جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۶ مورد دیگر

Hu Guo serves as a Lecturer in the Machine Learning section at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. Located at Universitetsparken 1, Copenhagen Ø, Hu Guo contributes to the department's research activities which span from theoretical foundations of machine learning to practical applications in diverse domains including medical data analysis, remote sensing, sustainability, and biological data modeling. The department participates in the SCIENCE AI Centre at the University of Copenhagen and maintains powerful compute resources for research. The research interests of Hu Guo encompass a broad spectrum of machine learning applications. Their work bridges theoretical machine learning with practical implementations across multiple disciplines. Notably, their research spans quantum computing applications in biomolecular simulations, natural language processing for emotion recognition and cross-cultural applications, medical AI for patient analysis and diagnostics, sustainable AI development with environmental considerations, and recommender systems with fairness evaluations. This interdisciplinary approach demonstrates strong connections between computer science theory and real-world problem solving in healthcare, environmental science, and cultural applications. Analysis of Hu Guo's recent publications reveals a strong focus on interdisciplinary applications of machine learning. Their work shows particular strength in quantum computing applications, medical AI, and sustainable computing practices. The publications demonstrate a pattern of addressing both theoretical challenges and practical implementations, with significant contributions to explainable AI, cross-cultural applications of language models, and energy-efficient computing solutions. The research consistently connects machine learning theory with real-world applications across healthcare, environmental science, and computational physics domains. Hu Guo actively contributes to the Machine Learning section's research activities, which include participation in the SCIENCE AI Centre and utilization of the department's powerful compute cluster. Their research aligns with the section's focus on both theoretical foundations and practical applications of machine learning across diverse domains including information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. The work contributes to initiatives like TreeSense - Centre for Remote Sensing and Deep Learning of Global Tree Resources.

• Machine Learning
• Quantum Computing
• Artificial Intelligence
• +۴ مورد دیگر

Thore Husfeldt is a Visiting Professor at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning , Quantum Computing , and related disciplines. He is affiliated with the SCIENCE AI Centre , focusing on theoretical and applied aspects of AI, including ethical considerations, medical informatics, and algorithm design. His research spans interdisciplinary domains, integrating Quantum Computing with Computational Biology and Medical Informatics . Recent work explores Environmentally Sustainable AI , Neural Network Architecture , and Cross-Cultural Adaptation . His contributions address challenges in AI Ethics , Healthcare Applications , and Quantum Simulation . The department’s Machine Learning Section and SCIENCE AI Centre provide resources such as a powerful compute cluster and modern labs. His role involves advancing AI research through collaborative projects and leveraging computational tools for societal and scientific impact.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۴ مورد دیگر

Rasmus Mikelsons serves as a Lecturer at the Department of Computer Science , University of Copenhagen . His email contact is rami@di.ku.dk . He is affiliated with the Machine Learning section and participates in the SCIENCE AI Centre, focusing on both theoretical and applied machine learning research. Research Interests Machine Learning Natural Language Processing Medical Data Analysis Remote Sensing Sustainability Computational Biology The Machine Learning section conducts research ranging from theoretical foundations to applications in domains such as information retrieval, medical imaging, and biological data modeling. Recent publications highlight trends in quantum machine learning, sustainable AI, and multimodal applications. Labs and Teams He is part of the Machine Learning section at the Department of Computer Science, which has access to a dedicated compute cluster and participates in initiatives like TreeSense, a center for remote sensing and deep learning of global tree resources.

Stefan Horst Sommerمشاهده پروفایل

استاد

• Machine Learning
• Riemannian Geometry
• Stochastic Processes
• +۳ مورد دیگر

Stefan Horst Sommer is a Professor at the Department of Computer Science (DIKU), University of Copenhagen . He leads the Pioneer AI (P1AI) section, serves as Head of Studies for Machine Learning and Data Science, and co-founded the Center for Computational Evolutionary Morphometry (CCEM) with Rasmus Nielsen. His work bridges stochastic processes , geometric statistics , and machine learning with applications in computational anatomy and diffusion modeling . Key Roles : Head of Pioneer AI, Head of MLDS Studies, CCEM PI Labs : Applied Geometry Lab, CCEM Research focuses on Riemannian geometry , anisotropic diffusion , and stochastic shape analysis . Current projects include geometric machine learning for aerodynamic modeling and probabilistic image registration with applications in medical imaging and evolutionary biology . His 96+ publications emphasize manifold-valued processes and geometric deep learning . Collaborations span computational anatomy , stochastic mechanics , and AI-driven scientific computing . He co-organizes international workshops on geometric statistics and maintains active GitHub repositories for open-source research tools.

Ronny Bergmannمشاهده پروفایل

دانشیار

• Nonsmooth Optimization on Manifolds
• Proximal Methods
• Manifold-Valued Image Processing
• +۷ مورد دیگر

Ronny Bergmann is an Associate Professor (Førsteamanuensis) at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he has been employed since March 2021. Previously, he served as a postdoctoral researcher and deputy head of the Numerical Analysis of Partial Differential Equations group at Chemnitz University of Technology, Germany, and earlier worked within the Image Processing Group at Kaiserslautern University of Technology where he completed his habilitation in early 2018. His doctoral research was conducted at the University of Lübeck under supervision of Jürgen Prestin. Bergmann's research primarily focuses on nonsmooth optimization on manifolds , with particular emphasis on proximal methods and their applications in manifold-valued image processing. His work extends to optimization methods on manifolds and high-dimensional power manifolds, graph-based approaches for manifold-valued data processing that combine local and nonlocal methods, and anisotropic periodic translation invariant spaces with Fourier-based algorithms. His research bridges theoretical mathematics with practical computational applications in image processing and data analysis. His recent publications demonstrate significant contributions to manifold optimization theory and applications, with a strong emphasis on developing computationally efficient algorithms for manifold-valued data. The publications span topics including Riemannian proximal gradient methods, Newton's methods on manifolds, total variation techniques for mesh processing, and geometric approaches to Kalman filtering. His work often involves developing practical implementations, such as the Manopt.jl and Manifolds.jl packages in Julia, which have demonstrated substantial performance improvements over existing tools. Bergmann's research has been published in top-tier journals including SIAM Journal on Scientific Computing, Journal of Optimization Theory and Applications, and SIAM Journal on Optimization, reflecting the high quality and impact of his contributions to the field of manifold optimization and its applications. As both a theoretical mathematician and computational scientist, Bergmann has made significant contributions to advancing the state-of-the-art in optimization on manifolds, particularly in developing methods that maintain intrinsic geometric properties while achieving computational efficiency. His work has applications across multiple domains including image processing, computer graphics, robotics, and data science.

Douglas Cochranمشاهده پروفایل

عضو هیئت علمی

• Remote Sensing
• Signal Processing
• Mathematics
• +۴ مورد دیگر

Douglas Cochran is a Research Professor in the School of Mathematical and Statistical Sciences and an Emeritus Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He has held roles such as Program Manager for Mathematics at the U.S. Air Force and DARPA, and served as a Visiting Faculty Fellow at the ASU Barrett Honors College. His research focuses on mathematical aspects of remote sensing, particularly for national security applications, with expertise in signal processing, radar systems, and information theory. Education: Ph.D. Applied Mathematics (Harvard, 1990), S.M. Applied Mathematics (Harvard, 1986), M.A. Mathematics (UC San Diego, 1980), S.B. Mathematics (MIT, 1979). Key Roles: Program Manager (U.S. Air Force, DARPA), Consultant (Australian Defense Science), Editor (IEEE Transactions on Signal Processing, Sampling Theory in Signal and Image Processing). His research interests include radar signal processing, multi-channel detection, and information-geometric foundations of sensing systems. Recent work addresses topics like cyclostationary signal analysis, radar clutter cancellation, and eigenvalue distributions in statistical signal processing. Notable awards include the IEEE Fellowship (2015), U.S. Department of Defense Medal for Exceptional Public Service (2005), and multiple teaching excellence recognitions. He has led over 20 grants, including NSF-funded projects on mathematical methods in medical imaging and MURI initiatives on information theory for adaptive systems. Cochran has advised numerous students and contributed to industry through consulting roles at companies like Motorola and Raytheon. His academic service includes editorial roles and organizing IEEE conferences, reflecting his broad impact in academia and defense applications.

Nicole Lazarمشاهده پروفایل

استاد

• Statistical Inference
• Functional Neuroimaging Data Analysis
• Model Selection
• +۹ مورد دیگر

Nicole Lazar is the Department Head and Professor of Statistics at Pennsylvania State University. She holds a PhD from the University of Chicago (1996), an MS from Stanford University (1993), and a BA in Psychology and Statistics from Tel Aviv University (1988). Her research focuses on foundational statistical inference, neuroimaging data analysis, and topological data science, with applications in cognitive neuroscience, climatology, and psychology. She pioneered statistical methods for fMRI analysis and has developed interdisciplinary approaches at the intersection of statistics, mathematics, and computer science. Her professional service includes roles as Editor of *The American Statistician*, Associate Editor for multiple journals, and co-Editor of a Special Issue on 'Statistical Inference in the 21st Century.' Lazar authored the textbook *The Statistical Analysis of Functional MRI Data* (Springer) and has published extensively in top journals. She is a Fellow of the American Statistical Association (2014), Fellow of the Institute of Mathematical Statistics (2021), and Elected Member of the International Statistical Institute (2006). Previously, she served on faculty at Carnegie Mellon University and the University of Georgia, where she also served as Interim Department Head (2014–2016). Lazar’s teaching includes advanced courses like STAT 470W (Statistical Consulting), STAT 512 (Applied Nonparametric Statistics), and STAT 580/581 (Advanced Topics in Statistics). Her work emphasizes methodological innovations, reproducibility in science, and promoting gender equity in statistics through her presidency of the Caucus for Women in Statistics (2019).

• Statistical Shape Analysis
• Medical Imaging and Brain Structure Analysis
• Machine Learning
• +۴ مورد دیگر

Anuj Srivastava is a Professor in the Department of Statistics at Florida State University. His research focuses on statistical shape analysis, computational geometry, and their applications in medical imaging, computer vision, and machine learning. He develops novel methods for analyzing complex shapes, trajectories, and functional data, with particular emphasis on elastic geometry and manifold-based techniques. Key research themes include shape analysis of 3D objects, brain subcortical structures, and tree-like anatomical networks. His work integrates statistical theory with computational tools to address challenges in biomedical imaging, motion tracking, and data-driven modeling. Notable contributions include frameworks for longitudinal elastic shape analysis (LESA), graph-based mobility modeling, and Bayesian emulation of human motion. Recent projects involve NSF-funded collaborations on stochastic shape processes, pandemic simulation (RAW-ALPS), and statistical analysis of chromosome conformations. His methods are applied to diverse domains such as neuroimaging, cell morphology analysis, and electrical load profiling. The research emphasizes interdisciplinary applications with clinical, environmental, and engineering relevance.

Pierre-Yves Gousenbourgerمشاهده پروفایل

مدرس

• Interpolation on Manifolds
• Optimization on Manifolds
• Bézier Curves and Surfaces
• +۵ مورد دیگر

Pierre-Yves Gousenbourger is a Teaching Assistant at the École polytechnique de Louvain (Louvain School of Engineering), part of the Catholic University of Louvain. He is affiliated with the ICTEAM research institute and supervises students in the Department of Electrical Engineering. His pedagogical approach includes the Apprentissage Par Projet (project-based learning) methodology. He holds a PhD (2020) and a Master's in Applied Mathematics from UCLouvain, both supervised by Pierre-Antoine Absil. His doctoral thesis, Interpolation and fitting on Riemannian manifolds , developed efficient algorithms for manifold-valued data fitting with applications in UAV control, medical imaging, and model reduction. His research focuses on interpolation, optimization, and imaging on Riemannian manifolds , emphasizing Bézier curves/surfaces for data approximation. Key areas include wind field estimation via covariance matrix fitting on SPSD manifolds, medical shape reconstruction (e.g., endometrial surfaces), and parametric model order reduction using manifold interpolation. His work integrates Riemannian geometry with computational efficiency, leveraging tools like exponential/logarithm maps. His publications consistently address data fitting on nonlinear manifolds , featuring Bézier-based methods, variational formulations, and applications spanning computer vision, medical imaging, and aerodynamics. Trends include denoising corrupted data, accelerating computational workflows, and generalizing Euclidean splines to manifolds. He collaborates with the RANSO and Dysco research groups and has worked with institutions including MIT, TU Chemnitz, and the University of Münster. He develops open-source numerical tools for manifold interpolation, distributed under the GNU GPL license.

Sebastian Weichwaldمشاهده پروفایل

دانشیار

• Causal Modelling
• Machine Learning
• Structural Equation Models
• +۳ مورد دیگر

Sebastian Weichwald is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen. He leads the Copenhagen Causality Lab (CoCaLa) and co-leads the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI. His academic journey includes a PhD at ETH Zurich (2019) and postdoctoral work at the University of Copenhagen. Research Interests: Causal Modelling, Causal Discovery, Structural Equation Models, Time Series Analysis, Neuroimaging, Biomedical Data Analysis. Awards: Best student paper at CIP 2014, Winner of Causality 4 Climate NeurIPS 2019 competition. Collaborations: Active in interdisciplinary projects with applications in cardiology, neuroscience, and biomedical imaging. Software Tools: Developed open-source libraries including CausalDisco , coroICA , tidybench , and Pymanopt for causal discovery, signal processing, and manifold optimization.

Palina Salanevichمشاهده پروفایل

استادیار

• Signal Processing
• Data Analysis
• Graphs
• +۹ مورد دیگر

Dr. Palina Salanevich is an Assistant Professor at Utrecht University's Mathematical Institute, specializing in Mathematical Modeling. Her work bridges harmonic and time-frequency analysis, geometric functional analysis, signal and image processing, and high dimensional probability theory. Her research focuses on: Geometric properties of random frames and matrices, particularly Gabor frames Phase retrieval problems and their applications in ptychography and speech recognition Machine learning with randomization, especially Random Vector Functional Link networks Data processing using non-negative matrix factorization for audio and EEG signals Harmonic and time-frequency analysis on graphs Dr. Salanevich's recent publications show a strong focus on phase retrieval with Gabor frames, random matrix theory applications in signal processing, and randomized approaches to machine learning. Her work consistently bridges theoretical mathematics with practical signal processing applications, particularly in developing stable and efficient algorithms for complex inverse problems. Her notable scientific achievements include: NWO Veni grant (2021) for "Time-frequency structured measurements in phase retrieval: stability and reconstruction" Board member of the NDNS+ research cluster (2024) Dr. Salanevich actively supervises students, including PhD student Olov Schavemaker (since December 2021), and offers various projects for Bachelor and Master students in areas related to mathematical modeling and signal processing. She has been involved in several collaborative research projects with institutions like CWI and researchers including Deanna Needell, Goetz E. Pfander, and others. She is organizing the "Phase retrieval in mathematics and applications" workshop at the Lorentz Center in August 2024, demonstrating her leadership in her research community.

• AI Safety
• Generative Modeling
• Reinforcement Learning
• +۳ مورد دیگر

Sangwoong Yoon is a Research Fellow in the Department of Electronic and Electrical Engineering at University College London (UCL), working under Professor Ilija Bogunovic on Reliable AI Alignment. His role focuses on developing algorithmic foundations for AI systems that operate safely and risk-awarely in real-world environments, contributing to UCL's leadership in trustworthy artificial intelligence research. Dr. Yoon's research centers on the intersection of generative modeling and reinforcement learning, with core objectives including building safe AI systems through energy-based models, diffusion processes, and inverse reinforcement learning. His work spans theoretical advances in anomaly detection, kernel density estimation, and sequential decision-making, while maintaining strong application relevance in robotics, biomedical systems, and fluid dynamics. This dual focus on theoretical rigor and practical impact defines his approach to reliable AI development. Analysis of his publication trajectory reveals consistent innovation in generative AI safety mechanisms, with increasing emphasis on medical and industrial applications since 2022. His recent work demonstrates sophisticated integration of reinforcement learning with diffusion models for robust decision-making, while maintaining cross-disciplinary relevance through collaborations in liver fibrosis research and microfluidics. The high citation counts (e.g., 83 for Deep-Hurricane-Tracker) reflect significant community impact. As an active member of Professor Bogunovic's research group, Dr. Yoon contributes to UCL's AI ecosystem through NeurIPS 2025 Area Chair duties and collaborative projects advancing reliable AI alignment. His work environment fosters interdisciplinary innovation at the hardware-software-safety interface within UCL's Electronic and Electrical Engineering department.

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

Jindong Wangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Robust Machine Learning
• +۱۱ مورد دیگر

Jindong Wang is a Tenure-Track Assistant Professor in the Department of Data Science at William & Mary. He is also a faculty member of the AI Safety Community at the Future of Life Institute. Previously, he was a Senior Researcher at Microsoft Research Asia from 2019 to 2024. His work spans robust, trustworthy, and responsible AI, with a focus on foundation models, transfer learning, and AI for social sciences. PhD, University of Chinese Academy of Sciences (2019) Bachelor’s, North China University of Technology (2014) Dr. Wang’s research centers on enhancing the reliability and societal impact of AI systems. Key areas include robust machine learning, domain generalization, federated learning, semi-supervised learning, and large language model evaluation and enhancement. He is particularly interested in the philosophical and behavioral aspects of LLMs and how AI can be leveraged in interdisciplinary domains such as social sciences. His work aims to build AI systems that are not only high-performing but also safe, interpretable, and aligned with human values. His recent publications (2021–2025) in top venues like TPAMI, NeurIPS, ICML, ICLR, and ACL reflect a strong trend toward understanding and improving foundation models—especially in noisy or out-of-distribution settings—and developing frameworks for dynamic evaluation, agent behavior, and trustworthy AI. These works often combine theoretical rigor with practical open-source implementations, demonstrating a commitment to reproducibility and community impact. World’s Top 2% Highly Cited Scientists (Stanford, since 2022) Most Influential AI Scholar (AMiner, since 2022) Best Paper Award, AAAI 2025 Good Data Workshop William & Mary Faculty Research Award (2025) Top 17 Most Cited NeurIPS Papers (2021) PaperDigest Most Influential CIKM Paper (2021) Most Cited Paper in ICDM 2017 and 2nd Most Cited in MM 2018 Dr. Wang actively mentors PhD and master’s students and welcomes internship applications for future cohorts. He has secured significant research visibility and impact, with over 20,000 citations (H-index 50) and leadership roles as associate editor of IEEE TNNLS, guest editor for ACM TIST, and area chair for ICML, NeurIPS, ICLR, KDD, and ACL. He has delivered tutorials at major conferences including IJCAI, WSDM, KDD, AAAI, and CVPR. His research is supported by collaborations with leading institutions and industry partners, and has been featured in Forbes and MIT Technology Review. He leads several influential open-source projects, including transferlearning , PromptBench , torchSSL , and USB , which together have received over 20,000 GitHub stars. These tools support research in transfer learning, prompt engineering, semi-supervised learning, and unified self-supervised benchmarks. He is also organizing key workshops such as FedGenAI-IJCAI’25 and the ICCV 2025 workshop on Trustworthy Study Transfers, fostering community engagement in emerging AI challenges.

• Shape Optimization
• Control Theory
• Partial Differential Equations
• +۶ مورد دیگر

Yannick Privat is a Professor at the University of Lorraine , affiliated with the Nancy School of Mines and the Élie Cartan Institute of Lorraine (IECL). He leads the Shape Optimization and Control of PDEs research group and is a member of INRIA's Sphinx team . His academic career spans institutions including University of Strasbourg and CNRS . Prior to his current role, Privat earned his Doctorate in Mathematics (2008) at the Élie Cartan Institute of Lorraine , supervised by Prof. Antoine Henrot , and completed a post-doctorate at the Orléans Mathematics Laboratory and the Jean Kuntzmann Laboratory (2008-2009). His research focuses on shape and topology optimization , control of partial differential equations , and applications to biology , with secondary interests in fluid mechanics , ferromagnetism , and quantum computing . His work bridges theoretical analysis with applied projects like mosquito release strategies for disease control and organ registration in augmented surgery . Recent publications highlight interdisciplinary trends in quantum control (2025), acoustic room reconstruction (2025), and ferromagnetic modeling (2024). These works often integrate numerical methods , geometric constraints , and biological systems . Privat has received prestigious recognition, including the Blaise Pascal Prize (2023) and Junior Membership in the University Institute of France (2021-2026). He serves on editorial boards for journals like Computational and Applied Mathematics and Evolution Equations and Control Theory . As an advisor, he has mentored 21 doctoral and postdoctoral researchers , including Ludovic Godard-Cadillac and Guillaume Ferrière , across topics ranging from epidemic control to stellarator design . His methodological contributions include INRIA's Sphinx project and the ANR STOIQUES consortium for shape optimization challenges.