جست‌وجوی استاد و پژوهشگر

Yiqi (Annie) Tangمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Bayesian Statistics
• Conformal Prediction
• +۳ مورد دیگر

Annie Tang is an Assistant Professor in the Statistics Department at Colby College, Waterville, ME. Her research spans both theoretical and applied statistics, with a focus on high-dimensional data analysis and Bayesian methodologies. She earned her PhD in Statistics from North Carolina State University under the supervision of Ryan Martin. Dr. Tang's research interests encompass high-dimensional statistics, Bayesian and Bayesian-adjacent methods, conformal prediction, variational inference, and foundational statistics. She applies these methods to health services research, including population health and sepsis outcomes. Her work often addresses sparse modeling and empirical prior methods to improve statistical inference in complex datasets. Analysis of her recent publications (2020-2025) reveals a strong focus on developing novel Bayesian and empirical Bayes techniques for high-dimensional problems, with increasing applications in healthcare. Her research bridges theoretical statistics and real-world health policy questions, particularly in sepsis mortality and regional healthcare variation. She has also contributed software tools such as the R package 'ebreg'.

Lehel Csatóمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Robotics
• Data Analysis
• +۴ مورد دیگر

Lehel Csató is a Professor at the Faculty of Mathematics and Informatics of Babeș-Bolyai University in Cluj-Napoca, Romania. His academic journey includes a PhD from Aston University and a postdoctoral position at the Max Planck Institute for Biological Cybernetics. He specializes in Machine Learning, Probabilistic Robotics, and Nonparametric Bayesian Methods, with a focus on Gaussian Processes and their applications in data analysis and robotics. Teaching includes courses in Logic and Functional Programming (Hungarian), Numerical Modelling in Data Analysis (English), and Artificial Intelligence (Hungarian). His research emphasizes sparse Gaussian Process approximations and their use in reinforcement learning, robotics, and medical image analysis. He contributed to the development of the Sparse Online Gaussian Process toolbox. Publications span topics from deep learning architectures to optimization techniques in neural networks. He has organized the ClujUAV drone navigation competition and collaborated on interdisciplinary projects involving robotics, vision, and medical data analysis.

Lixin Shenمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +۴ مورد دیگر

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Zachary Sunbergمشاهده پروفایل

استادیار

• Aerospace autonomy
• Partially observable Markov decision processes (POMDPs)
• Game theory
• +۵ مورد دیگر

Zachary Sunberg is an Assistant Professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. He is affiliated with the Research and Engineering Center for Unmanned Vehicles (RECUV) and leads the Autonomous Decision and Control Lab (ADCL). His research focuses on artificial intelligence for autonomous systems, particularly in aerospace autonomy, decision-making under uncertainty (via POMDPs), and human-AI interaction. Sunberg holds a PhD from Stanford University (2018), an MS and BS from Texas A&M University (2013, 2011). His work spans algorithm development for autonomous vehicles, including safe and efficient driving, rotorcraft control, and game-theoretic planning. Notable contributions include the POMCP and POMCPOW algorithms for continuous POMDPs, and open-source frameworks like POMDPs.jl. He has received a NSF CAREER Award (2024) and a NSF Graduate Fellowship (2012). Academic roles include mentoring the AI4ALL program, teaching courses on decision-making under uncertainty, and advising projects at the intersection of robotics and AI. His lab collaborates on applications ranging from weather observation to human-centered autonomy in unmanned systems.

Anna Scampicchioمشاهده پروفایل

پژوهشگر

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +۳ مورد دیگر

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +۵ مورد دیگر

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

Aliaksandr Hubinمشاهده پروفایل

دانشیار

• Statistics
• Artificial Intelligence
• Econometrics
• +۴ مورد دیگر

Aliaksandr Hubin is an Associate Professor of Statistics at Østfold University College (OUC), affiliated with the Section for Research Administration. He provides statistical support to researchers at OUC while conducting methodological research in Bayesian statistics, machine learning, and operations research. His academic background includes a PhD from the University of Oslo (2014–2018), a Master's from Molde University College (2012–2014), and a Specialist degree from Belarusian State University (2008–2013). His research focuses on Bayesian model selection, nonlinear regressions, neural networks, and weak supervision in machine learning. Hubin has held positions at the Norwegian Computing Center (2018–2020) and the University of Oslo as a postdoc (2021–2022). His work spans methodological advancements in MCMC algorithms, variational inference, and applications in epigenetics, econometrics, and healthcare. Key achievements include developing the GMJMCMC algorithm for Bayesian logic regression and the 'skweak' framework for weak supervision in NLP. His awards include the NIMA 2014 award for best MSc graduate, Belarusian Ministry of Education First Award (2013), and recognition at the Graybill 2017 conference. His research has been published in journals like Bayesian Analysis , Neural Computing & Applications , and Scientific Reports , with a focus on model uncertainty quantification and scalable Bayesian methods.

• Bayesian Statistics
• Neuroscience
• Time Series Analysis
• +۳ مورد دیگر

Beniamino Hadj-Amar is a Research Fellow in the Department of Statistics at Rice University, specializing in advanced statistical methodologies with applications in neuroscience and biomedical research. His work integrates Bayesian statistical models, time series analysis, and computational techniques to address complex problems in health and cognitive science. Education: PhD in Statistics (University of Warwick, UK), MSc in Statistics (University of Warwick, UK), and BSc in Mathematical Statistics and Data Management (University of Genoa, Italy). Research focuses on Bayesian modeling of neural and physiological systems, including circadian rhythms, neuromodulator dynamics, and gesture phase segmentation. Recent contributions include innovative approaches to improve data quality in neuroimaging and telemetric health monitoring. His interdisciplinary work bridges statistics with neuroscience, genomics, and clinical applications. Publications span 2019–2025, emphasizing methodological advancements in nonstationary time series, autoregressive models, and hidden Markov frameworks. His research addresses both theoretical challenges and practical applications in health informatics and systems biology.

• Radio Astronomy
• Medical Imaging
• Compressed Sensing
• +۳ مورد دیگر

Yves Wiaux is a Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He specializes in advanced signal processing techniques with applications in radio astronomy and medical imaging. His research focuses on compressed sensing, optimization algorithms, and imaging methods for radio interferometry and MRI. His work integrates mathematical frameworks like wavelet transforms and sparse regularization to address challenges in high-dimensional data reconstruction. Notable contributions include the development of algorithms like SARA (Sparsity Averaging Reweighted Analysis) and PURIFY for radio interferometric imaging, as well as applications in medical imaging such as magnetic resonance fingerprinting. Wiaux collaborates extensively in international projects, including the LOFAR Two Meter Sky Survey. He advises PhD students through the BASP (Biomedical and Astronomical Signal Processing) group and leads research initiatives in scalable imaging techniques for large datasets.

Joshua Lukemireمشاهده پروفایل

استاد پژوهشی

• Imaging Bayesian Analysis
• High-Dimensional Imaging Data Analysis
• Brain Network Analysis
• +۳ مورد دیگر

Joshua Lukemire is an Assistant Research Professor at Emory University's Department of Biostatistics and Bioinformatics. His research focuses on developing statistical methods for analyzing high-dimensional imaging data, particularly in pediatric applications like near-infrared spectroscopy for monitoring red blood cell transfusion efficacy in preterm infants. He also explores brain network differences between clinical groups using Bayesian techniques and hierarchical modeling. Education: B.S. from University of Georgia; M.S. and Ph.D. from Emory University (Department of Biostatistics). Research interests include Imaging Bayesian Analysis, longitudinal brain network connectivity studies, and statistical methods for healthcare applications. His work bridges neuroimaging analysis, pediatric medicine, and advanced computational statistics. Recent publications highlight innovations in functional brain network generation with graph neural networks (e.g., Fbnetgen and Braingb tools), as well as clinical studies on transfusion efficacy in preterm infants and electroconvulsive therapy outcomes. His methodological contributions include sparse Bayesian modeling, optimal experimental designs, and nonparametric vector autoregression approaches. No advising or grant details are listed in the provided text. His work is supported by collaborations across disciplines, though specific lab affiliations are not mentioned.

Peter Sollichمشاهده پروفایل

استاد

• Statistical Mechanics of Disordered Systems
• Statistical Inference
• Machine Learning
• +۳ مورد دیگر

Professor Peter Sollich is a leading academic specializing in statistical mechanics of disordered systems and machine learning methodologies. His research bridges soft matter physics and advanced computational techniques, focusing on rheology, polydispersity, and non-parametric Bayesian inference. He holds a PhD from the University of Edinburgh (1995) and an MPhil from the University of Cambridge (1992). Research Contributions: His work addresses complex systems such as amorphous solids, glassy dynamics, and force chain prediction in jammed materials. He has contributed to theoretical frameworks like mean-field descriptions of aging linear response and oscillatory shear yielding mechanisms. Recent studies leverage graph neural networks for predictive modeling in materials science. Funding & Collaboration: Co-investigator on EPSRC-funded projects including SpeechWave (2018–2022) and Robust Speech Recognition initiatives. His interdisciplinary approach integrates statistical physics with AI, evidenced by collaborations in conferences like STATPHYS23 and editorial roles at journals such as Journal of Physics A . Impact: With 198+ research outputs and 5,818 citations, his work advances understanding of soft materials, disordered systems, and machine learning applications. His methodologies have implications for material design, computational modeling, and interdisciplinary problem-solving.

Carlos M. Carvalhoمشاهده پروفایل

استاد

• Bayesian Statistics
• Causal Inference
• Time Series Analysis
• +۴ مورد دیگر

Carlos M. Carvalho is a La Quinta Centennial Professor in Business and Professor of Statistics at The University of Texas at Austin McCombs School of Business. He joined UT Austin in 2010 after serving on the faculty at the University of Chicago Booth School of Business. Carvalho also serves as the Executive Director of the Salem Center for Policy, which supports research, education, and dialogue around the impact of economic policies on markets and the free enterprise system. Dr. Carvalho's educational background includes: Ph.D. in Statistics, Duke University, 2006 His research focuses on Bayesian statistics in complex, high-dimensional problems with applications ranging from finance to genetics. Carvalho's work encompasses several key areas including causal inference , machine learning , policy evaluation , and empirical asset pricing . His research interests specifically include modern statistical tools for causal inference, dimensionality reduction in high-dimensional models, advanced statistics in asset pricing, and time series analysis. His work often bridges theoretical statistical methodology with practical applications in various domains. Analysis of Carvalho's recent publications reveals a strong focus on Bayesian methods, causal inference, and high-dimensional statistics. His work spans multiple disciplines including statistics, finance, medicine, and social science. A notable trend is the application of Bayesian techniques to complex real-world problems, particularly in causal inference and treatment effect estimation. His research shows increasing interdisciplinary collaboration, with applications in healthcare, finance, and social policy. Dr. Carvalho holds the prestigious La Quinta Centennial Professorship at UT Austin, recognizing his significant contributions to the field of statistics and business. While specific awards aren't detailed in the provided text, his extensive publication record demonstrates recognition by his peers. As an academic leader, Carvalho directs the Salem Center for Policy, which facilitates research on economic policies and market impacts. His work appears to involve collaboration with researchers across disciplines, particularly in finance, healthcare, and social sciences, though specific lab structures aren't detailed in the provided text.

Xinyi Xuمشاهده پروفایل

استاد

• Bayesian Statistics
• Model Uncertainty and Selection
• Decision Theory
• +۴ مورد دیگر

Xinyi Xu is a Professor of Statistics and Vice Chair for Graduate Studies at The Ohio State University's Department of Statistics. She is affiliated with the Institute for Population Research and serves as an Associate Editor for Bayesian Analysis . Her research focuses on Bayesian hierarchical modeling, decision theory, and high-dimensional data analysis with applications in biostatistics, sociology, and finance. Education Ph.D. in Statistics, University of Pennsylvania (2005) M.A. in Statistics, University of Pennsylvania (2003) B.S. in Mathematics, University of Science & Technology of China (2001) Research Interests Bayesian hierarchical modeling Statistical decision theory Model selection and averaging Applications in biostatistics, sociology, and finance Grants National Science Foundation grants (DMS-2015552, DMS-1613110, DMS-1209194) National Institute of Child Health grants National Center for Research Resources grants Labs/Teams Collaborates with the Translational Data Analytics center at Ohio State and interdisciplinary experts.

Nicolas VAYATISمشاهده پروفایل

استاد

• Machine Learning
• Healthcare Technology
• Theoretical Computer Science
• +۳ مورد دیگر

Nicolas VAYATIS is a Professor at the Ecole normale supérieure Paris-Saclay, affiliated with the Centre Borelli and the Department of Mathematics (DER de Mathématiques). He holds an ELLIS Fellowship (Paris unit) and specializes in theoretical foundations of machine learning, with applications to healthcare and industrial systems. He teaches in programs such as the Master M2 MVA, the normalien DER mathématiques, and the AI track at ENS Paris-Saclay. His research focuses on statistical learning, domain adaptation, change-point detection, and graph signal processing. He has advised over 20 PhD/Master’s students, including notable names like Antoine de Mathelin and Charles Truong. His work bridges academic theory and practical applications, collaborating on projects like fall detection using smart floor sensors and gait analysis with inertial measurement units. He contributes to open-source tools like the ruptures library for change-point detection and the ADAPT domain adaptation toolbox. Key collaborations include projects with INSEAD, Université Paris-Saclay, and industry partners like Michelin. His research has been published in venues such as NeurIPS, ICML, and AISTATS, with a focus on robust algorithms and real-world impact.