جست‌وجوی استاد و پژوهشگر

• Inverse Problems
• Statistical Learning
• Machine Learning
• +۴ مورد دیگر

Abhishake Abhishake is a Postdoctoral Researcher at the Department of Computational Engineering, LUT School of Engineering Sciences, focusing on inverse problems and statistical learning. His research spans mathematics, machine learning, and computational optimization. 2023–present: Postdoctoral Researcher, LUT University, Finland 2021–2022: Postdoctoral Researcher, Technische Universität Berlin, Germany 2018–2021: Postdoctoral Researcher, University of Potsdam, Germany His research interests include inverse problems, regularization methods, and machine learning. He has published extensively on Tikhonov regularization, nonparametric testing, and multi-penalty strategies. His work bridges mathematical theory with applications in pharmacometrics and statistical learning. Key trends in his publications (16 total) include inverse problems in Hilbert scales, regularization techniques for nonlinear statistical learning, and multi-task learning frameworks. Subfields cover oversmoothing penalties, manifold regularization, and convergence analysis.

Lukas Steinbergerمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +۳ مورد دیگر

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

Damiano Varagnoloمشاهده پروفایل

مدرس ارشد

• Distributed Optimization
• Control Theory
• Distributed Estimation
• +۶ مورد دیگر

Dr. Damiano Varagnolo is a Senior Lecturer at the Control Engineering Group, Luleå University of Technology, Sweden. His research focuses on distributed optimization, control systems for data centers and HVAC, and biomedical engineering applications. Current affiliation: Luleå University of Technology Primary research areas: Distributed optimization, Newton-Raphson Consensus methods, networked systems, energy-efficient control Selected applications: Smart building systems, biomedical modeling of pain responses, data center thermal management His work on distributed optimization explores asynchronous protocols and robustness in networked systems. Recent publications demonstrate applications in peer-to-peer networks, biomedical engineering, and energy-efficient computing infrastructure. Key collaborative projects include IEEE Transactions publications on data center cooling, European Control Conference contributions on sensor calibration, and IFAC symposium presentations on biomedical modeling. His distributed Gaussian regression methods appear in TPAMI publications.

• Nonparametric Statistics
• Semiparametric Models
• Credit Scoring
• +۴ مورد دیگر

Professor Marlene Müller serves as Professor of Applied Statistics at Beuth University of Applied Sciences Berlin within the Department of Mathematics, Physics, and Chemistry in the School of Engineering. Her academic career spans over two decades with significant contributions to nonparametric and semiparametric statistical methods, particularly applied to financial data analysis and credit scoring. Her research interests center on Nonparametric Statistics , Semiparametric Models , and Statistical Computing , with a strong emphasis on practical applications in finance. She has developed numerous R implementations for statistical methods, making complex techniques accessible to practitioners. Her work bridges theoretical statistics with real-world financial applications, particularly in credit risk modeling and portfolio analysis. Professor Müller's publication record shows a consistent focus on applying advanced statistical techniques to financial problems. Her work evolved from foundational theoretical contributions in the 1990s to more applied financial modeling in the 2000s, with a particular emphasis on credit scoring systems under regulatory frameworks like Basel II. She has maintained a strong focus on developing computational tools, especially through the R programming language, to implement these statistical methods. She is co-author of the influential textbook Nonparametric and Semiparametric Models (Springer Series in Statistics, 2004) with Härdle, Sperlich, and Werwatz, which has become a standard reference in the field. Her teaching portfolio includes advanced statistics courses with a strong computational component, and she has developed extensive educational materials for teaching nonparametric methods. Professor Müller maintains an active consultation schedule, with office hours listed through the summer semester of 2025, indicating her ongoing engagement with students and academic work. Her contributions to statistical computing through R package development and teaching materials demonstrate her commitment to advancing both theoretical and applied statistics education.

Le Wangمشاهده پروفایل

استاد

• Microeconomics
• Applied Econometrics
• Economic Analytics
• +۵ مورد دیگر

Le Wang is the David M. Kohl Chair and Professor in the Department of Agricultural and Applied Economics at Virginia Tech. He holds roles such as co-editor of China Economic Review and Journal of Labor Research , and associate editor of Econometric Reviews . He serves on the Board of Trustees of the Southern Economics Association, the Human Capital and Economic Opportunity Global Working Group at the University of Chicago, and the Institute for Labor Studies (IZA) in Germany. As director of the Young Scholars Program of the Global Labor Organization (GLO), he promotes interdisciplinary research and academic collaboration. He earned his Ph.D. in Economics from Southern Methodist University in 2006 and a B.A. in International Finance from Jinan University in 2001. His research focuses on applied econometrics, including causal inference and machine learning applications, as well as policy issues in energy economics, public policy, food and health systems, development economics, and labor markets. Notable contributions include studies on intergenerational mobility, gender earnings gaps, and the impact of food aid on civil conflict. Awarded the Kuznets Prize (2018) and multiple Outstanding Professor Awards, his work has been recognized for advancing methodological innovations and policy-relevant insights. Recent accolades include the 2023 Emerald Literati Award for his chapter on “Women’s Potential Earnings Distributions” and the VPR and Partnerships Award for Excellence in Transdisciplinary Research (2023). Le Wang’s expertise extends to editorial leadership and academic administration, reflecting his commitment to advancing rigorous research and equitable policy solutions.

Inyoung Kimمشاهده پروفایل

استاد

• Semiparametric/Nonparametric modeling
• Bayesian modeling and computation
• Biostatistics
• +۵ مورد دیگر

Inyoung Kim is a Professor in the Department of Statistics at Virginia Tech, affiliated with the College of Science. She joined Virginia Tech in 2007 after completing a postdoc in Biostatistics at Yale University and a PhD in Statistics from Texas A&M University. Her research focuses on semiparametric/nonparametric modeling, Bayesian computation, biostatistics, and bioinformatics. Education: PhD in Statistics, Texas A&M University (2002) Postdoc in Biostatistics, Yale University (2007) M.S. in Applied Statistics, Yonsei University B.S. in Mathematics, Jeju National University Her research interests span semiparametric/nonparametric modeling , Bayesian methods , biostatistical applications in environmental health and genomics, and functional data analysis . Recent work includes advancements in kernel machine regression for pathway analysis and nonparametric Bayesian clustering. Notable awards include the Best Paper Award in Biometrics (2018) , ENAR Student Paper Award (2019) , and the Best Biometrics Paper of the Year (2016) . She is actively involved in professional organizations like the ASA and ISBA. Her teaching includes courses on nonparametric statistics, regression analysis, and Bayesian methods. She collaborates across disciplines, including biomedical research and environmental epidemiology.

Bo Wangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cancer
• Genomics
• +۴ مورد دیگر

Dr. Bo Wang is an Assistant Professor at the University of Toronto's Department of Laboratory Medicine & Pathobiology, with a joint appointment in the Department of Computer Science. He holds the Canada CIFAR Artificial Intelligence Chair and serves as Chief Artificial Intelligence Scientist at the University Health Network (UHN). Previously, he was affiliated with the Department of Medical Biophysics and Surgery. Dr. Wang leads the Temerty Centre for AI Research and Education in Medicine (T-CAIREM), focusing on integrating AI into biomedical research and education. He earned his PhD in Computer Science from Stanford University in 2017, specializing in computational biology, cancer subtype prediction, and single-cell analysis. His research interests bridge artificial intelligence, genetics, and healthcare, with a focus on causal inference, machine learning applications in biomedicine, and genomic data analysis. He has pioneered courses like Basic Principles of Machine Learning in Biomedical Research alongside Dr. Rahul G. Krishnan. His work includes developing algorithms for high-dimensional variable selection and instrumental variable methods in causal analysis. Dr. Wang's publications emphasize causal mediation analysis, genetic risk scoring, and longitudinal health outcomes. Notable contributions include studies on vaccine efficacy against long-COVID, schizophrenia treatment efficacy, and Alzheimer’s disease biomarker modeling. Awards include the Canada CIFAR AI Chair (Vector Institute). His research is supported by collaborations with leading institutions and spans computational biology, healthcare analytics, and precision medicine.

Elisabeth GASSIATمشاهده پروفایل

استاد

• Statistics
• Probability
• Hidden Markov Models
• +۷ مورد دیگر

Elisabeth GASSIAT is a Professor at the University of Paris-Saclay, affiliated with the Orsay Mathematics Institute and the Probability and Statistics Team. She is a Senior member of the Institut Universitaire de France (IUF) for the period 2020-2025, recognizing her significant contributions to academia. Her primary research interests include: Hidden Markov Models and their theoretical properties Mixture models and nonparametric statistics Deconvolution and inverse problems with unknown noise Machine learning theory with focus on privacy and security Order identification and model selection Professor GASSIAT's recent work shows a strong evolution from classical statistical theory toward modern machine learning challenges, particularly in understanding theoretical limits of learning in complex models and addressing privacy concerns in machine learning systems. Her research spans both theoretical foundations and practical applications across various domains, with a remarkable consistency in methodological rigor throughout her career. She has received notable recognition as a Senior member of the Institut Universitaire de France (2020-2025). Professor GASSIAT is actively involved in teaching, offering courses such as M1 "Statistique" and Mathématiques 2 (Polytech). She has also co-organized the MHC2021 International conference on Mixtures, Hidden Markov models and Clustering with H. Holzmann and P. Vandekerkhove, demonstrating her leadership in the international statistics community.

Fushing Hsiehمشاهده پروفایل

استاد

• Data Mechanics
• Complex Systems Analysis
• Statistical Modeling
• +۳ مورد دیگر

Fushing Hsieh is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on Data Mechanics and Integrative Pattern Inferences on Complex Systems, with applications spanning finance, bioinformatics, and behavioral science. He holds a Ph.D. and has been actively involved in academic activities such as Davis Science Café. Research Interests: Development of statistical frameworks for analyzing complex systems High-frequency financial data modeling and volatility analysis Nonparametric methods for discrete time series in bioinformatics Network structures and signal transmission in emotion systems Recent Work Trends: Recent publications emphasize interdisciplinary applications of statistical mechanics, including stock dynamics modeling, sparse signal detection, and emotion response systems analysis. His work bridges computational statistics with real-world systems in finance, biology, and psychology. Grants & Advising: While specific grants are not detailed here, his extensive publication record suggests sustained research funding. No advised students are listed in the provided materials. Labs/Teams: Collaborates across departments at UC Davis, though specific lab affiliations are not explicitly mentioned.

Sigbert Klinkeمشاهده پروفایل

استاد

• Statistics
• Data Visualization
• E-Learning
• +۳ مورد دیگر

Sigbert Klinke is a Professor in the Chair of Statistics at Humboldt University of Berlin's Faculty of Economics and Business Administration. He specializes in statistical methodology, computational statistics, and educational technology. His work focuses on developing interactive e-learning platforms for statistics, such as MM*Stat and wiki-based systems. Klinke leads research in statistical software development, including R packages and open-source tools like Yxilon. Education: PhD in Statistics (implied by academic role). Research interests include data visualization, exploratory analysis, and reproducible research. He has authored numerous publications in statistical methodology, software development, and pedagogical innovations. His contributions span over 20 years, with impactful work in both academic research and educational tool creation. Publications emphasize statistical software (e.g., R packages, XploRe), e-learning frameworks, and methodological advancements. His work bridges theoretical statistics with practical applications, particularly in large-scale educational settings. Klinke's projects often involve interdisciplinary collaboration, such as integrating satellite image analysis with statistical computing environments.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Katharina Prokschمشاهده پروفایل

پژوهشگر

• Statistics
• Biomedical Imaging
• Data Analysis
• +۴ مورد دیگر

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.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۵ مورد دیگر

Frank Werner is a Professor of Mathematics at the Chair of Scientific Computing, University of Würzburg, specializing in statistical inverse problems and regularization theory. His research bridges statistics and inverse problems, with applications in biophysics, particularly fluorescence microscopy and non-Gaussian noise modeling. Education: Diplom in Mathematics (2009) and PhD (2012) from Georg-August-Universität Göttingen. Werner's research focuses on uncertainty quantification via minimax tests, nonlinear inverse problems, and photonic imaging. His recent work includes adaptive regularization methods and multiscale scanning techniques. He leads the Inverse Problems team and collaborates internationally, including at Fudan University, Technical University of Chemnitz, and the Max-Planck-Institut für biophysikalische Chemie (2014–2020). His publications span journals like Inverse Problems , Annals of Statistics , and SIAM Journal on Numerical Analysis , with trends emphasizing Poisson data, impulsive noise, and computational algorithms for microscopy. Scientific Awards: Diplomprüfung with Distinction (2009) Erskine Fellowship (2023) Werner is actively involved in academic service, organizing conferences (e.g., AIP2025 in Rio), and mentoring collaborations. He is married with two sons and maintains affiliations with societies like the EMS-TAG Inverse Problems and the Society for Inverse Problems.

Silvia Liveraniمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +۳ مورد دیگر

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 Senمشاهده پروفایل

استاد

• Nonparametric Statistics
• Large Sample Theory
• Shape-Constrained Estimation
• +۴ مورد دیگر

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.