جست‌وجوی استاد و پژوهشگر

Wesley Tanseyمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +۳ مورد دیگر

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

Saonli Basuمشاهده پروفایل

استاد

• Statistical Genomics
• Genetic Mapping
• Rare Variant Association
• +۵ مورد دیگر

Dr. Saonli Basu is a Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota School of Public Health. She serves as Founding Director of the Genomic Data Commons and Co-Director of the Analytics Core at the Masonic Institute for the Developing Brain. Education: PhD in Statistics (University of Washington), MStat (Indian Statistical Institute), BS in Statistics (Presidency College) Her research focuses on developing statistical methodologies for genetic mapping of complex traits, particularly rare variant association and gene-environment interaction modeling. She specializes in computational statistics, nonparametric inference, and statistical genetics applications for diseases like Alzheimer's, type 2 diabetes, and substance abuse. Recent publications show trends in SNP heritability analysis , admixed population genetics , longitudinal pregnancy studies , and multi-variant association tests . Key collaborative projects include cerebral small vessel disease genomics and B. pertussis vaccination outcome prediction models. Scientific honors include: Chair, ASA Genomics and Genetics Section (2020) Fellow, American Statistical Association (2017) NIH BMRD study section member (2017-2021) Young Investigator, International Indian Statistical Association (2016) She has taught graduate-level courses in human genetics statistics and probability models for over 15 years. Current research receives NIH/NIDA R01 and NIDDK R21 grants, with co-investigator roles in epidemiology and psychology-led R01 projects.

Yuanhua Fengمشاهده پروفایل

استاد

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +۵ مورد دیگر

Prof. Dr. Yuanhua Feng leads the Chair of Econometrics and Quantitative Methods at the University of Paderborn 's Faculty of Business Administration and Economics , focusing on Financial and Economic Data Science . His research includes semiparametric modeling of seasonal time series, long memory processes, spatial time series, and machine learning hybrids. Doctoral supervision for Sebastian Letmathe (2023), Bastian Schäfer (2022), Xuehai Zhang (2018), and others Teaching courses: Econometrics, Financial Econometrics & Quantitative Risk Management Research areas emphasize: Semiparametric GARCH/ACD models for risk management Non-negative financial process forecasting Spatial time series with long memory Hybrid time series/machine learning models Recent publications focus on: Risk measurement with long-memory GARCH Spatial volatility modeling Box-Cox transformed models DeSeaTS seasonal decomposition Notable collaborations include DFG projects and joint PhD supervision with Chinese institutions. Teaching materials integrate R packages like smoots , rugarch , and custom tools for VaR/ES applications.

Stasha Lauriaمشاهده پروفایل

مدرس

• Intelligent Robotics
• Neural Networks
• Pattern Recognition
• +۶ مورد دیگر

Dr. Stasha Lauria is a Lecturer in the Department of Computer Science within the College of Engineering, Design and Physical Sciences at Brunel University London. With over 15 years of experience in intelligent robotics, she specializes in human-machine interactions, neural networks, and pattern recognition, leading the Brunel Robotics Laboratory for cognitive mobile robot experiments. Education: Laurea, University of Studies “Federico II” of Napoli, Italy Ph.D. in Cybernetics, University of Reading, UK Research Focus: Her work centers on modeling mobile robots through natural language interactions, converting human speech into robot actions, and AI-driven signal processing. Current projects investigate social media's impact on human-robot dialogue management and robotics as educational tools, with strong emphasis on neural networks and big data applications in vision systems. Publication Trends: Recent publications (2022-2009) reveal consistent innovation in computer vision (object detection, segmentation) and human-robot interaction, increasingly integrating deep learning for manufacturing and vision tasks. Educational robotics remains a parallel thread, with quantitative evaluations of programming pedagogy. Research Grants: A human-centred internet of things platform for the sustainable mine of the future (European Commission, May 2020 - April 2024) Intelligent data-driven pipeline for certified metal parts manufacturing (European Commission, October 2018 - March 2023) Big Data Learning-based QoS Analysis for Cloud-services (Royal Society, March 2016 - February 2018) Laboratory & Collaboration: Founder of the Brunel Robotics Laboratory, she collaborates extensively with Dr. Theodora Koulouri, Prof. Xiaohui Liu, and Dr. Stephen Swift on human-robot interaction, as evidenced by co-author networks spanning neural networks, dialogue systems, and multi-agent architectures.

• Statistics
• Applied Mathematics
• Systems Biology
• +۲ مورد دیگر

Dr. Hannes Matuschek is a Researcher in the Department of Applied Mathematics at the University of Potsdam, Germany, with office space 2.09.1.24 and contact email hannes.matuschek@uni-potsdam.de. He actively participates in the institute's academic events including working group seminars and colloquia. Research Interests: His work spans Statistics, Applied Mathematics, Systems Biology, and Biomechanics. Key contributions include statistical methodology for linear mixed models (addressing Type I error/power tradeoffs and interaction effects), smoothing spline ANOVA for eye movement analysis in reading, stochastic modeling of gene regulatory networks, and vector field manipulations on spherical domains. His research bridges theoretical frameworks with applications in cognitive science, systems biology, and fraud detection. Publication Trends: His 12 publications (2012-2019) reveal a trajectory from computational systems biology (stochastic biochemical kinetics tools like iNA) toward statistical methodology development. Early work focused on noise approximation in gene networks, evolving into eye-tracking analysis and vector field mathematics, demonstrating consistent integration of advanced statistics with domain-specific challenges across biological and cognitive sciences. Scientific Awards: No awards were mentioned in the provided materials. Advising and Grants: No information was provided regarding student supervision, grant funding, or research collaborations. Labs and Teams: He operates within the Applied Mathematics research group at the University of Potsdam, contributing to seminars and events in analysis and interdisciplinary applications as evidenced by his institutional presence.

• Statistics
• Functional Data Analysis
• Aggregation Theory

Prof. Dr. Alois Kneip is a leading academic at the Department of Economics , University of Bonn, with affiliations to the Institute for Finance & Statistics and the Hausdorff Center for Mathematics . His research focuses on advanced statistical methodologies, particularly in Functional Data Analysis , Aggregation Theory , and Nonparametric Statistics . His work spans interdisciplinary applications, including econometric modeling, growth curve analysis, and high-dimensional regression. Key contributions include developing frameworks for Malmquist indices , DEA efficiency scores , and functional principal component analysis . Recent publications emphasize spatial regression, parameter cascading, and reconstruction of fragmented functional data. Alois Kneip’s research has been published in top-tier journals such as the Journal of the American Statistical Association , Annals of Statistics , and Econometric Theory . His methodological innovations bridge theoretical statistics with practical problems in economics, finance, and biomedical data analysis. He actively contributes to teaching and academic leadership at the University of Bonn.

Dominik Lieblمشاهده پروفایل

استاد

• Functional Data Analysis
• Nonparametric Statistics
• Econometrics
• +۳ مورد دیگر

Dominik Liebl is a Professor of Statistics at the University of Bonn's Department of Economics and a member of the Hausdorff Center for Mathematics (HCM), a Cluster of Excellence funded by the German Science Foundation (DFG). He also holds a visiting associate position at Colorado State University's Department of Statistics. His research spans Functional Data Analysis , Nonparametric Statistics , and Longitudinal Data Analysis , with applications in energy economics, finance, e-commerce, emotion psychology, and biomechanics. Recent methodological work focuses on simultaneous inference and statistical fairness . The articles in his profile demonstrate a strong emphasis on functional data methodologies applied to diverse domains like COVID-19 seroprevalence , electricity markets , and human movement science . Key subfields include confidence band design , biomechanical hypothesis testing , and high-dimensional econometric modeling . He actively contributes to open science through R-packages at CRAN/GitHub and serves as Associate Editor for the Journal of the Royal Statistical Society: Series C.

Mingzhou Yinمشاهده پروفایل

پژوهشگر ارشد

• Data-based modeling
• System identification
• Sparse learning theory
• +۳ مورد دیگر

Dr. Mingzhou Yin is a postdoctoral researcher at the Institute of Automatic Control within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been working since August 2024. He received his Doctor of Sciences degree from ETH Zurich in 2024 under the supervision of Prof. Roy S. Smith, with a dissertation titled 'Regularized and Nonparametric Approaches in System Identification and Data-Driven Control.' His research interests span data-based modeling and control, sparse learning theory, system identification using subspace and regularized methods, model predictive control, and periodic system theory. Dr. Yin has developed innovative approaches in low-rank matrix regression, Gaussian process-based control of nonlinear systems, and closed-loop identification frameworks. His work bridges theoretical advances with practical applications in energy-flexible buildings and aerospace systems. Dr. Yin has received significant recognition including the IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award and the Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize in 2023. His publications in IEEE Control Systems Letters, Automatica, and other top journals demonstrate his contributions to data-driven control theory. IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award (2023) Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize (2023) As an educator, Dr. Yin has supervised numerous student projects on data-driven predictive control, sparse learning algorithms, and closed-loop identification of networked systems. His teaching includes 'Data- and Learning-Based Control' exercises and previous TA roles for 'Robust Control and Convex Optimisation' and 'System Identification' courses.

• Probabilistic Machine Learning
• Probabilistic Circuits
• Probabilistic Programming
• +۲ مورد دیگر

Martin Trapp is an Academy Postdoctoral Researcher in the Department of Computer Science at Aalto University, specializing in probabilistic machine learning. He is affiliated with Professor Arno Solin's research group, focusing on advancing tractable probabilistic models for real-world applications. His research centers on Probabilistic Circuits , Probabilistic Programming , and Bayesian Nonparametrics , with emphasis on hardware-efficient implementations for edge devices and multimodal systems. Key interests include uncertainty quantification in deep learning, neurosymbolic AI integration, and medical imaging applications. His work bridges theoretical foundations with practical deployment constraints, particularly in resource-limited environments. Analysis of his 15 most recent publications (2022-2025) reveals three dominant trends: (1) hardware-aware probabilistic inference for TinyML applications, (2) scalable Bayesian methods using bitstring representations and probabilistic programming, and (3) multimodal robustness in vision-language systems and medical imaging. His contributions span from theoretical circuit representations to real-world implementations in mammography analysis and vision-language models. Trapp secured a HIIT short-term project grant (November 2022) for "Positive Semi-Definite Circuits" under the Department of Computer Science. No formal advising relationships are documented in available sources. He actively collaborates with researchers including Arno Solin, Rui Li, and Marcus Klasson across institutions like Aalto University and the Helsinki Institute for Information Technology. As a core member of Aalto's Probabilistic Machine Learning group, he contributes to advancing probabilistic AI methodologies with applications in healthcare, edge computing, and multimodal reasoning. His current work emphasizes deployable probabilistic systems that maintain rigorous uncertainty quantification while meeting hardware constraints.

• Statistics and Probability

Ersin Yılmaz serves as a Postdoctoral Researcher within the Department of Computer Science at Aalto University, conducting advanced research at the intersection of statistical theory and computational applications. His work contributes to the university's data science initiatives through methodological innovations in regression analysis. His research concentrates on statistical methodology development, particularly for complex data structures involving correlated errors and high-dimensional spaces. Key interests include shrinkage estimation techniques, kernel-based regression frameworks, and robust modeling approaches applicable to machine learning systems. This work bridges theoretical statistics with practical computational implementations in computer science contexts. Analysis of his publication record reveals a focused trajectory in advanced regression methodologies, specifically addressing estimation challenges in partially linear models with dependency structures. His work demonstrates technical rigor in mathematical statistics while maintaining relevance to contemporary data science applications, particularly in improving estimator efficiency under complex error dependencies. Scientific Awards: No scientific awards, fellowships, or honors were documented in the source material. Current information indicates no formal student supervision roles or grant-funded projects. The researcher operates within Aalto University's computer science infrastructure without specific lab or team affiliations mentioned in available records.

• Statistics
• Computer Science
• Economics
• +۶ مورد دیگر

Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.

Julien Fageotمشاهده پروفایل

پژوهشگر

• Sparsity
• Random Processes
• Approximation Theory
• +۷ مورد دیگر

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

Gilles Blanchardمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Statistical Learning Theory
• +۵ مورد دیگر

Gilles Blanchard is a Professor at Paris-Saclay University , affiliated with the Institute of Mathematics in Orsay. His research focuses on statistical learning theory , multiple testing , kernel methods , and high-dimensional statistics . Key Contributions: Decontamination of mutually contaminated models Novelty detection with semi-supervised learning Non-Gaussian component analysis (NGCA) for dimension reduction Scientific Awards: PhD thesis award from University of Paris 6 (2004) Google Scholar h-index of 49 with over 8,000 citations Recent Research Trends: False Discovery Rate (FDR) control in structured hypothesis testing Conformal prediction for link analysis Adaptive sampling in restless bandits Statistical learning on measure spaces His work bridges theoretical statistics with practical machine learning applications, including genome-wide association studies , persistent homology in topological data analysis , and random feature moments for compressive learning . He is involved in open-source software development like μTOSS for multiple testing standardization and has advised on projects related to flow cytometry and Hadrontherapy applications.

Max Hinneمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Neuroscience
• +۶ مورد دیگر

Max Hinne is an assistant professor at the Department of Artificial Intelligence, Radboud University, Nijmegen, The Netherlands, where he leads the Uncertainty in Complex Systems research group. His work bridges artificial intelligence, neuroscience, and statistics through advanced Bayesian methodologies. Dr. Hinne's research focuses on Bayesian modeling of brain networks using neuroimaging data. His primary interests include: Bayesian nonparametric models, particularly Gaussian processes Structural and functional brain connectivity analysis Predictive modeling of neural systems Causal inference frameworks Uncertainty quantification in complex systems Development of computational tools for neuroscience His approach emphasizes how probabilistic methods can address uncertainty in complex biological systems while providing interpretable models of brain function. Analysis of Dr. Hinne's publication trajectory reveals a consistent focus on Bayesian methods applied to increasingly diverse domains. Starting with foundational work in brain connectomics, his research has expanded to include applications in developmental psychology, medical genetics, and educational technology. His most recent work demonstrates sophisticated integration of nonparametric Bayesian methods with domain-specific challenges, particularly in handling uncertainty in complex, high-dimensional data across multiple scientific fields. Dr. Hinne actively mentors students and invites master's thesis projects focused on Bayesian nonparametric methods for neuroimaging data. He has developed several software tools including the Bayesian Connectomics Toolbox (BaCon), latent space modeling code, and GP CaKe for causal inference. His research group maintains strong connections with the Donders Institute for Brain, Cognition and Behaviour, facilitating interdisciplinary collaborations between statisticians, neuroscientists, and domain experts.

Abi Adamsمشاهده پروفایل

پژوهشگر ارشد

• Labor Economics
• Public Policy
• Development Economics
• +۴ مورد دیگر

Dr. Abi Adams is a Research Fellow at the Institute for Fiscal Studies (IFS), specializing in applied microeconomics with expertise in labor markets, household decision-making, and development economics. Her rigorous empirical work combines revealed preference methodologies with experimental approaches to analyze critical policy issues including marriage markets, education incentives, and poverty measurement. Her research spans Labor Economics , Behavioral Microeconomics , and Public Policy Analysis , with notable contributions to understanding: Marriage market dynamics and educational attainment in developing contexts Time inconsistency in household consumption decisions Consumer consideration sets and demand responsiveness Intergenerational transfer motivations Poverty measurement methodologies accounting for differential inflation Dr. Adams' publication record demonstrates consistent methodological innovation, particularly in adapting revealed preference frameworks to complex decision environments involving subjective beliefs and heterogeneous preferences. Her work frequently employs discrete choice experiments, hypothetical vignettes, and nonparametric estimation techniques to address identification challenges in field settings. She received the ESRC Outstanding Impact in Public Policy award in 2018 for research that directly contributed to the UK Supreme Court's unanimous ruling against employment tribunal fees. This landmark policy impact highlights the real-world significance of her empirical work on labor market institutions. Her research has been published in top economics journals including The Quarterly Journal of Economics , with additional working papers through IFS and collaborations on poverty measurement affecting official statistical methodologies.