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FRANCA STABLUMView profile

Professor

• Cognitive Psychology
• Neuropsychology
• Cognitive Rehabilitation
• +4 more

Prof. Franca Stablum is a Full Professor at the University of Padua. Her research focuses on cognitive psychology, neuropsychology, and rehabilitation, particularly in attention mechanisms, executive functions, and time perception across populations including older adults and neurological patients. She has pioneered computerized cognitive training tools and neurorehabilitation methodologies. Her work integrates experimental psychology, neuroimaging, and computational modeling. Key areas include: 1) Temporal processing disorders in neurological conditions (e.g., Parkinson's, MS); 2) Prospective memory deficits in brain injury patients; 3) Developmental time perception studies in children; 4) Digital interventions for cognitive aging. She has developed assessment tools like the 'Virtual Kitchen' for executive function evaluation and the SWIFT collaborative web-based training platform. Over 200+ publications span cognitive neuroscience, clinical neuropsychology, and rehabilitation engineering. Her research emphasizes translational approaches, bridging basic science with clinical applications. Active in international collaborations, she has contributed to standardization efforts in cognitive screening (e.g., Italian norms for Addenbrooke's Cognitive Examination).

Katharina ProkschView profile

Researcher

• Statistics
• Biomedical Imaging
• Data Analysis
• +4 more

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

Silvia LiveraniView profile

Associate Professor

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +3 more

Silvia Liverani is a Reader in Statistics and Head of the Centre in Data Science, Statistics and Probability at Queen Mary University of London (School of Mathematical Sciences). Her research focuses on Bayesian methods, spatial-temporal modeling, and computational statistics, with applications in epidemiology, environmental health, and genetics. She leads projects on biodiversity analysis and spatial health data, collaborating with institutions like the Royal Botanical Gardens, Kew and the Alan Turing Institute. Education: PhD in Bayesian Clustering from the University of Warwick (2009). Grants & Initiatives: Awarded £1,000 (2025) for an LGBTQ+ Mathematics Open Day; led a Data Study Group at the Alan Turing Institute (2023). Labs/Teams: Director of the Centre in Data Science, developer of R packages PReMiuM and CARME for Bayesian clustering and spatial modeling. Key Research Themes: Dirichlet process mixture models, spatial misalignment correction, and interdisciplinary collaborations in health and environment. Current work includes predicting biodiversity loss under climate change and analyzing socio-economic health disparities through spatio-temporal frameworks.

Bodhisattva SenView profile

Professor

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +4 more

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor (2008) M.Stat., Indian Statistical Institute, Kolkata B.Stat., Indian Statistical Institute, Kolkata Research Interests: Nonparametric function estimation Optimal transport applications in statistics Empirical Bayes and multiple testing High-dimensional statistical inference Statistical methods in astronomy Key Contributions: Developed multivariate distribution-free tests using optimal transport Advanced convex regression methods in multidimensions Contributed to nonparametric maximum likelihood estimation in mixture models Explored statistical applications in stellar abundance clustering His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.

Jakob ZechView profile

Professor

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +4 more

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

• Computer Architecture
• Machine Learning and Artificial Intelligence
• Embedded Systems
• +4 more

Daniel Bates is a Senior Research Associate at the University of Cambridge's Department of Computer Science and Technology. His research focuses on Computer Architecture and Machine Learning, emphasizing co-optimization of hardware and software. Key projects include the Loki manycore processor (with 128 cores fabricated in 2018) and the Muntjac RISC-V multicore processor, designed for simplicity and extensibility. Bates explores energy-efficient machine learning optimizations like dynamic gating and network architecture search. He teaches courses including Part IA Algorithms, Part IB Computer Architecture, and Part II Advanced Computer Architecture. His supervision style prioritizes student feedback and clarity, offering tailored support and prompt communication. Bates has contributed to over a dozen peer-reviewed publications since 2013, addressing topics from neural network security to embedded system optimization. His work bridges theoretical research and practical hardware implementation, with a focus on open-source contributions like the Muntjac processor. He actively collaborates with industry through the University's research initiatives, emphasizing real-world applications of computer architecture advancements.

Damek DavisView profile

Assistant Professor

• Optimization
• Machine Learning
• Signal Processing
• +4 more

Damek Davis is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with both the College of Engineering and the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of California, Los Angeles (2015). His research focuses on developing and analyzing optimization algorithms for large-scale, nonconvex, and nonsmooth problems arising in machine learning and signal processing. He emphasizes theoretical guarantees on algorithm performance and practical implementations that leverage modern computing architectures. His work spans stochastic optimization, subgradient methods, and convergence analysis, with applications to computational microscopy, phase retrieval, and decentralized systems. Recent contributions include advancements in adaptive stepsize methods, sharpness-aware optimization, and global optimality conditions for mixture models. Davis collaborates on theoretical foundations and algorithmic innovations in high-dimensional statistics, nonlinear optimization, and parallel computing. His scholarly output includes over 50 peer-reviewed articles, with recent highlights in top venues such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Transactions on Signal Processing. His research is supported by grants from NSF and AFOSR, focusing on topics like robust statistical estimation and algorithmic scalability.

• Causal Inference
• Statistical Methods
• Latent Variable Models
• +6 more

Robin Evans is a Professor of Statistical Science and Deputy Head of Department at the University of Oxford, affiliated with Jesus College as a Tutorial Fellow. His research focuses on causal inference, statistical models (including Bayesian networks, latent variable models, and marginal structural models), and their applications in epidemiology, medicine, and social sciences. He holds a PhD from the University of Washington (2011) and completed a postdoctoral fellowship at the University of Cambridge (2011–2013). His work emphasizes understanding multivariate statistical models, causal identifiability, and efficient model selection. Notable contributions include parameterizing causal models for simulation, developing methods for combining experimental and observational data, and advancing techniques for validating causal inferences. He advises graduate students in statistical theory and methodology. Key publications include works on robust ATE estimation, marginal causal flows, and parameterizing causal models. His research groups include Statistical Theory and Methodology and Computational Statistics and Machine Learning. He is also involved in interdisciplinary projects, such as analyzing Sustainable Development Goals (SDGs) for adolescents in Nigeria.

Riley CasperView profile

Assistant Professor

• Orthogonal Polynomials
• Integrable Systems
• Spectral Theory
• +4 more

W. Riley Casper is an Assistant Professor of Mathematics at California State University, Fullerton (CSUF). His research focuses on the interplay between noncommutative algebra, algebraic geometry, and integrable systems, with applications to special functions and computational fluid dynamics. He holds a PhD from the University of Washington and dual MS/BS degrees from North Dakota State University. Dr. Casper teaches a wide range of courses at CSUF, including Calculus, Differential Equations, Real/Complex Analysis, Abstract Algebra, and Topology. His work spans both pure and applied mathematics, with recent contributions to matrix-valued differential operators, bispectrality, and prolate spheroidal wave functions. He has collaborated with institutions like Louisiana State University and the University of California, Berkeley on projects blending algebraic methods with computational techniques. Selected publications include studies on fractional differential operators, prolate spheroidal systems, and ocean data assimilation. His research has appeared in journals like Communications in Mathematical Physics and the American Journal of Mathematics. Teaching innovations include advanced problem-solving methodologies and applications of algebraic geometry in undergraduate instruction. Professional service includes organizing conferences (GAP XVI, ICM Satellite) and delivering invited talks at institutions worldwide. His work bridges theoretical mathematics with computational challenges in fluid dynamics and quantum computing.

Jacob KauffmannView profile

Researcher

• Unsupervised Learning
• Explainable AI
• Clustering
• +3 more

Dr. Jacob Kauffmann is a Postdoctoral Researcher at the Technical University of Berlin, specializing in research areas such as Unsupervised Learning, Explainable AI, and Anomaly Detection. His work focuses on enhancing model interpretability in unsupervised techniques and uncovering biases like the Clever Hans effect in machine learning systems. Education: M.Sc. Computer Science, Technische Universität Berlin (2017) Research Interests: Jacob’s research bridges theoretical foundations and practical applications of explainable AI (XAI) in unsupervised learning. He investigates clustering explanations, anomaly detection mechanisms, and kernel-based methods to improve model transparency. His recent work explores dataset shifts via Wasserstein distances and neural network-driven cluster analysis. Publications: Dr. Kauffmann’s articles emphasize XAI advancements, such as explainable anomaly detection and model bias analysis. Key themes include algorithmic transparency, dataset transport phenomena, and deep learning applications in unsupervised tasks. Advising & Grants: No formal advisees listed. Research supported by grants related to XAI and unsupervised learning methodologies (specific grants not detailed).

Hannah MarienwaldView profile

Researcher

• Kernel Methods
• High-Dimensional Statistics
• Expectation Estimation
• +3 more

Hannah Marienwald is a doctoral researcher at the Technical University of Berlin (TU Berlin), affiliated with the Machine Learning group and BIFOLD – Berlin Institute for the Foundations of Learning and Data. She previously worked as a research associate at the Institute of Mathematics, Mathematical Statistics and Machine Learning at the University of Potsdam, collaborating with BZML and the Data Engineering Systems group at the Hasso-Plattner Institute. She holds a B.Sc. (2016) and M.Sc. (2019) in Computer Science from TU Berlin, with a focus on Machine Learning, statistics, probability theory, and econometrics. Her research interests include Kernel Methods, High-Dimensional Statistics, and Expectation Estimation, particularly in the context of Kernel Mean Embeddings. Her work on high-dimensional statistical methods and kernel techniques has led to contributions such as the publication at AISTATS 2021, advancing methodologies for multi-task learning and kernel-based estimation.

François OrieuxView profile

Assistant Professor

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +5 more

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.

• Structural Engineering
• Civil Engineering
• Structural Health Monitoring (SHM)
• +4 more

Prof. Carlo RAINIERI is an academic researcher affiliated with the Department of Bioscienze e Territorio at the University of Molise, Italy. His work focuses on structural health monitoring (SHM), vibration-based diagnostics, and the dynamic characterization of civil infrastructure. He has pioneered methodologies for environmental compensation in modal analysis, automation of operational modal analysis (OMA), and integration of remote sensing technologies for infrastructure assessment. RAINIERI's research spans bridge monitoring, historical structure preservation, and hospital facility resilience. Key contributions include development of kernel PCA-based environmental effect compensation, hybrid OMA algorithms, and SHM systems validated via shaking table tests. His recent work emphasizes practical clustering approaches for anomaly detection, smart healthcare facility design through vibration monitoring, and cyber-physical systems for infrastructure management. He has collaborated on projects involving UNESCO heritage sites like the Trisulti Charterhouse and the Santa Maria Maddalena church. His experimental studies include investigations on masonry structures' seismic vulnerability, cement paste behavior under storage conditions, and liquid storage tank dynamics. RAINIERI has also contributed to advancing digital twin technologies for historical buildings through BIM-FEM integration. Over 150 published articles demonstrate his expertise in modal parameter monitoring, damage detection algorithms, and probabilistic assessment techniques. His work bridges theoretical advancements with industrial applications, providing actionable insights for infrastructure maintenance and disaster resilience strategies.

• Calculus of Variations
• Partial Differential Equations
• Mathematical Analysis
• +3 more

Edoardo Mainini is an Associate Professor at the University of Genova, specializing in mathematical analysis and applied mathematics. His research focuses on calculus of variations, partial differential equations, elasticity theory, optimal transport, and nonlinear dynamics. He has contributed to studies on fractional equations, material science, and stochastic processes. His work often bridges pure and applied mathematics, addressing problems in mechanics, probability, and geometric analysis. Mainini has collaborated extensively with researchers such as M. Kružík, D. Percivale, and U. Stefanelli, producing influential papers in journals like Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal. . His recent articles (2020–2025) address topics ranging from fractional linear equations to Bayesian nonparametric models. Education includes a PhD in Mathematical Analysis from the University of Genova (2010) and a thesis on Infinite-dimensional porous media equations and optimal transportation . He has organized international conferences and seminars on variational methods and geometric structures. His research emphasizes rigorous mathematical frameworks for physical phenomena, including the linearization of elasticity models and the study of ground states in diffusion-dominated systems. Mainini’s contributions to carbon nanotube geometries and optimal transport theory have been recognized through invited talks at major events, such as the International School of Mathematics “Guido Stampacchia” . His work often explores the interplay between discrete and continuous models, with applications to materials science and geometric optimization.

• Calculus of Variations
• Solid Mechanics
• Partial Differential Equations
• +3 more

Elisa Davoli is a Full Professor at TU Wien's Institute for Analysis and Scientific Computing, leading the Multiscale Calculus of Variations research group. Her work focuses on materials science, calculus of variations, and partial differential equations, with applications in solid mechanics, plasticity, and magnetoelasticity. She has been awarded the Richard von Mises Prize (2020) and the FWF START Prize (2020). Her research bridges theoretical analysis with practical applications, including homogenization theory, phase transitions, and optimal control in material systems. Key research interests include multiscale modeling, variational methods for material failure, and mathematical aspects of metamaterials. Recent projects address high-contrast materials, nonlocal denoising models, and magnetic skyrmions. Davoli has organized workshops on fracture mechanics, calculus of variations, and imaging challenges. She teaches courses in mathematical analysis, partial differential equations, and elasticity theory, and has mentored students in applied mathematics and mechanics. Grants: FWF START Project (Tunable materials), Elise Richter Grant (Plasticity and magnetoelasticity), GAČR Joint Project (Large Strain Challenges). Events: Organized Bio-PDE Days (2024), 3rd Austrian Calculus of Variations Day (2023), GAMM Microstructure Seminar (2023).