جست‌وجوی استاد و پژوهشگر

• Big Data Analysis
• Bayesian Statistics
• Genomics Data Analysis
• +۳ مورد دیگر

Madhuchhanda Bhattacharjee is a Professor of Statistics and Data Science with expertise in statistical methodology for large-scale data analysis. Her research focuses on Bayesian integrated modeling of omics data, spatio-temporal processes, and stochastic processes. She holds advanced degrees including a Ph.D. in Statistics from the University of Pune (2000), an M.Phil. in Statistics from the University of Pune (1995), and a Master of Statistics from the Indian Statistical Institute (1993). She is a Fellow of the Royal Statistical Society and an Elected Member of the International Statistical Institute, among other prestigious memberships. Her work addresses critical areas such as microbiome analysis, spatial epidemiology of diseases like cancer, and modeling climate variability using random matrix theory. She has contributed to the development of novel statistical methods for assessing dependence structures in spatio-temporal data and has published extensively on Bayesian approaches to integrate multi-omics datasets. Key research themes include: Inference on large-scale data using random matrices and parallel processing Bayesian modeling of heterogeneous biomedical data Spatio-temporal modeling of public health crises like the 2020 pandemic Her recent work explores statistical methods for predicting preterm birth via maternal blood multi-omics integration and spatial association measures for time series. She co-leads the Statistical Advisory Unit (SAU), providing advanced statistical methodologies for interdisciplinary research projects. Major awards include Fellow of the Royal Statistical Society (2005) and Elected Membership in the International Statistical Institute (2005). She has advised on several grants focusing on omics data integration and disease marker identification. Her contributions span academic leadership roles, including memberships in global statistical societies like the International Indian Statistical Association and the Caucus for Women in Statistics. She has pioneered statistical methodologies applied to environmental and healthcare challenges, emphasizing reproducibility in data-intensive research.

Christiana Charalambousمشاهده پروفایل

مدرس

• Biostatistics
• Medical Statistics
• Pain Perception
• +۴ مورد دیگر

Christiana Charalambous is a Lecturer in Statistics at The University of Manchester. Her research focuses on developing advanced statistical methodologies for medical and health-related applications, particularly in pain perception mechanisms and longitudinal data analysis. She is a core member of the Digital Futures research platform, contributing to statistical innovations in healthcare analytics. Her educational background includes a PhD in Statistics (2011) with a thesis titled Variable Selection in Joint Modelling of Mean and Variance for Multilevel Data , supervised by Professor Jianxin Pan. This work laid the foundation for her subsequent research in joint modelling frameworks. Key research interests span: (1) Biostatistical Modelling including copula-based joint models, longitudinal-survival analysis, and latent class models; (2) Pain Perception Dynamics exploring Bayesian estimation of pain-related traits and their neurophysiological correlates; (3) Infection Dynamics involving stochastic models for within-host disease progression and public health applications. Her methodological work integrates advanced statistical theory with clinical and epidemiological data. Recent articles highlight innovations in Gaussian copula frameworks, Burr distribution applications for infection timing analysis, and boundary effects of expectation in pain perception. Her work frequently bridges statistical theory with practical clinical challenges, such as optimizing biomarker variability analysis in longitudinal studies. As Principal Investigator of the Statistical Advisory Unit (SAU) project, she leads multidisciplinary collaborations advancing statistical methodologies across university departments. No scientific awards are explicitly mentioned in her profile. Her advisory contributions include supervising her own PhD research but no listed advisees. Research outputs are concentrated in statistical methodology with strong interdisciplinary medical applications, reflecting her role in the intersection of statistics and healthcare research.

• Statistics
• Econometrics
• Applied Mathematics
• +۳ مورد دیگر

Yanbo Tang is a Lecturer in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on statistical theory and methodology, including Bayesian inference, high-dimensional asymptotics, numerical integration, and applications in astrophysics and social sciences. He is affiliated with the Artificial Intelligence Network and Mathematics research and teaching staff at Imperial College. Education: Not explicitly stated in the provided text. His research interests span statistical theory such as Laplace and saddlepoint approximations, adaptive quadrature methods, and copula models. He also explores applications in areas like extragalactic X-ray jet variability and parental psychological control effects on adolescents. Recent work emphasizes stochastic convergence rates and assumption-lean inference techniques. Selected publications (2020–2024) highlight contributions to high-dimensional statistical problems, asymptotic behavior of likelihood methods, and Monte Carlo integration challenges. His work bridges theoretical advancements with practical computational solutions in complex data analysis. No grants or awards are explicitly listed in the provided information. He contributes to academic discussions, such as commenting on Vansteelandt and Dukes' work on assumption-lean inference.

Dana Yangمشاهده پروفایل

استادیار

• Statistical Inference
• Machine Learning
• Computational Complexity
• +۴ مورد دیگر

Dana Yang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. She joined Cornell in Spring 2022 after completing a Simons-Berkeley fellowship focusing on computational complexity of statistical inference at UC Berkeley. Previously, she was a postdoctoral associate at Duke University’s Fuqua School of Business. Her education includes a B.S. in Mathematics from Tsinghua University and M.A./Ph.D. in Statistics from Yale University. Yang’s research interests span statistical inference, machine learning, and computational complexity. She focuses on problems involving planted structures (e.g., matching recovery), privacy-preserving algorithms, high-dimensional statistics, and graph theory. Her work bridges theoretical foundations with practical applications in data science. Her recent articles explore phase transitions in statistical recovery, private convex optimization, and algorithmic fairness. Notable contributions include studies on planted matching problems, community detection efficiency, and secure sequential learning protocols. No scientific awards were explicitly mentioned in the provided materials. Her advising history and grant involvement remain unspecified in the current data. Dana Yang is affiliated with Cornell’s Comstock Hall facility, though specific lab or team affiliations were not detailed.

Eric Eisenstatمشاهده پروفایل

دانشیار

• Bayesian Econometrics
• Time-Series Analysis
• Macroeconomics
• +۴ مورد دیگر

Eric Eisenstat is an Associate Professor and Director of HDR Programs in the School of Economics at The University of Queensland (UQ). He holds a Ph.D. from the University of California, Irvine (2007). His research focuses on Bayesian time-series econometrics, structural inference from multivariate models, model uncertainty/averaging, and big data shrinkage estimation. He also works on marketing mix models for policy and private organizations. Education: Ph.D. in Economics, University of California, Irvine (2007) Research Interests: Bayesian econometrics and time-series analysis Structural macroeconomic inference Model uncertainty and averaging Big data applications in economics Marketing mix modeling Consulting: Provides services to policy institutions and private organizations, specializing in big data-driven marketing mix models. Professional Activities: Director of Higher Degree Research (HDR) Programs Available for academic supervision

Nan Yeمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Dr. Nan Ye is a Senior Lecturer in Statistics and Data Science at the University of Queensland's School of Mathematics and Physics. His research focuses on machine learning, statistics, and optimization, with contributions to sequential decision making, weakly supervised learning, and probabilistic graphical models. He holds a PhD in Computer Science from the National University of Singapore (NUS) and double first-class honors in Computer Science and Applied Mathematics from NUS. Previously, he held postdoctoral positions at QUT, UC Berkeley, and NUS. Dr. Ye teaches advanced courses such as STAT3007/7007 Deep Learning, covering topics from foundational machine learning to state-of-the-art deep learning architectures and applications. His work has been published in top venues like NeurIPS, ICML, and UAI, earning awards including the IJCAI-JAIR Best Paper Prize (2022) and UAI Best Student Paper Award (2014). His research interests span theoretical and applied machine learning, including reinforcement learning, optimization algorithms, and their applications in fields like healthcare and environmental science. He actively supervises students in these areas and collaborates on interdisciplinary projects. Dr. Ye's academic profile includes extensive contributions to open-source tools and educational materials, reflecting his commitment to advancing both research and pedagogy in data science.

Stanislav Minskerمشاهده پروفایل

استادیار

• High-dimensional statistics
• Statistical learning theory
• Concentration of measure inequalities
• +۳ مورد دیگر

Stanislav Minsker is an Assistant Professor in the Department of Mathematics at the University of Southern California (USC), where he has been since Fall 2015. His research focuses on theoretical aspects of high-dimensional statistics, statistical learning theory, and concentration of measure inequalities. Before joining USC, he was a Visiting Assistant Professor at Duke University (2012-2014) and worked in Quantitative Analytics at Wells Fargo Securities (2014-2015). He holds a B.Sc. in Mathematics from Novosibirsk State University (2007) and a Ph.D. in Mathematics from Georgia Institute of Technology (2012). He is a co-director of the MS programs in Mathematical Data Science and Statistics at USC. His research has been supported by NSF awards, including CIF-1908905 and DMS CAREER-2045068. His work emphasizes robust statistical methods, including median-based estimators, U-statistics, and algorithms for heavy-tailed data. Key contributions include innovations in robust covariance estimation, median-of-means approaches, and scalable Bayesian inference. His teaching spans advanced courses in statistical inference, time series analysis, and mathematical statistics.

Ming Wangمشاهده پروفایل

استاد

• Biostatistics
• Data Integration
• Causal Inference
• +۱۲ مورد دیگر

Ming Wang, PhD is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. She also serves as Director of the Masters Program in Biostatistics . Her research focuses on statistical method development for biomedical and human health problems, including causal inference, longitudinal data analysis, and computational methods for genomic and imaging data. She collaborates on studies involving cancer, cardiovascular disease, kidney disease, neurodegenerative disorders, and child abuse. Education : PhD in Biostatistics, Emory University (2013) MSc in Biostatistics, University of Louisville (2008) BSc in Applied Mathematics, Peking University (2006) Research Interests : Methodological: Bayesian statistics, missing data, spatial statistics, risk prediction Clinical: Prostate cancer, kidney disease, cardiovascular outcomes, neurodegeneration Key Awards : Elected Member, International Statistical Institute (2021) Dean's Excellence in Teaching Award (2019) NISS/ASA Junior Faculty Presentation Award (2016) Professional Roles : Associate Editor: Biometrics , Statistics in Medicine , Journal of Biopharmaceutical Statistics Statistical Editor: Journal of the American Society of Nephrology Her work bridges statistical innovation with clinical impact, particularly in leveraging large-scale health data for precision medicine and population health strategies.

Bhuvnesh Jainمشاهده پروفایل

استاد

• Cosmology
• Gravitational Lensing
• Machine Learning in Astronomy
• +۳ مورد دیگر

Bhuvnesh Jain is the Walter H. and Leonore C. Annenberg Professor in the Natural Sciences at the University of Pennsylvania's Department of Physics and Astronomy. He co-directs the Penn Center for Particle Cosmology and the Penn Data Driven Discovery Initiative. His affiliations include roles with the Dark Energy Survey (DES), Large Synoptic Survey Telescope (LSST), and Euclid space mission. He holds the Edmund J. and Louise W. Kahn Term Chair (2011-2015) and is a Fellow of the American Physical Society. Education: Ph.D., MIT (1994) A.B. in Physics (High Honors), Princeton University (1989) Research Interests: Cosmology: Formation of large-scale structure, dark matter/dark energy properties Gravitational lensing: Weak lensing analyses, CMB-galaxy cross-correlations Machine learning: Applications to astrophysical data, deep learning for image deblending Galaxy clusters: Splashback radius studies, halo boundary dynamics Modified gravity: Astrophysical tests of gravity theories Recent Article Trends (2025): Focus on resolving cosmological tensions (Hubble constant, structure growth) Weak lensing analyses combining DES, ACT, and Euclid data Development of diffusion-based reconstruction techniques Machine learning approaches for parameter inference Awards/Leadership: Co-chair of LSST Weak Lensing Science Collaboration Advisory roles in DES, LSST, and Simons Observatory Organized Data Science for Social Good programs Advising/Grants: Current advisees: Agrawal, Gomes, Park, Zhong, Gatti, Lin Alumni placements at Chicago, Cambridge, Caltech, and industry Leadership in Penn's Data Science minor and postdoctoral fellowships Labs/Teams: Core member of Penn's cosmology group Collaborations with DES, LSST, and CMB surveys (ACT/SPT) Developed courses on statistical methods in natural sciences

Joseph G. Ibrahimمشاهده پروفایل

استاد

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +۴ مورد دیگر

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.

Daniele Bianchiمشاهده پروفایل

مدرس ارشد

• Empirical Asset Pricing
• Financial Econometrics
• Machine Learning
• +۴ مورد دیگر

Daniele Bianchi is a Senior Lecturer (Associate Professor) in Finance at Queen Mary University of London's School of Economics and Finance. Previously, he held an Assistant Professor position at Warwick Business School. He earned his PhD from Bocconi University in 2014. His research focuses on empirical asset pricing, financial econometrics, machine learning applications in finance, and Bayesian statistical methods. Notable areas include commodity risk premiums, bond risk modeling, systemic risk analysis, and macroeconomic factor dynamics. His work bridges theoretical econometrics with practical financial market analysis. Bianchi has published in top journals such as the Journal of Business and Economic Statistics , Review of Financial Studies , and Journal of Econometrics . His recent work emphasizes large-scale Bayesian models and machine learning techniques for understanding financial markets and macroeconomic systems. He advises students through his teaching and research activities at Queen Mary, though specific student names are not listed. His academic website can be found at whitesphd.com .

Fatih Kizilaslanمشاهده پروفایل

پژوهشگر ارشد

• Biostatistics
• Cure Models
• Frailty Modeling
• +۳ مورد دیگر

Fatih Kizilaslan is a Research Fellow in the Department of Biostatistics at the University of Oslo, Norway, specializing in statistical models for high-dimensional and functional data. He holds a PhD in Mathematics from Gebze Technical University (2015) and has extensive academic experience, including roles as Associate Professor (2019-2023) and Assistant Professor (2016-2019) at Marmara University, Turkey. His research focuses on cure models, frailty modeling, and reliability analysis. He has conducted research at McMaster University (Canada) and the Gebze Institute of Technology (Turkey). His academic interests include survival analysis, statistical inference for extreme-value distributions, and applications of biostatistics in medical research. He is affiliated with the Statistical models for high-dimensional and functional data research group. His work emphasizes methodological advancements in stress-strength reliability, multicomponent systems, and record-based statistical models. Notable contributions include publications on cure models, frailty analysis, and reliability estimation in complex systems. His research bridges theoretical statistics with practical applications in biomedical and engineering contexts. He actively collaborates on projects involving high-dimensional covariates and predictive modeling for clinical outcomes.

Felix Herrmannمشاهده پروفایل

استاد

• Computational Imaging
• Inverse Problems
• Machine Learning
• +۵ مورد دیگر

Felix Herrmann is a Professor at the Georgia Institute of Technology, holding a joint appointment between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering within the College of Computing. He leads the Seismic Laboratory for Imaging and Modeling (SLIM) and co-directs the Center for Machine Learning for Seismic (ML4Seismic). His research focuses on computational imaging, inverse problems, and machine learning applications in geophysics, particularly in seismic and medical imaging. Herrmann has pioneered innovations in compressive sensing for time-lapse seismic data acquisition, earning the SEG Reginald Fessenden Award in 2020. His work emphasizes Bayesian inference, uncertainty quantification, and scalable computational methods for subsurface monitoring, including carbon sequestration and reservoir characterization. Education: Ph.D. in Engineering Physics (Delft University of Technology, 1997), followed by postdoctoral roles at Stanford University and MIT before joining the University of British Columbia (2002). He transitioned to Georgia Tech in 2017 as a Georgia Research Alliance Eminent Scholar in Energy. Research Interests: Herrmann’s cross-disciplinary program integrates randomized linear algebra, PDE-constrained optimization, and high-performance computing. Key areas include digital twin technologies for subsurface monitoring, generative AI for geostatistical modeling, and scalable inversion algorithms (e.g., wavefield reconstruction inversion). His methodologies aim to reduce costs and improve reliability in seismic imaging and carbon storage monitoring. Scientific Contributions: Over 150+ peer-reviewed articles, including seminal work on compressive sensing in seismic acquisition and Bayesian experimental design. His software contributions include InvertibleNetworks.jl and Devito , advancing computational geophysics. Labs & Collaborations: Directs SLIM, fostering industry partnerships through ML4Seismic to develop AI-driven seismic imaging tools. Active in cloud-based scalable workflows and event-driven seismic imaging systems.

• Environmental Data Science
• Data Assimilation
• Climate Modeling
• +۳ مورد دیگر

Dr. Eviatar Bach is a Lecturer in Mathematics of Environmental Data Science at the University of Reading, affiliated with the Department of Mathematics and Statistics within the School of Mathematical, Physical and Computational Sciences. His research focuses on data assimilation, dynamical systems, and environmental applications, particularly in climate modeling, remote sensing, and machine learning integration. He contributes to the Data Assimilation Research Centre (DARC) and teaches courses like ST2PST Probability and Statistical Theory. His work bridges computational methods with real-world environmental challenges, emphasizing uncertainty quantification and predictive modeling. Research interests include ensemble Kalman methods, inverse problems, and the application of statistical techniques to climate and ecological systems. His recent articles highlight advancements in filtering algorithms, parameter estimation, and the use of remote sensing data to assess environmental impacts. Dr. Bach collaborates on projects analyzing monsoon dynamics, solar farm effects, and vegetation responses to climate change. His teaching and research reflect a commitment to advancing data-driven solutions for environmental science.

• Machine Learning
• Signal Processing
• Pattern Recognition
• +۳ مورد دیگر

Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.