جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Image Retrieval
• Data Clustering
• +۳ مورد دیگر

Mohammed Lamine Kherfi is a researcher affiliated with Université de Ouargla, Algeria. His work focuses on machine learning, image retrieval, and data clustering with applications in computer vision and optimization. He has collaborated extensively with researchers like Oussama Aiadi, Mebarka Allaoui, and Djemel Ziou. His research bridges theoretical advancements in machine learning with practical applications in areas such as fruit classification, semantic image retrieval, and deep learning models. Key research areas include optimization algorithms (e.g., PSO integration with t-SNE), multi-view learning, and Bayesian methods for image representation. He has contributed to improving clustering techniques, feature extraction, and the development of lightweight neural network architectures. His work often emphasizes efficient and energy-aware solutions for real-world problems. Over 30 publications span prestigious venues like Expert Systems with Applications, IEEE Access, and Multimed Tools Appl. His collaborative network includes institutions in Algeria and international partners, reflecting a global impact in computational intelligence and computer vision.

Julija Zavadlavمشاهده پروفایل

استادیار

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +۳ مورد دیگر

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

• Uncertainty Quantification
• Data Assimilation
• Machine Learning
• +۴ مورد دیگر

Dr. Truong Vinh Hoang is a Researcher at the RWTH Aachen University , affiliated with the Chair of Mathematics for Uncertainty Quantification . His work focuses on integrating machine learning with data assimilation techniques for nonlinear dynamical systems . He has presented at multiple international conferences and seminars on these topics. Specializes in Bayesian methods and stochastic numerics Developed ML-EnCMF (Machine Learning-Ensemble Conditional Mean Filter) for non-linear data assimilation Applied techniques to Lorenz-63 and Lorenz-96 systems under chaotic regimes Contributed to localized neural network architectures for high-dimensional state tracking His research trends from 2020-2022 show increasing emphasis on deep learning-based filtering and Bayesian optimization for systems with non-Gaussian dynamics . Notably, he implemented variance reduction techniques to improve filter stability with small ensemble sizes. All publications demonstrate practical applications in computational science and stochastic modeling . Dr. Hoang is part of the MATH4UQ team at RWTH Aachen University, contributing to cutting-edge research in uncertainty quantification and nonlinear data assimilation .

Dirk Peschkaمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Fluid Dynamics
• Materials Science
• +۷ مورد دیگر

Dr. Dirk Peschka is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany, where he contributes to the Partial Differential Equations Research Group (FG1) . He is affiliated with the Berlin Mathematics Research Center MATH+ , the Society for Applied Mathematics and Mechanics (GAMM) , and the German Physical Society (DPG) . Research Interests Mathematical modeling of fluid dynamics and materials science using partial differential equations (PDEs). Applications in thin film dynamics, semiconductor devices, and reactive multiphase flows. Development of gradient flow frameworks and thermomechanical models via GENERIC formalism. Analysis of contact line behavior, dewetting processes, and fluid-structure interaction. Numerical methods for semiconductor simulations and geoscience applications. Publications Trends His recent work (2022–2025) emphasizes energy-based modeling of thin films, reactive flows, and semiconductor degradation. Key themes include contact line dynamics, gradient flows, and multiscale analysis of materials and fluid systems. Memberships Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin Mathematics Research Center MATH+ Society for Applied Mathematics and Mechanics (GAMM) German Physical Society (DPG)

• Reinforcement Learning
• Computational Decision-Making
• Machine Learning
• +۳ مورد دیگر

Carlo D'Eramo is a Professor in Reinforcement Learning and Computational Decision-Making at the Center for Artificial Intelligence and Data Science (CAIDAS) of Julius-Maximilians-Universität Würzburg (JMU). He leads the LiteRL research group and the hessian.AI independent research group, focusing on lightweight methods for adaptive autonomous agents in complex real-world environments. B.Sc., M.Sc. in Computer Engineering - Politecnico di Milano (2011, 2015) Double M.Sc. in Computer Science - University of Illinois at Chicago (2015) Ph.D. in Information Technology - Politecnico di Milano (2019) His research spans multiple RL domains including multi-task learning , curriculum learning , adversarial RL , options learning , and multi-agent coordination . Recent work explores physics-informed ML, neural network distillation, and uncertainty-driven exploration. 2025 publications highlight advancements in: Bellman update optimization via adaptive distillation uncertainty propagation in tree search multi-agent policy gradients real-world educational applications The group's work shows increasing specialization in physics-integrated RL and efficient neural architectures. Spotlight Presentation - ICML (2025) Spotlight Presentation - ICLR (2024) Oral Presentation - NeurIPS (2020) As Senior Area Chair for RLC and Area Chair for major AI conferences (AAAI, NeurIPS, ICLR), he contributes to academic governance. His team includes researchers like Ahmed Hendawy, Théo Vincent, and Georgia Chalvatzaki, with collaborations spanning TU Darmstadt and hessian.AI.

Reiner Rummelمشاهده پروفایل

استاد

• Physical Geodesy
• Satellite Gravimetry
• Gravitational Field Analysis
• +۳ مورد دیگر

Reiner Rummel is a Carl von Linde Senior Fellow at Technische Universität München (TUM), holding a professorial academic rank within the Department of Astronomical and Physical Geodesy. His career includes leadership roles such as former head of TUM’s Institute for Astronomical and Physical Geodesy, and professorships at Delft University of Technology. He has contributed significantly to satellite gravimetry, Earth’s gravitational field analysis, and geodetic observing systems. Education: Engineering diploma from TU München (1970), PhD from Technical University of Darmstadt (1974, dissertation on stochastic methods in geodesy). Postdoctoral research at Ohio State University (1974–76) and German Geodetic Research Institute (1976–78). Professional roles include membership in international academies (e.g., German Academy of Sciences Leopoldina, Royal Netherlands Academy) and leadership in ESA’s GOCE mission advisory group. Research focuses on physical geodesy, satellite-based gravitational field determination, and spectral methods. Notable achievements include advancing the GOCE mission’s scientific outcomes and developing methodologies for Earth’s gravity field analysis. Awards include the Bavarian Order of Merit (2008), Vening Meinesz Medal (1998), and honorary doctoral degrees from multiple universities. Publications span over four decades, emphasizing satellite gravimetry applications, dynamic ocean topography, and geodetic system unification. His work bridges geodesy with climate science, oceanography, and solid Earth dynamics. Current affiliations include TUM’s Institute for Advanced Study and leadership in global geodetic networks. Awards: Over 10 major honors, including medals from geophysical societies and academy memberships. Key Projects: GOCE mission science, Integrated Global Geodetic Observing System (IGGOS), and height system unification initiatives.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

François G Schmittمشاهده پروفایل

پژوهشگر

• Turbulence
• Complexity in Oceanography
• Stochastic Processes
• +۳ مورد دیگر

François G Schmitt is a Researcher at CNRS , focusing on turbulence and its applications in oceanography, astrophysics, and interdisciplinary science. His work spans: Turbulence modeling and multifractal analysis Physics-biology couplings in marine ecosystems Scaling processes in environmental systems Renewable energy applications Key research highlights include: 2024 study revealing Van Gogh's Starry Night contains scientifically accurate turbulence patterns 2022 papers on reactive scalars in turbulence and copepod behavior 2020 textbook Turbulence et Ecologie Marine His recent publications (2021-2024) demonstrate expertise in fluid dynamics, ecological modeling, and astrophysical turbulence. He advised PhD candidate Wenwei Wu (2021) and co-organized special issues on air-sea interactions. No scientific awards or part-time positions are documented.

Shinichi Nakajimaمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Generative Modeling
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.

Jörg Schallerمشاهده پروفایل

استاد مدعو

• Silicon Biogeochemistry
• Soil Science
• Phosphorus Cycling
• +۳ مورد دیگر

Dr. Jörg Schaller serves as Adjunct Professor and Head of the Working Group Silicon Biogeochemistry at the Leibniz Centre for Agricultural Landscape Research (ZALF) in Müncheberg, Germany. His research focuses on silicon's critical role in agricultural systems, particularly its effects on soil phosphorus availability, water retention capacity, and global carbon cycling within terrestrial ecosystems. His primary research domains include: Silicon biogeochemical cycling in agricultural landscapes Mechanisms of silicon-enhanced phosphorus availability Silicon-mediated soil water holding capacity improvements Silicon's contribution to carbon sequestration processes Development of silicon-based sustainable crop production systems Analysis of his 2023-2025 publications reveals a strong emphasis on silicon's practical applications for climate-resilient agriculture. Key findings demonstrate silicon amendments significantly improving barley yields in Kenyan acidic soils, reducing N2O emissions in arable systems, and enhancing drought resilience through improved soil-plant-water relations. His work integrates Arctic permafrost studies with tropical agricultural systems, establishing silicon as a cross-ecosystem regulator of nutrient cycling and greenhouse gas dynamics. Scientific Awards: No specific awards were documented in the source materials. Advising and Grants: Student mentorship details and grant funding information were not provided in available documentation. Dr. Schaller leads the Silicon Biogeochemistry working group within ZALF's Research Area 1 "Landscape Functioning," where his team investigates fundamental silicon processes and develops sustainable soil management strategies through field experiments, laboratory analyses, and modeling approaches across diverse global ecosystems.

• Machine Learning
• Neural Networks
• Numerical Analysis
• +۳ مورد دیگر

Shokhrukh Ibragimov is a Researcher at the Department of Mathematics and Computer Science, University of Münster. He is affiliated with the Applied Mathematics Münster: Institute for Analysis and Numerical Analysis. His research focuses on machine learning, particularly neural networks and optimization challenges in high-dimensional spaces. Collaborating with prominent figures like Arnulf Jentzen, he investigates topics such as the curse of dimensionality, gradient descent convergence, and the behavior of local minima in neural network training. His work bridges theoretical analysis and computational methods, contributing to advancements in deep learning and numerical analysis. Research interests include the mathematical foundations of neural networks, numerical approximations for high-dimensional problems, and the theoretical underpinnings of optimization algorithms in machine learning. His recent publications address critical gaps in understanding neural network limitations and the effectiveness of gradient-based methods.

• Differential Privacy
• Machine Learning
• Stochastic Optimization
• +۳ مورد دیگر

Abhradeep Thakurta is a researcher at Pennsylvania State University (College of Engineering, Computer Science and Engineering Department) and Microsoft Research Silicon Valley, focusing on Differential Privacy and Machine Learning . His work explores privacy-preserving techniques in optimization, model training, and data analysis. Key affiliations: Pennsylvania State University (College of Engineering), Microsoft Research Silicon Valley Academic rank: Researcher His research spans Differential Privacy in Stochastic Optimization , Convex Optimization , and Deep Learning . He investigates methods to enhance privacy guarantees while maintaining model accuracy and efficiency, particularly through matrix factorization, adaptive clipping, and noise correlation. Recent publications (2023–2025) highlight advancements in privacy amplification , checkpoint reuse , and secure model training . Collaborations include top researchers from institutions like Google, Stanford, and MIT.

Michael Röcknerمشاهده پروفایل

استاد

• Stochastic Analysis
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Prof. Dr. Dr. Sc. hc Michael Röckner is a distinguished Professor at the Faculty of Mathematics, Bielefeld University , with significant roles in interdisciplinary research initiatives. He serves as Deputy Speaker for the Collaborative Research Center (SFB) 1283, focusing on "Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications." Additionally, he leads subprojects A5 (Fokker-Planck-Kolmogorov equations) and B1 (stochastic partial differential equations) within SFB 1283. Affiliated with the Center for Cognitive Interaction Technology (CITEC) in the Stochastic Analysis Group. Member of the International Research Training Group 2235 and the Bielefeld Graduate School in Theoretical Sciences . Board member of the Specialized Language Center and module responsible for advanced courses in Stochastics and Optimization . His research spans Stochastic Analysis, Partial Differential Equations, and Applied Probability Theory , focusing on nonlinear Fokker-Planck equations, McKean-Vlasov SDEs, and gradient flows in Wasserstein spaces. His work bridges theoretical mathematics with applications in physics and finance. Scientific contributions include pioneering studies on nonlinear Fokker-Planck-Kolmogorov equations , stochastic porous media equations , and ergodicity of nonlinear processes . Awards include an honorary Doctor of Science (Dr. Sc. hc) .

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.

• Mathematical Statistics
• Stochastic Processes
• Machine Learning Theory
• +۴ مورد دیگر

Dr. Mahsa Taheri is a Scientific Associate (faculty-level researcher) in the Department of Mathematics at the University of Hamburg , embedded within the Faculty of Mathematics, Computer Science and Natural Sciences. She belongs to the research section Mathematical Statistics and Stochastic Processes and focuses on the emerging area Mathematics of data-based methods . Research Interests: Her work sits at the intersection of mathematical statistics, stochastic processes, and machine-learning theory . She develops non-asymptotic guarantees for modern learning algorithms, studies regularization and sparsity in deep neural networks, and quantifies statistical-computational trade-offs in high-dimensional regimes. Publication Trends: Across 13 peer-reviewed contributions (2016-2025) she consistently addresses the theoretical foundations of learning algorithms, with a marked emphasis on sample complexity, regularization, sparsity, and efficiency in neural networks and high-dimensional regression. The 2025 preprints extend this agenda to diffusion models and tail-index estimation. Scientific Awards: None explicitly reported in the available material. Advising & Grants: No advisee list or grant information was provided in the source text. Labs & Teams: She is part of the Mathematics of data-based methods research team within the Mathematical Statistics and Stochastic Processes unit; no dedicated laboratory name is mentioned.