جست‌وجوی استاد و پژوهشگر

Simone Di Marinoمشاهده پروفایل

دانشیار

• Optimal Transport
• Mathematical Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Simone Di Marino is an Associate Professor in the Department of Mathematics at the University of Genoa. His research focuses on Optimal Transport, Mathematical Analysis, and their applications to Partial Differential Equations and Functional Analysis. He is a member of the research commission and teaches courses such as Mathematical Analysis, Calculus, and specialized topics in Mathematics and Engineering programs. His research interests include Optimal Transport Theory, Nonlinear Analysis, and the development of variational methods for problems in mathematical physics and differential geometry. He explores topics such as gradient flows, entropy regularization, and geometric inequalities on manifolds. Recent work highlights contributions to the theory of Grand-Canonical Optimal Transport, nonlinear mobilities in transport models, and the convexity properties of ground state energies in quantum systems. His articles often bridge pure analysis with applications in physics and numerical methods. Di Marino is available for student consultations on Wednesdays from 2pm to 4pm. Though no specific grants or awards are listed, his extensive publication record reflects active engagement in collaborative research.

Yaakov Malinovskyمشاهده پروفایل

استاد

• Decision theory
• Stochastic ordering
• Sequential selection
• +۶ مورد دیگر

Yaakov Malinovsky is a Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on decision theory, stochastic ordering, sequential selection methods, group testing, and nonparametric methods. He has held editorial roles at journals including Enumerative Combinatorics and Applications , Methodology and Computing in Applied Probability , and The American Statistician . His work bridges theoretical probability and applied statistics, with contributions to group testing optimization, sequential analysis, and combinatorial probability. Malinovsky earned his Ph.D. in Statistics from The Hebrew University of Jerusalem in 2009. He has secured research funding from the United States-Israel Binational Science Foundation for projects on minimax online learning policies in stochastic sequential selection. His teaching portfolio includes courses such as Probability Theory, Mathematical Statistics, and specialized topics in stochastic processes and nonparametric methods. His recent research explores round-robin tournament models, prime number distribution via dice rolls, and optimal stopping rules. He has published extensively in top-tier journals like Statistica Sinica , Biometrics , and Sequential Analysis , demonstrating expertise in statistical inference and combinatorial problems. His work often addresses practical applications in epidemiology and algorithm design while advancing foundational probability theory.

Oliver DeWolfeمشاهده پروفایل

دانشیار

• String Theory
• Supergravity
• Holography
• +۳ مورد دیگر

Oliver DeWolfe is an Associate Professor of Theoretical High Energy Physics at the University of Colorado at Boulder. He specializes in string theory, supergravity, and their applications to particle physics, cosmology, and quantum field theory through holography. His research explores quantum gravity, unification of fundamental forces, and dual descriptions of strongly coupled systems. Education: PhD in Physics from MIT (2000, advisor: Barton Zwiebach), BA in Physics & Astronomy from Wesleyan University (1995). Postdoctoral positions included Princeton University (2003-2005) and Kavli Institute for Theoretical Physics, UC Santa Barbara (2000-2003). Teaching: Awarded 2011-12 Boulder Faculty Teaching Excellence Award. Taught courses in General Relativity, Quantum Field Theory, Gravitational Theory, and reformed quantum mechanics curricula using concept tests and tutorials (2008-2009). Research Contributions: Focuses on string theory applications to cosmology, supersymmetry breaking, and strongly correlated systems. Key publications include studies on charged black holes, gauge/gravity duality, moduli stabilization, and holographic models of QCD.

Peter Filzmoserمشاهده پروفایل

استاد

• Compositional Data Analysis
• Robust Statistics
• Outlier Detection
• +۵ مورد دیگر

Peter Filzmoser is a Professor at the Institute for Statistics and Mathematical Economics (E105) of Vienna University of Technology, leading the Computational Statistics Research Area (E105-06) and affiliated with the Network Lab. His research focuses on: Compositional Data Analysis Robust Statistics Outlier Detection Machine Learning for High-Dimensional Data with applications in geochemistry, mobility data, tribology, and sustainable development. Recent publications (2023) demonstrate significant advances in explainable outlier detection using Shapley values, robust techniques for compositional data analysis, and applications in forecasting heterogeneous time series. His work extends compositional data analysis through graph signal processing and develops novel robust methodologies for real-world problems. Professor Filzmoser has advised over 15 Master's and PhD students from 2021-2023. Key research projects he leads include: Automotive Intelligence for/at Connected Shared Mobility CSTAT: Blind Source Separation Generalized relative data and Robustness in Bayes spaces

Christophe Giraudمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• High-Dimensional Statistics
• +۳ مورد دیگر

Christophe Giraud is a Professor of Mathematics at Université Paris-Saclay (Orsay) since 2012, with an adjunct position at Cornell University since 2016. He holds a PhD in Mathematics from Université Paris VI (2001) and a Habilitation from Université Paris XI (2011). His research focuses on high-dimensional statistics, machine learning, and their applications in biology and ecology. He leads the Master's program in Mathematics and Applications at Paris-Saclay and is Deputy Director of the Graduate School of Mathematics there. Giraud has authored Introduction to High-Dimensional Statistics (2015, 2021) and serves as an Action Editor for JMLR and JEMS . His work bridges theoretical statistics and practical algorithms, addressing challenges like clustering, bandit algorithms, and fair online learning. He has supervised numerous PhD students and contributes to software such as GGMselect. His publications span journals like Annals of Statistics , NeurIPS , and Journal of Machine Learning Research .

Philipp Rennerمشاهده پروفایل

مدرس ارشد

• Computational Economics
• Game Theory
• Dynamic Incentive Problems
• +۳ مورد دیگر

Dr. Philipp Renner is a Senior Lecturer in the Department of Economics at Lancaster University Management School (LUMS). His research focuses on computational economics and game theory, particularly addressing dynamic incentive problems in taxation, managerial remuneration, insurance, and policy design. These problems involve complex mathematical challenges such as bilevel optimization and dynamic programming. He is actively involved in interdisciplinary research, emphasizing computational challenges in solving high-dimensional dynamic programs and dynamic game equilibria. His work bridges theoretical economics with practical algorithmic solutions. Renner participates in notable research groups including the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy, and the Economics Research Group. Key research themes include monetary policy analysis, optimal fiscal frameworks, and the application of machine learning to incentive problems. His recent contributions include studies on persistent supply shocks in monetary policy and polynomial optimization approaches to principal-agent models. Renner’s academic activities span conference presentations on topics like dynamic incentive mechanisms and computational economics, reflecting his commitment to advancing quantitative methods in economic analysis.

Mattia Zorziمشاهده پروفایل

دانشیار

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +۸ مورد دیگر

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

Miles Lopesمشاهده پروفایل

دانشیار

• Bootstrap Methods
• High-Dimensional Inference
• Error Estimation
• +۳ مورد دیگر

Miles Lopes is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on developing and analyzing bootstrap methods for high-dimensional statistical problems, with particular attention to error estimation in randomized algorithms and high-dimensional inference. He holds a Ph.D. from UC Berkeley. Research interests include bootstrap approximation techniques, high-dimensional covariance estimation, spectral statistics, and applications of randomized algorithms in numerical linear algebra. His work bridges theoretical statistics and computational methods, addressing challenges in modern data analysis. Recent publications emphasize bootstrap methods for eigenvalue analysis in high-dimensional PCA, operator norm approximation, and robust statistical inference in complex models. His methodologies have applications in functional data analysis, multivariate testing, and software development for randomized algorithms.

• Statistical Signal and Image Analysis
• Biomedical Applications
• Environmental Statistics
• +۳ مورد دیگر

Armin Schwartzman is a Professor at the Halıcıoğlu Data Science Institute and Department of Biostatistics at the University of California, San Diego (UCSD). He holds a PhD in Statistics from Stanford University, an MSc in Electrical Engineering from the California Institute of Technology, and a BSc in Electrical Engineering from Technion-Israel Institute of Technology. His research focuses on statistical methods for signal and image analysis with applications in biomedical and environmental domains, including spatial inference, high-dimensional data analysis, and random field theory. Education : PhD in Statistics, Stanford University, 2006 MSc in Electrical Engineering, California Institute of Technology, 1996 BSc in Electrical Engineering, Technion-Israel Institute of Technology, 1995 Research Interests : Dr. Schwartzman develops statistical methodologies for analyzing complex data, particularly in neuroimaging, climate science, and genomics. Key areas include spatial inference for image analysis, multiple testing in high-dimensional data, and applications of random field theory. His work bridges theoretical statistics and practical challenges in biomedical and environmental research. Grants & Funding : NIH R01EB026859 (2019–2023): Spatial inference methods for image analysis NIH R21EB013795 (2012–2015): Voxelwise analysis of imaging response in neuro-oncology NIH R01CA157528 (2012–2019): Multiple testing methods for random fields Advising : Current advisees include Sam Davenport, Anubhav Singh-Sachan, and others. Former students have transitioned to roles at institutions like Moderna, University of Florida, and Cruiser. Labs & Collaborations : His research group works on projects such as: Random field theory for noise modeling in images Spatial inference in neuroimaging and climate data Machine learning for medical imaging (e.g., glaucoma diagnosis) Mountain glacier monitoring via satellite imagery

Angxiu Niمشاهده پروفایل

استادیار

• Probability
• Dynamical Systems
• Chaos
• +۵ مورد دیگر

Angxiu Ni is an Assistant Professor in the Department of Mathematics at the University of California, Irvine. Their research focuses on Probability and Applied Dynamical Systems, with significant contributions to linear response theory for chaotic and high-dimensional systems. They developed computational tools such as the Fast Response Formula, Path-Kernel method, and Ergodic/Foliated kernel differentiation. PhD from UC Berkeley Mathematics Postdoctoral work at Peking University BICMR Former Assistant Professor at YMSC, Tsinghua University Research areas include: Linear response theory for hyperbolic systems Non-intrusive shadowing algorithms Adjoint theories and gradient explosion analysis Applications to fluids, geophysics, and machine learning They are mentored by prominent mathematicians including John Strain and Qing Nie, and have collaborated with Stefano Galatolo on optimal response problems.

Johan Sokrates Windمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Neural Networks
• Overparameterized Machine Learning
• +۳ مورد دیگر

Johan Sokrates Wind is a Research Fellow at the University of Oslo's Department of Mathematics, specializing in Differential Equations and Computational Mathematics. His primary affiliation is with the Faculty of Mathematics and Natural Sciences. He holds a Master's in Industrial Mathematics from the Norwegian University of Science and Technology (NTNU) and began his PhD in August 2021. Wind's research focuses on deep learning, neural networks, and overparameterized machine learning systems. He has explored topics such as the Neural Tangent Kernel, Deep Linear Networks, and implicit biases in optimization algorithms. His work bridges theoretical analysis with practical implementations, as evidenced by his blog The Good Minima , where he publishes technical insights on neural network behavior and training dynamics. Notably, he has contributed to projects like real-time visual odometry on smartphones during his part-time role at Arm Ltd. Wind is an active participant in competitive programming and Kaggle competitions, showcasing his problem-solving skills and algorithmic expertise. His research emphasizes analytically tractable models and the mathematical foundations of modern AI systems. Recent investigations include the RWKV language model architecture, efficient CIFAR-10 classification, and the role of initialization and learning rates in SGD's implicit bias. Wind’s publications highlight interdisciplinary approaches, combining elements of optimization theory, computational mathematics, and applied machine learning. He maintains an active blog with detailed technical posts, demonstrating a commitment to open science and knowledge-sharing. His academic journey reflects a balance between theoretical rigor and practical innovation, positioning him as a rising researcher in computational and mathematical aspects of deep learning.

Magdalena Larforsمشاهده پروفایل

استاد

• String Theory
• Calabi-Yau Manifolds
• Quantum Gravity
• +۲ مورد دیگر

Magdalena Larfors is Professor of Theoretical Physics at Uppsala University, where she leads a research group studying string theory and M-theory compactifications. Her work focuses on how geometry and topology of extra dimensions determine observable physics, using methods from algebraic and complex geometry complemented by machine learning approaches. Research areas include: Mathematical aspects of string theory compactifications Geometry of Calabi-Yau manifolds Physics-inspired machine learning applications Classification of stable string vacua She is principal investigator for multiple funded projects including 'Exploring the Structure of Stable String Vacua' and 'Realistic Ground States of String Theory'. Her recent publications demonstrate innovative applications of computational methods to problems in high-dimensional geometry.

• Bayesian Statistics
• Uncertainty Quantification
• Statistical Theory
• +۳ مورد دیگر

Eduard Belitser is an Associate Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on Bayesian statistics, uncertainty quantification, and statistical theory, contributing to the UN Sustainable Development Goals. He currently teaches courses such as Experimental Design and Data Analysis , Statistical Models , and Statistics for AI . Belitser is actively involved in academic collaborations and has presented his work at international conferences and institutions. Belitser’s research interests include Bayesian methods, robust estimation, and applications to change point analysis, inverse problems, and variable selection. He explores topics like empirical Bayes procedures, optimality in estimation, and uncertainty quantification, with a focus on mathematical foundations and practical implications in statistical modeling and data analysis. Belitser's recent publications (2022–2025) emphasize Bayesian uncertainty quantification in change point detection, inverse problems, and robust statistical estimation. His work highlights methodological advancements, such as empirical Bayes approaches for sparse quantiles and optimality principles in variable selection and multiple testing frameworks, reflecting a blend of theoretical rigor and applied data analysis. No scientific awards or honors are explicitly mentioned in the provided information. In advising, he has supervised 3 PhD theses, though student names are not listed. His professional activities include editorial roles ( Statistica Neerlandica ), visiting research at North Carolina State University (March–April 2024), and organizing the 34th European Meeting of Statisticians (July 2023). He has also delivered invited lectures on Bayesian UQ and change-point models in 2023 and 2024.

• Personality Psychology
• Developmental Psychology
• Psychopathology
• +۴ مورد دیگر

Professor Marleen De Bolle is a prominent faculty member at Ghent University (Universiteit Gent) specializing in personality psychology with a particular focus on child and adolescent development. Her extensive research portfolio demonstrates deep engagement with the Five-Factor Model of personality and its applications across developmental stages and clinical contexts. Dr. De Bolle's research interests center on the intersection of personality development and psychopathology in children and adolescents. She has made significant contributions to understanding how personality traits relate to various psychological outcomes, including depression, anxiety, and behavioral problems. Her work often employs cross-cultural methodologies to examine the universality of personality structures and their developmental trajectories from childhood through adolescence. She has been particularly active in investigating the application of dimensional personality models to youth psychopathology, including their relevance to DSM-5 classification systems. Analysis of her publication record reveals consistent focus on personality structure across development, with particular attention to the Five-Factor Model's applicability to children and adolescents. Her work demonstrates sophisticated methodological approaches including latent class analysis, hierarchical modeling, and cross-cultural comparisons. The majority of her research examines the relationship between normal-range personality traits and maladaptive outcomes, with increasing attention to dimensional models of personality pathology as reflected in DSM-5. Professor De Bolle has maintained an exceptionally productive research career through extensive collaboration, primarily with colleagues at Ghent University including Filip De Fruyt and Barbara De Clercq. Her work spans numerous high-impact journals in personality, developmental, and clinical psychology, demonstrating both theoretical sophistication and clinical relevance. She has contributed significantly to understanding how personality traits relate to various psychological outcomes across development, with implications for both theoretical models and clinical practice.

Luca Magriمشاهده پروفایل

استاد

• Scientific Machine Learning
• Data Assimilation
• Fluid Mechanics
• +۸ مورد دیگر

Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.