جست‌وجوی استاد و پژوهشگر

Plamen Stefanovمشاهده پروفایل

استاد

• Inverse Problems
• Partial Differential Equations
• Geometric Analysis
• +۳ مورد دیگر

Dr. Plamen Stefanov is a Professor of Mathematics at Purdue University's Department of Mathematics within the College of Science. He holds a Ph.D. from Sofia University (1988) and has served as Associate Head for Graduate Studies from 2019 to 2023. His research focuses on inverse problems, integral geometry, partial differential equations, and microlocal analysis, with applications to scattering theory and geometric analysis. Recent work includes studies on light ray transforms, nonlinear wave phenomena, and boundary rigidity problems. His research has been supported by collaborative projects and international conferences, and he maintains an active role in the academic community through editorial work and mentoring efforts. His academic contributions span theoretical advancements in inverse scattering, tomography, and geometric analysis, with a particular emphasis on developing novel methodologies for solving inverse problems in complex systems. His work often bridges mathematical theory with practical applications in physics and engineering. Notable projects include investigations into the Lorentzian scattering rigidity problem and the recovery of nonlinear terms in wave equations. Dr. Stefanov’s expertise is reflected in over 150 peer-reviewed articles, with recent publications addressing topics such as the solid-fluid transmission problem, microlocal analysis in tomography, and the mathematical foundations of multiwave tomography. He is an active member of the inverse problems community, contributing to conferences and journal editorships.

Mario Micheliمشاهده پروفایل

مدرس ارشد

• Mathematical image processing
• Shape analysis
• Variational methods
• +۴ مورد دیگر

Mario Micheli is a Senior Lecturer in the Department of Applied Mathematics and Statistics at Johns Hopkins University, within the Whiting School of Engineering. His research focuses on mathematical image processing, shape analysis, variational methods, applied statistics, and mathematical 3D printing. He holds a Laurea in Telecommunications Engineering from the University of Padova (1999), an MS in Electrical Engineering from UC Berkeley (2001), and a PhD in Applied Mathematics from Brown University (2008). His work emphasizes modeling deformations in shape analysis and imaging/video data, leveraging differential geometry and variational methods. Notable contributions include centroid-based turbulence correction, matrix-valued kernels for shape deformation, and stochastic system analysis. He has been honored with the 2023 Robert B. Pond, Sr. Excellence in Teaching Award, 2024 Professor Joel Dean Excellence in Teaching Award, and the 2023 Excellence in Teaching, Advising, and Mentoring Award. His publications span turbulence imaging, geometric shape analysis, and stochastic systems, reflecting expertise in interdisciplinary applied mathematics. Micheli’s research bridges theoretical foundations with practical applications in computer vision, data science, and engineering systems.

Florence d'Alché-Bucمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۶ مورد دیگر

Florence d'Alché-Buc serves as Professor at Telecom Paris (Institut Polytechnique de Paris) and holds a Simons chair at Isaac Newton Institute, Cambridge (May-June 2025). She leads the Signal, Statistics and Machine Learning Research Team (S2A) within LTCI laboratory's Image, Data and Signal Department, and co-organizes the 2025 thematic programme 'Representing, Calibrating and leveraging predictive uncertainty' at Isaac Newton Institute. Her research centers on Machine Learning & Artificial Intelligence with applications in bioinformatics, medical domains, and industrial settings. Key subdomains include (Operator-valued) Kernel Methods, Structured Output Prediction, Complex data analysis, Reliable Machine Learning, and Dynamical Systems Modeling. Her work emphasizes robust theoretical frameworks for real-world deployment, particularly in safety-critical applications requiring interpretability and uncertainty quantification. Recent publications (2019-2024) reveal consistent innovation in kernel-based structured prediction, sketching techniques for scalability, and graph learning with optimal transport. She bridges theoretical advances with industrial applications through collaborations with Airbus, ENGIE, SAFRAN and others, focusing on efficient algorithms for complex data while maintaining rigorous statistical foundations. Scientific recognition includes: Simons chair at Isaac Newton Institute (2025) Ellis Fellow and Board member overseeing European AI PhD programmes She advises doctoral candidates like Jayneel Parekh and leads major initiatives including the Télécom Paris Chair on Data Science & AI (2019-2023) funded by industrial partners, and the ELIAS project (European Lighthouse on AI & Sustainability). Her service includes senior editorial roles at IEEE TPAMI and JMLR, plus program leadership at NeurIPS and ICML. As LTCI laboratory member and occasional CMAP collaborator at Ecole Polytechnique, she drives cross-institutional research through the S2A team while co-organizing international workshops on frugal AI and kernel methods.

Daniel Tenbrinckمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Biomedical Imaging
• +۶ مورد دیگر

Daniel Tenbrinck is an Academic Councilor and Acting Professor (W3) at the Department of Data Science, Faculty of Natural Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg). His career spans roles at the University of Münster, ENSICAEN (France), and FAU Erlangen-Nürnberg. He holds a PhD in Computer Science (2013) from WWU Münster. Research Focus: Data Science, Machine Learning, Biomedical Imaging, Variational Methods, Graph Theory, and Numerical Analysis. Recent Publications: Explore hypergraph p-Laplacians, Fourier neural operators for image classification, and Bregman learning frameworks for sparse networks. Awards: Received the 2022 Teaching Award from FAU's Faculty of Natural Sciences and a 2021 performance bonus for outstanding contributions. Secured significant third-party funding, including a €1.98M BMBF grant for "COMFORT" (2024) and a €915k Bavarian Digitalization grant (2023). Teaching: Offers courses in Numerics, Mathematical Image Processing, Inverse Problems, and Data Science seminars. Actively involved in academic program development through grants like the 2023 Innovation Fund for Teaching.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۳ مورد دیگر

Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.

• Machine Learning
• Quantum Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Marcus Friis Klausen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Machine Learning section. His work intersects theoretical and applied machine learning across interdisciplinary domains. Research interests include: Quantum machine learning and hardware acceleration Explainable AI and model interpretability Clinical and healthcare applications of NLP Neuroscience-informed language modeling Environmentally sustainable AI practices Geometric and non-Euclidean deep learning Recent publications show trends in: Quantum computing applications for molecular simulations Medical imaging and clinical decision support Algorithmic fairness and ethical information retrieval Neural network optimization for energy efficiency Biological data modeling and remote sensing

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۳ مورد دیگر

Jeppe Fræhr Linderød works as a Lecturer at the Department of Computer Science , University of Copenhagen. His research aligns with the department's Machine Learning section, focusing on theoretical foundations and applications in information retrieval, medical data analysis, remote sensing, and sustainability. He is part of the interdisciplinary SCIENCE AI Centre . His recent publications span diverse subfields including: Quantum machine learning and optical computing Explainable AI and feature attribution Large language models for emotion recognition Medical informatics applications Fairness in recommender systems Green/sustainable AI practices He contributes to the department's computational infrastructure, including access to a powerful compute cluster. His work often intersects with environmental and healthcare domains, particularly through projects like the TreeSense center for remote sensing applications.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۱۰ مورد دیگر

Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI

Ruslan Salakhutdinovمشاهده پروفایل

استاد

• Deep Learning
• Probabilistic Graphical Models
• Large-scale Optimization
• +۲ مورد دیگر

Ruslan Salakhutdinov is a UPMC Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. His research focuses on Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization, with applications spanning artificial intelligence, computer vision, and robotics. His recent publications demonstrate strong focus on multimodal systems, diffusion models, reinforcement learning, and self-supervised techniques. Research trends show consistent innovation in generative AI methods and their application to robotics and decision-making systems. Awards include Microsoft Faculty Fellow Sloan Fellowship Directs research group advising over 30 PhD/Master's students. Secured funding from NSF, Google, and SAP Labs. Leads the Machine Learning Department's research initiatives with international collaborations.

• Watermarking Generative AI
• Differential Privacy
• Reinforcement Learning
• +۳ مورد دیگر

Yu-Xiang Wang is Associate Professor at UC San Diego's Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering. He directs the S2ML Lab, focusing on machine learning theory and applications in privacy, reinforcement learning, and generative AI. His research develops provable algorithms for watermarking AI content, practical differential privacy, offline RL, and adaptive deep learning. Current NSF projects investigate adaptivity in neural networks and privacy-preserving ML. Publications address watermark robustness, differential privacy optimization, neural collapse phenomena, and sparse model training. Recent advances include privacy-preserving generative AI detection and adaptive algorithms for nonstationary environments. Honored with NSF CAREER award and paper awards (NeurIPS Spotlight, ICML Oral), he teaches graduate courses in machine learning and differential privacy. Research applications span healthcare, finance, and AI safety.

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor of Electrical and Systems Engineering & Radiology at the University of Pennsylvania and Director of the Center for Innovation in Data Engineering and Science (IDEAS). He holds secondary appointments in Computer and Information Science and Statistics and Data Science. His research focuses on the foundations of deep learning, trustworthy AI, and their applications in computer vision and biomedical data science. Education: B.S. (Electrical Engineering, valedictorian) from Pontificia Universidad Católica de Chile (1997), M.S. and Ph.D. (Electrical Engineering and Computer Sciences) from UC Berkeley (2000 and 2003). Prior roles include Professor at Johns Hopkins University (2004–2022) and leadership roles in institutions like MINDS and THEORINET. Research spans machine learning (mathematics of deep learning, manifold learning), computer vision (activity recognition, medical imaging), biomedical data science (cardiac MRI analysis, autism diagnosis), and dynamical systems. Awards include IEEE Fellow (2014), IAPR Fellow (2016), ACM Fellow, and the IEEE Edward J. McCluskey Technical Achievement Award (2021). Current lab members include 15+ graduate students and postdocs working on topics like adversarial robustness, optimization theory, and biomedical applications. His work is supported by grants from the Department of Defense and collaborations with organizations like Amazon and NORCE. Labs/Teams: Director of IDEAS Center, MINDS, and THEORINET. Active in editorial roles for journals like TPAMI and CVIU, and program chair for ICCV 2015 and CVPR 2014.

• Numerical Analysis
• Optimization on Manifolds
• Riemannian Geometry
• +۳ مورد دیگر

Ronny Bergmann is an Associate Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU) since March 2021. Previously, he held research positions at TU Chemnitz (2018–2021) and TU Kaiserslautern (2013–2018), where he completed his habilitation in 2018. He earned his PhD in 2013 from Universität zu Lübeck under the supervision of Jürgen Prestin. His research focuses on numerical optimization on Riemannian manifolds, geometric computing, and manifold-valued data analysis. Key contributions include the development of the Manifolds.jl and Manopt.jl packages in Julia, which provide frameworks for optimization and data analysis on manifolds. He also works on applications in imaging, inverse problems, and geometric inverse methods. Recent work emphasizes non-smooth optimization on manifolds, Fenchel duality in non-Euclidean spaces, and geometric algorithms for shape analysis. His publications span journals like SIAM Journal on Scientific Computing , Journal of Optimization Theory and Applications , and Applied and Computational Harmonic Analysis . Bergmann teaches courses such as TMA4125 Mathematics 4N and actively promotes reproducible research through tools like Quarto. He collaborates on projects involving Lie groups, mesh denoising, and geometric deep learning.

Barbie Kleinمشاهده پروفایل

دانشیار

• Anatomy Education
• Instructional Design
• 3D Printing
• +۳ مورد دیگر

Dr. Barbie Klein is an Associate Professor with joint appointments in the Department of Anatomy (School of Medicine) and Department of Cell and Tissue Biology (School of Dentistry) at UCSF. She co-directs the Anatomy Learning Center and advises the Willed Body Program. Her research focuses on interprofessional anatomy education, instructional design, and applications of 3D printing/virtual reality in medical education. Her work spans: Gross anatomy/histology curriculum development VR-based medical simulations Deep learning for medical image reconstruction Innovative pedagogy assessment Honors include induction into the UCSF Academy of Medical Educators (2024). She leads NIH-funded projects on educational technology integration and maintains collaborations with engineering departments for imaging research. Current projects explore AI-enhanced spectroscopic microscopy and federated learning for cancer staging.

• Signal Processing
• Biomedical Engineering
• Machine Learning
• +۳ مورد دیگر

Pei-Chun Su is a Gibbs Assistant Professor at Yale University. Her research focuses on advanced signal processing techniques applied to biomedical problems, particularly in fetal electrocardiogram (ECG) analysis, optimal shrinkage methods for high-dimensional data, and clinical applications of noise reduction algorithms. She holds an email address at Yale: pei-chun.su@yale.edu. Her work integrates mathematical theory with practical medical challenges, such as recovering fetal heart signals from maternal ECG recordings and developing robust metrics for high-dimensional datasets. Recent projects include applying neural operators and modern statistical methods to improve signal clarity in complex biomedical scenarios. No scientific awards or grants are explicitly mentioned in the provided information. She has no listed advisees or teams, though her research interests suggest potential collaboration in biomedical engineering and data science.

Robert Beinertمشاهده پروفایل

مدرس ارشد

• Applied Mathematics
• Inverse Problems
• Optimal Transport
• +۳ مورد دیگر

Robert Beinert is a Senior Lecturer (Privatdozent) in Applied Mathematics at the Technische Universität Berlin, affiliated with the Department of Applied Mathematics within the Faculty of Mathematics and Natural Sciences. He holds a Dr. rer. nat. (Ph.D.) from Georg-August-Universität Göttingen (2015) and completed his Habilitation at Technische Universität Berlin in 2025. His research focuses on inverse problems, optimal transport, phase retrieval, and mathematical imaging, with applications in signal processing and data analysis. He has held academic positions including Research Associate roles at TU Berlin (2020–2025), Karl-Franzens-Universität Graz (2016–2020), and Georg-August-Universität Göttingen (2016). His work bridges theoretical foundations and practical applications, with contributions to regularization techniques, optimal transport theory, and algorithmic development. Key research interests include phase retrieval uniqueness analysis, Gromov-Wasserstein transport, and denoising methodologies for manifold-valued data. His recent publications emphasize advancements in optimal transport frameworks, regularization strategies, and applications in image processing and machine learning.