جست‌وجوی استاد و پژوهشگر

Carl Schmertmannمشاهده پروفایل

استاد

• Demography
• Population Health
• Fertility Forecasting
• +۴ مورد دیگر

Carl Schmertmann is Professor of Economics at Florida State University and Research Associate at FSU's Center for Demography and Population Health. His academic career spans multiple institutions and research domains within demography. His educational background includes a B.S. in Economics from the University of Wisconsin and a Ph.D. in Economics from the University of California, Berkeley. Schmertmann's research focuses on developing innovative methods for demographic analysis, particularly in forecasting cohort fertility and childlessness, and estimating demographic rates in small areas with sparse data. His work bridges economics, statistics, and population studies, emphasizing methodological rigor and practical applications for understanding population dynamics. His publication record demonstrates consistent contributions to demographic methodology, with recent work focusing on data quality in mortality estimation, spline-based demographic modeling, and addressing biases in cohort profile construction. His research spans both theoretical methodological development and applied demographic analysis across various geographic contexts. As former Editor of Demographic Research, Schmertmann has significantly influenced the field's scholarly discourse. His collaborative work extends across institutions including Universidade Federal de Minas Gerais, University of California, Berkeley, University of Texas, and the Max Planck Institute for Demographic Research. Schmertmann maintains an active research program through the Center for Demography and Population Health at Florida State University, where he continues to advance methodological approaches to demographic analysis and contribute to understanding population dynamics in diverse contexts.

Rolf Larssonمشاهده پروفایل

استاد

• Time Series Analysis
• Econometrics
• Statistical Modeling
• +۵ مورد دیگر

Rolf Larsson is a Professor at the Department of Mathematics at Uppsala University, Sweden. His academic career spans theoretical statistics, econometrics, and applied statistical analysis across multiple disciplines. His primary research interests include: Time Series Analysis and Econometrics Statistical Inference and Methodology Panel Data Analysis Climate Data Modeling Bayesian and Frequentist Statistics Multivariate Statistical Methods Discrete Data Analysis Professor Larsson's publication record demonstrates exceptional breadth across statistical theory and applications. His work on autoregressive models, unit root testing, and confidence distributions represents significant theoretical contributions to statistics. He has also made important applied contributions in climate science through his analysis of ice core data examining relationships between temperature, CO2, and methane. His methodological innovations span from discrete factor analysis to shrinkage estimators in beta regression models, with applications in econometrics, educational data, and medical research. His recent work on confidence distributions for autoregressive parameters (2024) continues his long-standing interest in foundational statistical theory. His collaborative work extends across disciplines, including medical statistics research examining the relationship between serotonergic medication, sunshine, and suicide. His publication record shows consistent high-quality output across top statistical and econometric journals over multiple decades.

Patrick Flahertyمشاهده پروفایل

دانشیار مدعو

• Bioinformatics
• Genomics
• Machine Learning
• +۳ مورد دیگر

Patrick Flaherty is an Adjunct Associate Professor at the University of Massachusetts Amherst. His research focuses on interdisciplinary approaches in bioinformatics, genomics, and computational biology, with a strong emphasis on developing novel algorithms for genomic data analysis, predictive modeling in clinical medicine, and systems biology applications. His work integrates statistical methods, machine learning, and experimental design to address challenges in genetic modulation, biomarker discovery, and disease prediction. Key research interests include: Development of advanced computational tools for analyzing genomic and transcriptomic data Machine learning models for hierarchical clustering and dimensionality reduction Prediction of post-trauma complications and infectious diseases using multi-biomarker approaches Statistical methods for hypothesis testing and experimental design Recent publications highlight contributions to biomarker prediction, matrix factorization techniques for bounded data, and Bayesian models for cancer genomics. His work bridges computational innovation with practical clinical and biological applications. Advising and grants: No explicit details provided in the text. Labs/teams: No specific lab or team affiliations mentioned.

Adam Johansenمشاهده پروفایل

استاد

• Monte Carlo methodology
• Sequential Monte Carlo algorithms
• Bayesian statistics
• +۳ مورد دیگر

Adam Johansen is a Professor of Statistics at the University of Warwick, specializing in computational statistics, sequential Monte Carlo algorithms, Bayesian methodology, and gradient flows. His research spans theoretical advancements and practical applications in simulation-based inference. Current projects: OCEAN (ERC Synergy Grant for next-gen ML algorithms), CoSinES, and APTS (co-director until 2023) Research interests focus on Monte Carlo methodology , Bayesian statistics , and sequential inference . Recent work explores scalability in decentralized systems, gradient flows for sampling, and robust algorithmic frameworks. His 15 most recent publications emphasize particle methods, convergence analysis, and mathematical statistics. He supervises a research group with PhD students including Rocco Caprio, Shu Huang, and Usman Ladan, while contributing to editorial roles at the Journal of the Royal Statistical Society: Series B and London Mathematical Society Newsletter . He developed the SMCTC and RcppSMC libraries for sequential Monte Carlo applications.

Jim E. Griffinمشاهده پروفایل

استاد

• Bayesian Nonparametric Methods
• Regression Modelling with High-Dimensional Data
• Time Series Modelling in Econometrics and Finance
• +۴ مورد دیگر

Jim E. Griffin is a Professor of Statistical Science at University College London's Department of Statistical Science. His research focuses on Bayesian Nonparametric Methods, Regression Modelling with High-Dimensional Data, and Time Series Modelling in Econometrics and Finance. He leads multiple collaborative projects including Bayesian Modelling of Macroeconomic Data (with Maria Kalli), Athletic Performance Passport for anti-doping detection (with James Hopker), and Efficient Computational Methods for Bayesian Variable Selection (with Sam Livingstone and Xitong Liang). Key research areas include applications in genomics, environmental science (via eDNA modelling with Eleni Matechou), and computational methods for model uncertainty visualization. Preprints and code for projects like Cartesian credible sets and tensor vector autoregression models are available via arXiv.org and GitHub repositories maintained by collaborators. His work bridges theoretical advancements in Bayesian statistics with practical applications in finance, ecology, and sports science. Collaborations span disciplines, with notable contributions to statistical methodologies for high-dimensional problems and model interpretability frameworks.

• Machine Learning
• Statistics
• Bayesian Methods
• +۴ مورد دیگر

Tamara Broderick is an Associate Professor with tenure at MIT, affiliated with the Electrical Engineering and Computer Science (EECS) Department, the Laboratory for Information and Decision Systems (LIDS), and the Institute for Data, Systems, and Society (IDSS). Her research focuses on machine learning and statistics, particularly Bayesian methods, uncertainty quantification, and probabilistic modeling. She completed her PhD at UC Berkeley and has held significant roles in advancing scalable Bayesian inference techniques. Her work includes developing the Nomon assistive technology for single-switch users, showcased in projects like the Harlem Shake video explaining MCMC methods. She teaches advanced courses such as 6.7830 Bayesian Modeling and Inference in Spring 2025. Broderick advises numerous PhD students and postdocs, mentoring in EECS and related MIT programs. Her contributions span theoretical advancements, practical algorithms, and impactful applications in healthcare and accessibility.

Xiao Wangمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Nonparametric Regression
• +۸ مورد دیگر

Xiao Wang is the Department Head and J.O. Berger and M.E. Bock Professor of Statistics at Purdue University's Department of Statistics. His research focuses on machine learning, nonparametric methods, and statistical inference with applications in engineering and physical sciences. He has advised numerous PhD students and holds multiple awards, including ASA and IMS fellowships. Education: B.S. and M.S. from University of Science and Technology of China (1997–2000), Ph.D. in Statistics from University of Michigan (2005). Research interests include deep learning, functional data analysis, and reliability theory. Recent work emphasizes multimodal sampling, generative adversarial networks, and high-dimensional regression. Key awards: ASA/IMS Fellowships (2021), Purdue Professional Achievement Award, and William J. Studden Publication Award (2023). Active roles include associate editor of Technometrics and course instructor for advanced statistical theory courses. Advised students have contributed to areas like robust adversarial learning, manifold approximation, and Bayesian neural networks. Current lab members include researchers in machine learning and computational statistics.

Zdravko Botevمشاهده پروفایل

مدرس

• Monte Carlo Methods
• Network Reliability
• Kernel Density Estimation
• +۳ مورد دیگر

Dr. Zdravko Botev is a Lecturer in Computational Statistics and Applied Probability at the School of Mathematics and Statistics, UNSW Sydney. He holds a PhD from the University of Queensland (2010) and has held postdoctoral fellowships at the Universities of Montreal and Cambridge. His research focuses on adaptive Monte Carlo methods, rare-event probability estimation, network reliability modeling, kernel density estimation, and model selection. He is the author of widely used MATLAB scripts for nonparametric density estimation and has contributed to influential texts like Data Science and Machine Learning: Mathematical and Statistical Methods . Education: PhD in Statistics, University of Queensland, Australia, 2010 Research Interests: Dr. Botev's work emphasizes computational statistics and rare-event simulation, with applications in network reliability and kernel density estimation. His methods address challenges in high-dimensional data analysis, Monte Carlo algorithms, and efficient statistical computation. Key areas include: - Adaptive Monte Carlo methods for rare-event simulation - Kernel density estimation techniques and boundary conditions - Variance reduction strategies in statistical modeling Professional Engagement: Member of the Australian Academy of Sciences Christopher Heyde Medal Awards Committee (2021–2023) Steering Committee member for the Monte Carlo Methods Conference Associate Editor for INFORMS Journal of Computing and Australian & New Zealand Journal of Statistics Chair of the organizing committee for the 12th International Conference on Monte Carlo Methods and Applications (2019) Teaching: Dr. Botev has taught courses such as Data Mining (MATH5836), Geotechnical Data Collection and Analysis (MINE8680), and Probability and Statistics (MATH2901/MATH2931). His teaching has received positive student evaluations for clarity and engagement.

Jerome Keatingمشاهده پروفایل

استاد

• Statistical Estimation
• Pitman Nearness Criterion
• Reliability Theory
• +۳ مورد دیگر

Jerome Keating is the Peter T. Flawn Professor of Management Science and Statistics at the Carlos Alvarez College of Business, UTSA. He holds a Ph.D. in Mathematical Sciences from the University of Texas at Arlington, alongside M.A. and B.S. degrees in Mathematics from the same institution. His roles include Undergraduate Advisor of Record and former department chair (Management Science and Statistics, Demography). He has extensive experience in academia, industry (Bell Helicopter Textron, Los Alamos National Laboratory), and editorial roles. **Education**: Ph.D. in Mathematical Sciences, University of Texas at Arlington M.A. in Mathematics, University of Texas at Arlington B.S. in Mathematics, University of Texas at Arlington His research focuses on statistical estimation criteria (e.g., Pitman nearness), reliability theory, and environmental science applications. He has published over 50 peer-reviewed articles, two books, and contributed to projects like NASA’s Magnetospheric Multiscale mission and leak detection systems for underground tanks. **Awards**: President’s Distinguished Achievement Award for Teaching Excellence (1989, 2001) Chancellor’s Council Teaching Award (1995, 2001) Fellow of the American Statistical Association (1997) Don B. Owen Award in Statistics (2006) **Consulting & Grants**: Collaborated with NASA, Southwest Research Institute, Valero, and Mobil Research on reliability analysis and statistical applications. He co-advised two doctoral dissertations and served on numerous committees. His work spans editorial roles for journals like *STATS* and *Communications in Statistics*.

Andrew Caseyمشاهده پروفایل

دانشیار

• Astrophysics
• Stellar Astrophysics
• Data Analysis
• +۳ مورد دیگر

Associate Professor Andrew Casey is a computational astrophysicist at Monash University's School of Physics and Astronomy, specializing in data-driven methods applied to astrophysical problems. He holds a PhD from Australian National University (ANU), with postdoctoral research at the University of Cambridge as a King's College Senior Member. His work bridges astrophysics, statistics, and machine learning, focusing on stellar astrophysics, Bayesian inference, and large-scale surveys like Gaia and GALAH. He leads projects on stellar interiors, galactic evolution, and unresolved binary systems. Casey has received awards including the Young Tall Poppy Award and the Australian Prime Minister's Endeavour Award. He actively mentors PhD students in data analysis and computational methods. Education: PhD in Astronomy & Astrophysics (ANU), Visiting Graduate Student at MIT (via Endeavour Award). Research interests include: computational data analysis, stellar astrophysics, Bayesian statistics, machine learning applications in astronomy, and algorithm optimization. Notable projects include GaiaUnlimited (studying Milky Way disc structure) and GALAH surveys (chemical tagging of stars). Recent articles focus on topics like Gaia data analysis, exoplanet detection via astrometry, and models for M dwarfs. His work often emphasizes interdisciplinary approaches, combining physics-based models with AI-driven techniques. Awards: Young Tall Poppy Award (2018), Fresh Science (2018), Senior Member (2015), Australian Prime Minister's Endeavour Award (2011), and InnovationACT Prize (2011). Advising and grants: Accepting PhD students; leads ARC-funded projects such as the Discovery Early Career Researcher Award. Projects include Gaia survey analysis, star formation studies, and binary star detection. Labs/Teams: Involved with the Monash Astrophysics Group and collaborates internationally on surveys like Gaia and SDSS-V.

Deborah Sulemمشاهده پروفایل

استادیار

• Data Science
• Statistics
• Machine Learning
• +۷ مورد دیگر

Deborah Sulem is an Assistant Professor (tenure-track) of Data Science in the Faculty of Informatics at the Università della Svizzera italiana (USI). She leads the Statistical Science Laboratory and is affiliated with the Institute of Computing (CI). Her research focuses on graphs/networks, Bayesian inference, high-dimensional statistics, explainability, and fairness in machine learning. Education: Ph.D. in Statistics, University of Oxford (2023), supervised by Judith Rousseau, Mihai Cucuringu, Xiaowen Dong, and Vincent Rivoirard Postdoctoral Researcher at Barcelona School of Economics and Universitat Pompeu Fabra (with David Rossell and Gabor Lugosi) Joined USI in 2024 Research Interests: Combines statistical theory with modern machine learning, emphasizing graph deep learning, point processes, and nonparametric methods. Her work addresses challenges in high-dimensional data analysis and ethical AI practices like model explainability and fairness. Grants & Labs: Leads the Statistical Science Laboratory Focuses on interdisciplinary projects involving dynamic networks and Bayesian model selection Contact: Email | Office: East Campus, Sector D, D5.18 (P5)

Paul Rathouzمشاهده پروفایل

استاد مهمان

• Biostatistics
• Epidemiology
• Developmental Psychopathology
• +۵ مورد دیگر

Paul Rathouz is a Visiting Professor and former Chair in the Department of Population Health at Dell Medical School, University of Texas at Austin. He serves as Director of the Biomedical Data Science Hub, fostering collaborative data science initiatives in clinical and population health research. With a Ph.D. in Biostatistics from Johns Hopkins University, Rathouz has extensive experience in academic leadership, previously serving as Chair of Biostatistics and Medical Informatics at the University of Wisconsin and holding roles at the University of Chicago. His research focuses on methodological advancements in statistical modeling, including missing data analysis, generalized linear models, and outcome-dependent sampling designs. Applied work spans developmental psychopathology, sleep epidemiology, and arsenic exposure studies. He has led high-impact projects like longitudinal studies of speech development in children with cerebral palsy and interventions to improve provider-patient communication in surgery. Rathouz holds fellow status in the American Statistical Association (2015) and received the Van Ryzin Award from ENAR (1997). His collaborative approach emphasizes full lifecycle research involvement, from project inception to publication, and he actively contributes to workforce development in research methods within Dell Med. Professional affiliations include the American Statistical Association and International Biometric Society. The Biomedical Data Science Hub under his direction leverages UT Austin’s interdisciplinary strengths to advance translational science, particularly in health services research and surgical outcomes optimization.

Philippe Rigolletمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Philippe Rigollet is the Cecil and Ida Green Distinguished Professor of Mathematics at the Massachusetts Institute of Technology, where he was appointed full professor in July 2020. He is also affiliated with MIT's Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). His academic journey spans positions at Princeton University (2008-2014) and Georgia Tech (2007-2008) before joining MIT in 2015. Rigollet's educational background includes a PhD in mathematical statistics from the University of Paris VI (now Sorbonne University) in 2006, an M.Sc. in Statistics & Actuarial Science from ISUP in 2003, and a B.Sc. in Applied Mathematics from the University of Paris VI in 2002, along with a B.Sc. in Statistics in 2001. His research spans statistics, machine learning, and optimization, with particular focus on high-dimensional problems, statistical limitations of learning under computational constraints, statistical optimal transport, and the mathematical foundations of transformer models. Rigollet approaches interdisciplinary problems at the intersection of computer science and statistics, bringing insights from each field to the other. His work has evolved from theoretical investigations of statistical versus computational trade-offs to practical applications in areas like cryoelectron microscopy and biological data analysis. Analysis of his recent publications reveals a strong trend toward developing mathematical frameworks for understanding modern AI architectures, particularly transformer models, through the lens of optimal transport and dynamical systems. His work connects deep learning theory with classical statistical methods, creating bridges between seemingly disparate mathematical domains. Notable scientific achievements: NSF CAREER Award (2011/2015) for Large Scale Stochastic Optimization and Statistics Elected Fellow of the Institute of Mathematical Statistics (2021) for contributions to statistical versus computational trade-offs, theory of aggregation, and statistical optimal transport Frank E. Perkins Award for Excellence in Graduate Advising (2023) Best Paper Award at Conference On Learning Theory (COLT) (2013) Invited Speaker at International Congress of Mathematicians (2026) Rigollet has advised numerous PhD students, many of whom have secured prestigious academic positions at institutions including Yale, NYU, Georgia Tech, and Duke. His research group at MIT includes current PhD students and postdoctoral researchers working on topics ranging from mathematical foundations of transformers to biological applications of optimal transport. He maintains active collaborations with the Broad Institute, particularly through the Eric and Wendy Schmidt Center, applying novel mathematical methods to genomic data analysis.

Julien Bectمشاهده پروفایل

دانشیار

• Bayesian Optimization
• Gaussian Processes
• Uncertainty Quantification
• +۴ مورد دیگر

Julien Bect is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His primary research focuses on Bayesian optimization, Gaussian processes, uncertainty quantification, and sequential design of experiments. He has collaborated extensively with researchers such as Emmanuel Vazquez and Paul Feliot, contributing to advancements in statistical modeling and computational methods for engineering and scientific applications. His work emphasizes the development of efficient algorithms for estimating probabilities of failure, excursion sets, and quantile-based inversion in complex systems. Notable contributions include methodologies for multi-fidelity computer experiments, Bayesian subset simulation, and adaptive experimental design. His research bridges theoretical foundations in statistics with practical applications in fields like reliability engineering, food safety, and electrical systems. Bect has published numerous articles in prestigious journals such as Technometrics , SIAM/ASA Journal on Uncertainty Quantification , and Bernoulli , showcasing his expertise in statistical inference and optimization. His recent work explores novel approaches in quantile set inversion and complex-valued frequency response modeling, reflecting his commitment to advancing methodologies for real-world stochastic systems.

JESUS MARIA RODRIGUEZمشاهده پروفایل

استاد

• Heart Rate Variability Analysis
• Neural Networks
• Pattern Recognition
• +۳ مورد دیگر

Jesus Maria Rodriguez Presedo is a Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (USC), affiliated with the Faculty of Physics and the Center for Research in Intelligent Technologies (CITIUS). He leads the GSI Group (Intelligent Systems Group) and focuses on biomedical signal processing, particularly in Heart Rate Variability (HRV) analysis, neural networks, and telemedicine solutions. His work integrates machine learning, stochastic modeling, and clinical applications to improve cardiac monitoring and diagnostic tools. Dr. Rodriguez earned his PhD from USC in 1994 with a thesis on intelligent ischemia monitoring, advised by Dr. Senén Barro and Dr. Ramón Ruiz Merino. His research emphasizes HRV analysis, biosignal interpretation, and software development (e.g., the RHRV package). He has contributed to telemonitoring systems for patients with chronic conditions like sleep apnea and COPD, leveraging multiagent systems and ubiquitous computing. His recent work explores nonlinear dynamics in time series, stochastic embeddings via variational autoencoders, and adaptive neural networks for multichannel signal analysis. Applications span cardiac event detection, ECG segmentation, and abductive frameworks for medical diagnostics.