions like IEEE and projects on digital twins underscore his technical leadership in applied research.

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

• Geophysics
• Bayesian Inference
• Inverse Problems
• +۲ مورد دیگر

Malcolm Sambridge is a researcher at the Research School of Earth Sciences, Australian National University . His work focuses on advancing Bayesian inference methods for geophysical problems, particularly through the development of trans-conceptual sampling frameworks. In the article "Trans-Conceptual Sampling: Bayesian Inference With Competing Assumptions" , Sambridge and colleagues propose a novel approach to Bayesian sampling that generalizes trans-dimensional methods, enabling exploration across models with varying conceptual assumptions, basis functions, and forward physics. This framework eliminates the need for bespoke parameter transformations, allowing automated sampling with modern algorithms like Hamiltonian MCMC. The research demonstrates that trans-conceptual sampling achieves results comparable to reversible-jump MCMC in standard cases but extends applicability to scenarios where traditional trans-dimensional methods would fail, such as models with non-local basis functions or heterogeneous parameterizations. Applications range from airborne electromagnetic inversion to synthetic seismic tomography, showcasing the method's versatility in handling complex geophysical problems.