جست‌وجوی استاد و پژوهشگر

Ruming Zhangمشاهده پروفایل

عضو هیئت علمی

• Partial Differential Equations
• Inverse Problems
• Numerical Methods
• +۴ مورد دیگر

Prof. Dr. Ruming Zhang is a Tenure-Track Professor at TU Berlin's Faculty II - Mathematics and Natural Sciences, leading the Analysis and Applications group since May 2023. He specializes in numerical methods for partial differential equations and inverse problems, with a focus on wave scattering and periodic structures. His research bridges theoretical analysis and computational techniques, addressing challenges in areas like photonic crystals and non-destructive testing. Education & Career: PhD in Mathematics (Chinese Academy of Sciences, 2014) Postdoctoral Researcher at Michigan Technological University Marie-Curie Fellow (University of Bremen, 2015-2018) Junior Group Leader at KIT (Karlsruhe Institute of Technology, 2018-2023) Research Interests: Analysis and numerical methods for PDEs, inverse scattering problems, waveguide analysis, periodic structures, and their applications in nanotechnology and engineering. His work emphasizes high-order numerical schemes and theoretical frameworks for ill-posed problems. Key Contributions: Development of nonuniform mesh methods for periodic surface scattering, high-order numerical techniques for bi-periodic structures, and monotonicity-based shape reconstruction in waveguides. His methods address challenges in computational wave physics and mathematical modeling. Awards: Richard-von-Mises Prize (GAMM, 2023) Marie-Curie Fellowship (EU FP7-PEOPLE, 2015-2017) Teaching & Mentorship: Offers courses on inverse problems, scattering theory, boundary element methods, and applied analysis. Advises students on thesis topics in mathematical theory for photonic crystals and wave propagation. Actively promotes interdisciplinary collaboration between mathematicians and engineers. Grants & Projects: DFG Grant (2019-2024): Higher-order methods for acoustic scattering in periodic structures Marie-Curie COFUND Fellowship (Bremen TRAC, 2015-2017) Labs/Teams: Leads the Analysis and Applications research group at TU Berlin, focusing on interdisciplinary projects combining mathematical theory with computational tools for real-world applications.

Maxwell J. Mehlmanمشاهده پروفایل

استاد

• Bioethics
• Medical Ethics
• Law and Medicine
• +۵ مورد دیگر

Maxwell J. Mehlman serves as Distinguished University Professor and Arthur E. Petersilge Professor of Law at Case Western Reserve University School of Law, where he also co-directs the Law-Medicine Center. Additionally, he holds a professorship in the Department of Bioethics at the Case Western Reserve University School of Medicine. His dual appointments bridge the critical intersection of law, medicine, and ethics. Dr. Mehlman earned his Juris Doctor from Yale Law School in 1975 and holds two bachelor's degrees—one in Political Science from Reed College (1970) and another in Philosophy, Politics and Economics from Oxford University (1972), where he studied as a Rhodes Scholar. Before joining the Case Western Reserve faculty in 1984, he practiced law with Arnold & Porter in Washington, D.C., specializing in federal regulation of health care and medical technology. Mehlman's research spans the complex terrain where medical practice, legal frameworks, and ethical considerations converge. His work focuses particularly on genetic engineering, biomedical enhancement, patient-physician relationships, military bioethics, and the legal implications of medical practice guidelines. He explores how emerging biotechnologies challenge existing social structures and ethical norms, with particular attention to issues of equality, justice, and individual rights in the era of human enhancement. His publications reveal a consistent focus on the societal implications of biomedical advances, particularly in genetic engineering and human enhancement. Mehlman's scholarship demonstrates how legal frameworks struggle to keep pace with rapid scientific developments, often creating ethical dilemmas that require innovative policy solutions. His work on medical malpractice guidelines shows his interest in practical applications of bioethics in healthcare delivery systems. Co-author of Access to the Genome: The Challenge to Equality Co-editor with Tom Murray of the Encyclopedia of Ethical, Legal and Policy Issues in Biotechnology Co-author of Genetics: Ethics, Law and Policy , the first casebook on genetics and law Mehlman has dedicated his career to examining the governance challenges posed by emerging biomedical technologies. His research on DIY gene editing, military bioethics, and performance enhancement in sports demonstrates his commitment to addressing ethical questions before they become widespread societal problems. His work bridges theoretical ethical frameworks with practical legal applications, making significant contributions to both academic discourse and policy development in bioethics.

Daniel D. De Haanمشاهده پروفایل

پژوهشگر ارشد

• Philosophical Anthropology
• Philosophical Theology
• Philosophy of Cognitive Science
• +۵ مورد دیگر

Daniel D. De Haan serves as the Frederick Copleston Senior Research Fellow and Lecturer in Philosophy and Theology in the Catholic Tradition at Campion Hall and Blackfriars, University of Oxford. He is also a Research Fellow in the Ian Ramsey Centre for Science and Religion within the Faculty of Theology and Religion. As principal investigator, he leads the "Conceptual Clarity Concerning Human Nature" project sponsored by the Templeton World Charity Foundation. Dr. De Haan received his doctorate in philosophy in 2015 from the Center for Thomistic Studies at the University of St Thomas, Houston, Texas, and the De Wulf-Mansion Centre for Ancient, Medieval and Renaissance Philosophy at the Catholic University of Leuven, Belgium. Prior to Oxford, he was a postdoctoral fellow on the neuroscience strand of the "Theology, Philosophy of Religion, and the Sciences" project at the University of Cambridge. His research focuses on philosophical anthropology, examining how moral psychology, phenomenology, neuroscience, and metaphysics inform our understanding of human persons. He investigates challenges neuroscience and evolution pose for hylomorphism, along with questions in philosophy of religion and medieval philosophy, particularly Thomas Aquinas and Avicenna. His work demonstrates a distinctive integration of Thomistic philosophy with contemporary cognitive science and neuroscience. Analysis of Dr. De Haan's publications reveals consistent engagement with Thomistic philosophical anthropology, exploring the relationship between intellectual operations and psychosomatic processes. His work spans medieval philosophy, metaphysics, philosophy of mind, and theology-science dialogue, with notable application of Thomistic concepts to contemporary philosophical challenges in cognitive science. Dr. De Haan has secured significant research funding as principal investigator for the Templeton World Charity Foundation project "Conceptual Clarity Concerning Human Nature." His scholarly activities include editorial work on collections related to medieval philosophy and active participation in interdisciplinary research bridging theology, philosophy, and cognitive science. At Oxford, Dr. De Haan is affiliated with both Campion Hall and Blackfriars, institutions with strong connections to Catholic intellectual tradition. His work with the Ian Ramsey Centre positions him at the intersection of theological inquiry and scientific investigation, contributing to dialogues about religion and scientific understanding.

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +۳ مورد دیگر

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

• Structural Health Monitoring
• Civil Engineering
• IoT/Edge Computing
• +۵ مورد دیگر

Dr. Zhen Peng is a Research Fellow at Curtin University's School of Civil and Mechanical Engineering, part of the Faculty of Science and Engineering. He holds an ARC Early Career Industry Fellowship (2025–2028), focusing on developing cost-effective bridge monitoring systems using computer vision and edge computing in collaboration with Main Roads WA. His work bridges structural engineering, IoT/edge computing, and machine learning to enhance infrastructure safety. Dr. Peng earned his PhD from Curtin University (Chancellor's Commendation, 2022). His research emphasizes structural dynamics, nonlinear damage detection, and mobile crowdsensing frameworks for infrastructure monitoring. He has published extensively in top journals like Engineering Structures and Structural Control and Health Monitoring , receiving notable awards such as the 2023 Best Paper Award and a Gold Medal in the China Postdoctoral Innovation Competition. His current projects include deploying IoT-driven systems for real-time bridge condition assessment and training students via available 2025 PhD scholarships. Dr. Peng teaches courses in civil engineering and structural analysis, contributing to both academia and industry through innovation in smart infrastructure technologies.

Timothy Duffمشاهده پروفایل

استادیار

• Algebraic Geometry
• Computer Vision
• Numerical Analysis
• +۱ مورد دیگر

Timothy Duff is an Assistant Professor in the Mathematics Department at the University of Missouri's College of Arts and Science. He co-organizes the Math & Data Seminar and specializes in applied computational algebraic geometry for 3D reconstruction in computer vision. Research integrates algebraic geometry with machine learning and numerical analysis to solve geometric problems in imaging systems. Core interests include multi-view geometry, minimal solvers, and certified numerical methods. Recent publications emphasize efficient algorithms for camera calibration, 3D reconstruction, and polynomial system solving. Work frequently develops tools in Macaulay2 and addresses theoretical challenges in computer vision through algebraic frameworks.

Alina Roitbergمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +۷ مورد دیگر

Alina Roitberg is a Junior Professor (Assistant Professor) at the University of Stuttgart , affiliated with the Faculty of Computer Science, Electrical Engineering and Information Technology . Her research focuses on advancing computer vision, machine learning, and robotics applications, particularly in human activity recognition, domain adaptation, and synthetic data generation. She explores challenges in action understanding, cross-domain generalization, and real-world deployment of AI systems in fields like healthcare, autonomous vehicles, and industrial automation. Her work emphasizes robust learning under noisy conditions, multimodal data fusion, and ethical AI applications. Recent projects include foundational studies on large language models in construction (AEC), video-based muscle group estimation, and improving driver activity recognition for autonomous vehicles. She also investigates circular factory design through uncertainty-aware process optimization and human-robot interaction. Dr. Roitberg's contributions span academic publications and industrial collaborations, addressing both theoretical advancements and practical implementations. Her research bridges computer vision techniques with real-world problems, emphasizing scalability and ethical considerations in AI deployment.

Daniela Calvettiمشاهده پروفایل

استاد

• Large Scale Scientific Computing
• Computational Inverse Problems
• Uncertainty Quantification
• +۴ مورد دیگر

Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.

Enno Mammenمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Additive Models
• +۶ مورد دیگر

Enno Mammen is a Professor of Mathematical Statistics at Heidelberg University, leading the Institute for Applied Mathematics. His career includes roles as Chair for Mathematical Statistics at Heidelberg (2014–present), Chair for Statistics at the University of Mannheim (2003–2014), and various academic positions since 1986. He holds a PhD (1983) and habilitation (1992) from Heidelberg University. Research interests focus on nonparametric statistics, bootstrap methods, additive models, high-dimensional data, and statistical theory. Key contributions include foundational work on the wild bootstrap, penalized nonparametric estimators, and nonparametric diffusion models. He has authored over 150 papers in top journals like the Annals of Statistics and Biometrika. Current research spans Hawkes processes, neural network statistics, and non-Euclidean data analysis. He has supervised 12 PhD students since 2010, with many progressing to academic roles. Awards include the Heinz Maier Leibnitz Prize (1989) and IMS Fellowship (1998). Active in editorial roles for journals like the Annals of Statistics and Bernoulli. Major funding includes leadership of the DFG-funded Research Training Group 'Statistical Modeling of Complex Systems' (2013–2022) and collaborations with Russian institutions on stochastic differential equations.

• Computer Vision
• Robotics
• Medical Imaging
• +۴ مورد دیگر

Yuhao Chen is a Research Assistant Professor at the University of Waterloo, specializing in cutting-edge research at the intersection of computer vision, robotics, and healthcare. His work focuses on 3D reconstruction, food tracking, medical imaging, and AI-driven solutions for nutrition analysis and sports analytics. He has contributed to benchmark datasets like NutritionVerse, MetaGraspNet, and FoodVerse, advancing applications in robotic grasping, dietary intake estimation, and human-object interaction analysis. Research interests include egocentric video analysis, real-time 3D reconstruction, zero-shot learning, and multi-task learning. His projects often integrate Gaussian splatting, photometric SLAM, and diffusion models to solve complex problems in food tracking, medical image segmentation, and sports player motion analysis. Recent work highlights include FoodTrack for dietary monitoring and RepViT-MedSAM for medical image segmentation. Yuhao Chen’s innovations span robotics, healthcare, and AI, with a focus on practical applications such as automated nutrition assessment, robotic bin picking, and athlete performance analysis. His research emphasizes scalable frameworks and physically informed 3D reconstruction methods to address real-world challenges in health, sports, and automation.

Pascal Fuaمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +۶ مورد دیگر

Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.

• Numerical mathematics
• Technical mathematics
• Analysis
• +۶ مورد دیگر

Barbara Kaltenbacher is a Professor at the Institute of Mathematics, University of Klagenfurt. She serves as Deputy Head of the Institute and is actively involved in academic governance through roles in curricular commissions for Information Technology and Mathematics. Her research focuses on nonlinear acoustics , inverse problems , and partial differential equations , particularly in modeling wave propagation and parameter identification. Her work spans theoretical and applied domains, including Acoustic nonlinearity parameter tomography Fractional regularization techniques Optimization of imaging and ultrasound models Well-posedness of nonlinear PDEs Her recent publications address advanced mathematical challenges in nonlinear acoustics and inverse problems, with applications in medical imaging and materials science. She contributes to academic leadership through committee memberships and maintains active research collaborations across disciplines.

Irwin Youseptمشاهده پروفایل

استاد

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

Prof. Dr. Irwin Yousept is a Full Professor of Mathematics at Universität Duisburg-Essen, leading the research group AG Optimal Control of Partial Differential Equations. His work focuses on the mathematical analysis and numerical solutions of electromagnetic problems, particularly in superconductivity and inverse problems. He holds a PhD from TU Berlin (2008) and has held academic positions at TU Darmstadt and TU Berlin. His research includes PDE-constrained optimization, numerical analysis, and applications in high-temperature superconductivity and electromagnetic shielding. Affiliations: Universität Duisburg-Essen, Fakultät für Mathematik Education: Diplom (2005), PhD (2008) in Mathematics from TU Berlin Research interests span Maxwell's equations, numerical methods for PDEs, and optimal control, with applications in superconductivity, electromagnetic shielding, and induction heating. He has authored over 40 publications and received awards including the Richard-von-Mises-Preis GAMM (2014). Current grants include DFG-funded projects on inverse problems and superconductivity.

Nan Chenمشاهده پروفایل

دانشیار

• Applied Mathematics
• Uncertainty Quantification
• Data Assimilation
• +۷ مورد دیگر

Nan Chen is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison, and a faculty affiliate of the Institute for Foundations of Data Science (IFDS), a multi-University TRIPODS Phase II Initiative. His research spans applied mathematics with applications in atmosphere-ocean science, climate dynamics, and data science. Education: PhD from Courant Institute of Mathematical Sciences (CIMS) and Center of Atmosphere and Ocean Science (CAOS), New York University (NYU), May 2016 Postdoc research associate at CIMS, NYU (June 2016-May 2018) Master's degree from School of Mathematical Sciences, Fudan University, Shanghai Undergraduate in Mechanical Engineering, Fudan University, Shanghai Visited Department of Scientific Computing at Florida State University working with Dr. Max Gunzburger and Dr. Xiaoming Wang Nan Chen's research focuses on contemporary applied mathematics, particularly modeling complex systems, stochastic methods, numerical algorithms, and data science. He specializes in uncertainty quantification (UQ), data assimilation, and developing statistically accurate algorithms to address the curse of dimensionality in large-dimensional complex dynamical systems with strong non-Gaussian features. His work has significant applications in atmosphere-ocean science, including predicting phenomena such as the Madden-Julian Oscillation (MJO), monsoons, El Niño Southern Oscillation (ENSO), and sea ice dynamics. He has also extended his research to material science, neuroscience, and other complex systems. His recent publications demonstrate expertise in inverse problems, wave equations, numerical methods, and data compression techniques that blend mathematical theory with practical applications. Dr. Chen has authored a book titled "Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation, and Reduced-Order Models" published by Springer, and a tutorial paper "Taming Uncertainty in a Complex World: The Rise of Uncertainty Quantification — A Tutorial for Beginners" in the Notices of the AMS. Professional Activities: Organizing "Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications" (August 21-22, 2025) Author of two articles in Elsevier's Reference Module in Earth Systems and Environmental Sciences Participant in Wisconsin Science and Computing Emerging Research Stars (WISCERS) program Judge for Outstanding Student Paper Award (OSPA) program at American Geophysical Union (AGU) fall meetings Involved in Madison Experimental Mathematics Lab (MXM Lab) Dr. Chen actively mentors undergraduate students for research during semesters and summers, encouraging them to present at the UW undergraduate symposium. He also offers reading and independent study courses for interested undergraduates. He is currently seeking highly motivated PhD students to join his research group with possible Research Assistantship support.

Norbert Pauloمشاهده پروفایل

استاد

• Philosophy of Digitalization
• Ethics of Artificial Intelligence
• Empirically Informed Ethics
• +۶ مورد دیگر

Prof. Dr. Norbert Paulo is the Endowed Professor for Philosophy and Ethics of Digitalization at the Catholic University of Eichstätt-Ingolstadt (KU), located within the School of Transformation and Sustainability. His work bridges classical philosophical inquiry and contemporary challenges posed by digital technologies and artificial intelligence. Research Interests: Philosophy and ethics of digitalization and AI Methodology of practical philosophy, especially empirically informed ethics Moral psychology and experimental philosophy Political philosophy, applied ethics, legal philosophy, and history of philosophy Prof. Paulo’s recent publications investigate how moral theory can guide the design and regulation of autonomous vehicles, explore foundational questions in empirical ethics, and bring historical philosophical texts into dialogue with modern ethical debates. Labs & Teams: While no specific laboratory is mentioned, his endowed professorship suggests institutional support for interdisciplinary research connecting philosophy, law, psychology, and technology studies.