جستجوی استاد راهنمای دانشگاهی

• 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 پژوهش

• 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.

Yasser Roudiمشاهده پروفایل

استاد

• Theoretical Neuroscience
• Neural Networks
• Information Processing
• +۳ مورد دیگر

Professor Yasser Roudi holds the position of Professor of Disordered Systems at King’s College London’s Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He earned his PhD from SISSA (International School for Advanced Study) in 2005 and a physics degree from Sharif University of Technology in Tehran. Prior to joining King’s, he worked at prestigious institutions like the Kavli Institute for Systems Neuroscience in Norway and NORDITA. His research focuses on theoretical neuroscience, neural networks, and the physics of disordered systems, employing mathematical tools to model information processing in brains and machines. He has been recognized with awards including the Eric Kandel Young Neuroscientist Prize (2015) and membership in the Royal Norwegian Society of Sciences and Letters. His work explores topics such as grid cells’ toroidal topology, restricted Boltzmann machines, and maximum entropy models for cortical populations. Recent publications highlight advancements in understanding neural oscillations, stochastic processes, and reinforcement learning algorithms. Roudi leads the Disordered Systems research group at King’s, contributing to the study of complex systems and statistical mechanics. Education: PhD, SISSA (2005); BSc, Sharif University of Technology (Physics). Affiliations: King’s College London, Kavli Institute for Systems Neuroscience, NORDITA, UCL, and the Institute for Advanced Study. Roudi’s research bridges theoretical physics and neuroscience, addressing fundamental questions about neural information processing and system dynamics. His awards underscore his impactful contributions to the field, while his interdisciplinary approach continues to push boundaries in understanding complex biological and artificial systems.

• Robust Methods
• Statistical Genetics
• Multivariate Analysis
• +۷ مورد دیگر

Asuman Turkmen is a Professor of Statistics at The Ohio State University, affiliated with the Translational Data Analytics (TDA) initiative and based at the Newark Campus. She holds a PhD from Auburn University (2008) and specializes in robust statistical methods for multivariate analysis and statistical genetics. Her research focuses on outlier detection, rare variant associations in complex diseases, and addressing missing heritability through approaches like structural variations, epigenetics, and gene-environment interactions. Her professional service includes roles such as Newark Campus Ombudsman (2022–present), judge for student research forums, and leadership in university committees (e.g., JEDI Committee, Undergraduate Committee). She co-directs the TDAI Data Science for Women Summer Camp and mentors in doctoral programs. Notably, she received the Ohio State University Newark Scholarly Achievement Award in 2015. Dr. Turkmen’s recent work emphasizes Bayesian methods for population stratification correction, X-linked variant effects, and robust statistical techniques. Her publications span Genetic Epidemiology , Annals of Human Genetics , and other top journals, reflecting her contributions to both theoretical and applied statistical genetics.

Sarat Dassمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Data Science
• +۷ مورد دیگر

Sarat Dass is a Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. He is actively engaged in research, teaching, and supervising PhD students. His primary research interests span Statistics, Bayesian Statistics, Data Science, and their applications in Epidemiology and Spatio-temporal processes. He has published extensively, with 136+ research outputs since 2006, focusing on topics like ionospheric TEC forecasting, disease modeling (e.g., chikungunya, COVID-19), and statistical computing methods. Education & Teaching: Teaches courses such as F79BI: Bayesian Inference and Computational Methods, emphasizing advanced statistical methodologies and computational techniques. Research Interests: His work integrates machine learning (e.g., LSTM, neural networks) with statistical modeling for environmental and health applications. Key areas include predicting ionospheric disturbances during solar flares/earthquakes, analyzing disease spread dynamics, and optimizing hydrocarbon exploration using Gaussian processes. Collaborations: Collaborates internationally, particularly in Malaysia, Indonesia, and Pakistan, on projects involving epidemiology, space weather, and geophysical modeling. His research contributes to Sustainable Development Goals related to health, climate action, and innovation. Advising & Grants: Accepts PhD students and has secured grants for projects on disease modeling and environmental data analysis. His work bridges theoretical statistics with practical applications in public health and geophysics.

Luis Lehnerمشاهده پروفایل

استاد پژوهش

• Strong Gravity
• Gravitational Wave Astronomy
• General Relativity
• +۷ مورد دیگر

Luis Lehner holds the Carlo Fidani Rainer Weiss Chair in Theoretical Physics at the Perimeter Institute for Theoretical Physics and is an Associate Professor in the Department of Physics at the University of Guelph. He also holds adjunct roles at the University of Wisconsin-Milwaukee and Louisiana State University. His research focuses on strong gravity regimes, testing alternative theories of gravity using gravitational wave detectors, and exploring higher-dimensional scenarios and AdS/CFT correspondence. He is a prominent figure in gravitational wave astronomy, working on multimessenger observations and developing frameworks to probe General Relativity's validity. Lehner has held leadership positions including Deputy Faculty Chair at Perimeter Institute and advisory roles at KITP and ICTP-SAIFR. His work bridges theoretical physics with observational astrophysics, emphasizing black hole dynamics, relativistic hydrodynamics, and turbulence. Recent publications highlight advancements in gravitational wave signal analysis, black hole mimicker studies, and nonlinear gravity effects. Education : Not explicitly listed in text, but his roles imply PhD-level training in Physics. His awards include membership in the National Academy of Sciences of Argentina, the Buchalter Cosmology Prize, and fellowships from APS and the Institute of Physics. He actively participates in international collaborations like the Gravitational Wave International Committee and contributes to detector networks like LIGO-Virgo.

Matthias Schonlauمشاهده پروفایل

استاد

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +۳ مورد دیگر

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Julien Chiquetمشاهده پروفایل

مدرس پاره‌وقت

• Statistics
• Machine Learning
• Sparse Methods
• +۱۰ مورد دیگر

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

Jianfeng Luمشاهده پروفایل

استاد

• Mathematical Analysis
• Algorithm Development
• Computational Physics
• +۵ مورد دیگر

Jianfeng Lu is the James B. Duke Distinguished Professor of Mathematics at Duke University, with affiliations in the Department of Mathematics, Chemistry, and Physics within the Trinity College of Arts & Sciences. He holds a Ph.D. from Princeton University (2009). His research focuses on mathematical analysis, algorithm development for computational physics, theoretical chemistry, materials science, and machine learning, particularly in high-dimensional PDEs, quantum dynamics, and multiscale modeling. Notable achievements include the 2024 James B. Duke Distinguished Professorship. His work spans interdisciplinary collaborations, with grants from the National Science Foundation (NSF) and Department of Energy (DOE), addressing challenges in quantum computing, electronic structure theory, and AI-driven material design. Research interests include quantum Markov processes, reinforcement learning theory, and numerical methods for PDEs. He advises numerous graduate students and postdocs, mentoring in applied mathematics and computational physics. His labs and teams contribute to projects like the Duke Materials Initiative and the ELSI collaboration for electronic structure theory.

Jean-Jacques Forneronمشاهده پروفایل

استادیار

• Econometrics
• Macroeconometrics
• Industrial Organization
• +۳ مورد دیگر

Jean-Jacques Forneron is an Assistant Professor of Economics at Boston University, specializing in econometrics and computational methods. He holds a PhD from Columbia University (2018), an MA in Economics and Statistics from ENSAE Paris (2012), and an MSc in Management from HEC Paris (2012). His research focuses on advancing econometric theory, particularly in dynamic models, moment condition estimation, and stochastic optimization techniques. He explores topics like model misspecification, optimal transportation approaches, and robust estimation under noisy conditions. His recent work emphasizes methodological innovations in simulation-based estimation, including sieve estimators, scrambled methods of moments, and likelihood-free sampling. These contributions aim to address challenges in parameter identification, computational efficiency, and statistical inference under non-standard conditions. Forneron teaches advanced econometrics courses at both MA and PhD levels, including Advanced Econometrics 2 and Advanced Topics in Econometrics , often co-instructing these courses. His office is located in SSW 415D on Boston University's campus.

Anirban DasGuptaمشاهده پروفایل

استاد

• Mathematical Statistics
• Asymptotics
• Applied Probability
• +۳ مورد دیگر

Anirban DasGupta is a Professor of Statistics at Purdue University's Department of Statistics within the College of Science. He received his B.S. (1977), M.S. (1978), and Ph.D. (1984) degrees from the Indian Statistical Institute. His office is located in MATH 528, and he can be contacted at dasgupta@purdue.edu or by phone at 765-494-6033. His research focuses on mathematical statistics, asymptotics, applied probability, analytic number theory, probability inequalities, and connections between mathematical disciplines. Recent publications emphasize high-dimensional inference, distribution theory, and applications in statistical methodology. Professor DasGupta has received significant recognition including: Fellow of the Institute of Mathematical Statistics (1993) Top Ten Outstanding Teachers in College of Science (1987, 1997) Member of IMS Council (2008-09, 2014-16) He has advised numerous PhD students including Sanjib Basu, Saurabh Mukhopadhyay, and Mei-Mei Zen. He has held editorial positions for major statistical journals including Annals of Statistics, Bernoulli, and Journal of Statistical Planning and Inference.

Luis Carvalhoمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Computational Biology
• Computational Statistics
• +۳ مورد دیگر

Luis Carvalho is an Associate Professor in the Department of Mathematics and Statistics at Boston University. His research focuses on Bayesian inference, computational statistics, computational biology, network modeling, remote sensing, and transportation engineering. He develops statistical methods for high-dimensional discrete spaces and applies them to interdisciplinary problems in biology, environmental science, and urban systems. Expertise includes Bayesian statistical methods, computational biology, and statistical inference. His work bridges theoretical advancements with practical applications, such as analyzing environmental health data, urban crime patterns, and energy-efficient building design. He has contributed to bioinformatics tools like RAMZIS for glycoprotein analysis and developed methodologies for network-regularized regression and remote sensing data interpretation. Research Highlights Bayesian approaches for high-dimensional data analysis Integration of geospatial and health data to study asthma triggers Statistical modeling of urban energy systems and climate adaptation Computational methods for glycomics and proteomics His publications span computational statistics, environmental science, and bioinformatics, addressing both methodological innovation and real-world challenges. Collaborative projects include interdisciplinary efforts in climate mitigation, public health monitoring, and transportation network analysis.

Maryclare Griffinمشاهده پروفایل

استادیار

• Statistical Inference
• Time Series Analysis
• Variable Importance
• +۳ مورد دیگر

Maryclare Griffin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research develops statistical methods for complex data analysis, with emphasis on time series modeling, variable importance measures, Bayesian computation, and interpretable machine learning. Current methodological work includes adaptive overdifferencing for non-stationary processes, mixture representations for Bayesian simulation, and structured shrinkage priors for high-dimensional inference. Applications span clinical genomics, environmental monitoring, and biophysical trend analysis. Recent publications demonstrate innovations in time series model identification, optimization algorithms for regularized regression, and variable importance frameworks for nonlinear models. Collaborative work extends to metastasis prediction in melanoma and lightning strike pattern modeling. Holds a PhD in Statistics from the University of Washington and completed postdoctoral training at Cornell's Center for Applied Mathematics. Maintains active research in computational statistics and model interpretability.

• Machine Learning
• Statistics
• Probability Theory
• +۳ مورد دیگر

Dr. Steffen Grünewälder is a Senior Lecturer in Statistics at the University of York, Department of Statistics. Previously, he held roles as Senior Lecturer at Newcastle University (2022-2024) and Lecturer at Lancaster University (2014-2022). His research focuses on statistical learning theory, kernel methods, and reinforcement learning. He has contributed to advancements in Gaussian processes, Markov processes, and fairness in machine learning. His work spans theoretical developments and practical applications, including compressed empirical measures, stochastic processes, and algorithmic fairness. Key research interests include Bayesian methods, computational statistics, and optimization techniques in machine learning. Dr. Grünewälder’s recent publications explore topics such as deep Gaussian processes, Markov chain analysis, and reinforcement learning algorithms. He has collaborated on interdisciplinary projects, including ecological studies on animal behavior. He can be contacted via email at steffen.grunewalder@york.ac.uk .

David Eckmanمشاهده پروفایل

استادیار

• Stochastic Simulation
• Operations Research
• Decision-Making Under Uncertainty
• +۳ مورد دیگر

Dr. David Eckman is an Assistant Professor in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering at Texas A&M University. He previously served as a postdoctoral fellow in the Department of Industrial Engineering & Management Sciences at Northwestern University, under Professors Matthew Plumlee and Barry Nelson. He holds a Ph.D. in Operations Research from Cornell University (2019) and a B.S. in Industrial Engineering from the University of Pittsburgh (2014). His research focuses on stochastic simulation for decision-making under uncertainty, including ranking-and-selection procedures, simulation-optimization algorithms, and simulation output analysis. His work emphasizes developing methods to enhance efficiency and accuracy in simulation-based decision processes. Recent publications highlight advancements in simulation-optimization, such as data farming techniques, budget allocation strategies, and clustering methods for simulation outputs. His contributions span theoretical frameworks and practical tools, including the SimOpt testbed for algorithmic experimentation. No scientific awards or grants are explicitly listed in the provided texts. Dr. Eckman’s professional activities include maintaining a research website and an active Google Scholar profile, reflecting his commitment to disseminating knowledge in computational and operations research domains.