جست‌وجوی استاد و پژوهشگر

Alan Heavensمشاهده پروفایل

استاد

• Cosmology
• Astrophysics
• Dark Matter
• +۴ مورد دیگر

Professor Alan Heavens is a cosmologist and astrophysicist at Imperial College London's Department of Physics, part of the Faculty of Natural Sciences. His primary research focuses on dark matter, dark energy, gravitational lensing, and cosmological models. He has contributed to projects like the Euclid mission and co-led the CosmoVerse initiative addressing observational tensions in cosmology. He has also engaged in interdisciplinary work, including studies on medical treatments for severe cases of SARS-CoV-2 infections through the RECOVERY trials. Heavens' cosmological research explores fundamental physics questions, such as testing Einstein's gravity theory and analyzing the Standard Cosmological Model's limitations. His techniques include Bayesian inference, data compression, and machine learning applied to cosmic surveys. Notable collaborations include work on weak lensing, cosmic shear, and parameter inference from galaxy surveys. His publications span cosmology, astrophysics, and medical research, reflecting his dual engagement in theoretical physics and applied clinical studies. He has advised on large-scale observational projects and contributed to international initiatives like the Snowmass 2021 process. No specific awards or grants are detailed in the provided texts, though his active research profile suggests significant contributions to the field.

Boris Jean Eudes Berangerمشاهده پروفایل

مدرس

• Extreme Value Theory
• Environmental Extremes
• Big Data
• +۳ مورد دیگر

Boris Jean Eudes Beranger is a Lecturer in Data Science at the School of Mathematics and Statistics, UNSW Sydney, and an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). He holds a PhD in Statistics from Université Pierre and Marie Curie (Paris 6) and UNSW Sydney (2016), and an MSc in Mathematics from Université Pierre and Marie Curie (2011). His research focuses on Extreme Value Theory applied to environmental extremes (e.g., floods, heatwaves), Symbolic Data Analysis for complex datasets, and Statistical Theory for big data challenges. He has developed novel methods for analyzing histogram-valued data and max-stable processes in high dimensions, with applications in climate science and telecommunications. Key publications include work on tail density estimation using kernel methods, composite likelihood approaches for spatial extremes, and likelihood-based inference for aggregated data. His work emphasizes computational efficiency and robustness in extreme event modeling. Awards: J.B. Douglas Award (2014), Outreach Participation Award (2019) Grants: ACEMS Research Support Scheme (2018–2021), multiple industry collaborations Teaching: Courses include Data Science fundamentals, regression analysis, and extreme value theory Labs/Teams: UNSW Data Science Hub (uDASH), leading the UNSW Statistics Seminar Series

Michele Peruzziمشاهده پروفایل

استادیار

• Spatial Statistics
• Bayesian Methods
• Environmental Health
• +۴ مورد دیگر

Overview Michele Peruzzi is an Assistant Professor of Biostatistics at the University of Michigan–Ann Arbor's School of Public Health. His research focuses on Bayesian methods for spatial and multivariate data, particularly in environmental health applications, high-resolution sensor data, and scalable statistical computing. He develops software tools like the meshed and spamtree R packages to address computational challenges in large-scale analyses. Education PhD in Statistics, Università Bocconi (2019) MSc in Economic and Social Sciences, Università Bocconi (2013) BSc in Economic and Social Sciences, Università Bocconi (2010) Research Focus Peruzzi's work addresses three core areas: (1) scalable Bayesian geostatistical methods for environmental sensors (e.g., satellite imaging, air quality), (2) response surface modeling for large health datasets, and (3) climate change impacts on ecosystems and human health. His methods emphasize computational efficiency and reproducibility. Key Contributions Developed meshed Gaussian processes for partitioned domain analysis Pioneered spatial multivariate trees for big data regression Advanced phenology modeling via satellite data for continental-scale vegetation analysis Affiliations Based in Ann Arbor, MI, Peruzzi holds office in SPH II M4531. His work intersects statistics, environmental science, and public health, with applications in climate change mitigation and precision medicine.

Olga Kloppمشاهده پروفایل

استاد

• Nonparametric Estimation
• High-Dimensional Inference and Sparsity
• Network Models
• +۳ مورد دیگر

Olga Klopp is a Professor of Statistics at ESSEC Business School and a member of the CREST Statistics Department. Her research focuses on nonparametric estimation, high-dimensional inference and sparsity, network models, and matrix completion. She has made significant contributions to statistical theory in low-rank modeling and missing data problems, with applications to networks, epidemiology, and machine learning. Nonparametric Estimation and High-Dimensional Inference : Key areas in her work, including theoretical guarantees and adaptive methods. Network Models and Graphons : Addressing dynamic networks, change-point detection, and graphon games with missing links. Matrix Completion : Pioneering work on robust and collective matrix completion with low-rank constraints. Her recent publications highlight advancements in tensor decomposition, topic modeling via projections, and sparse network estimation. She advises PhD students in statistics and network analysis.

• High-energy nuclear physics
• Quantum Chromodynamics (QCD)
• Quark-Gluon Plasma (QGP)
• +۵ مورد دیگر

Bjoern Schenke is a Distinguished Scientist in the Physics Department at Brookhaven National Laboratory since 2024. He also holds an Adjunct Professor position at Stony Brook University since 2019, contributing to academia through teaching and mentorship. His research focuses on theoretical nuclear physics under extreme conditions, particularly the behavior of quark-gluon plasma and gluon saturation phenomena. Education: Ph.D. in Physics (summa cum laude) from Goethe University Frankfurt (2008), advised by Prof. Dr. Carsten Greiner Dr. Schenke's research spans high-energy nuclear theory , with emphasis on: Relativistic viscous hydrodynamic simulations of heavy-ion collisions Color-Glass Condensate effective theory QCD-based modeling of multi-particle final states Development of computational frameworks like MUSIC and IP-Glasma His recent publications (2023-2025) demonstrate multiscale modeling across diverse domains: Electron-Ion Collider applications (8 papers) Quark-Gluon Plasma characterization (6 papers) Bayesian inference techniques (5 papers) Jet quenching and substructure analysis (7 papers) Isobar collision studies (4 papers) Four-dimensional QCD equations of state (3 papers) Scientific recognition includes: APS Fellowship (2022) BNL Science & Technology Award (2023) IUPAP Young Scientist Prize (2013) Zimanyi Medal in Nuclear Theory (2017) He contributes to major initiatives like the JETSCAPE collaboration and has developed influential models including IP-Glasma for initial state description in heavy-ion collisions. His work bridges theoretical nuclear physics with experimental observables at facilities like RHIC, LHC, and future EIC.

Lenny Smithمشاهده پروفایل

استاد

• Climate Science
• Statistical Modeling
• Chaos Theory
• +۲ مورد دیگر

Lenny Smith is a distinguished Professor of Statistics at the London School of Economics and Political Science (LSE) , where he also directs the Centre for the Analysis of Time Series (CATS) . Additionally, he serves as a Senior Research Fellow at Pembroke College, Oxford . With a PhD in Physics from Columbia University (1987), his career spans prestigious institutions including École Normale Supérieure, Warwick, and Potsdam University. Academic Roles: LSE Professor of Statistics, CATS Director, Pembroke College Senior Research Fellow Education: PhD in Physics (Columbia University, 1987); Undergraduate in Physics, Mathematics, and Computer Science (University of Florida) Research Focus: Climate modeling, ensemble forecasting, uncertainty quantification, chaos theory, and applications to disaster risk reduction Grants: Funded by ONR, NOAA, EPSRC, NERC, European Commission, and UK Research Councils Key Projects: NAPSTER (NERC), DIME and REMIND (EPSRC), THORPEX strategic planning Scientific Recognition: Royal Meteorological Society Fitzroy Prize (2003), Selby Fellowship (Australian Academy of Sciences) Students Supervised: Roman Frigg, David Stainforth, Emma Suckling, Falk Niehörster, H. Du, T. Maynard, S. Higgins, A. Jarman Media Presence: Quoted in Nature, New Scientist, BBC, Financial Times, and The Daily Telegraph Smith's work bridges rigorous mathematical analysis with practical applications, notably in climate change economics and weather risk management . His research on ensemble forecasting and probabilistic skill has shaped methodologies for evaluating climate models. He actively contributes to public science through his book A Very Short Introduction to Chaos and media appearances, emphasizing the importance of scientific uncertainty in policy decisions. His publications reveal a consistent focus on dynamical coherence , model error analysis , and weather derivatives , with recent work exploring multi-model cross-pollination and predictability limits . Smith's interdisciplinary approach combines nonlinear dynamics , statistical physics , and hydrological modeling to address real-world climate challenges.

Venkat Chandrasekaranمشاهده پروفایل

استاد

• Optimization
• Information Sciences
• Convex Optimization
• +۵ مورد دیگر

Venkat Chandrasekaran is the Kiyo and Eiko Tomiyasu Professor at the Computing and Mathematical Sciences and Electrical Engineering divisions within the Division of Engineering and Applied Science at the California Institute of Technology . B.A., Rice University, 2005 B.S., Rice University, 2005 M.S., Massachusetts Institute of Technology, 2007 Ph.D., Massachusetts Institute of Technology, 2011 As an applied mathematician, Chandrasekaran's work focuses on optimization and the information sciences . His group develops mathematical foundations for applications in science and engineering, spanning convex optimization , statistical inference , inverse problems , graphs and combinatorial optimization , and applied algebra and geometry . His publications from 2024–2004 demonstrate expertise in mathematical optimization , statistical modeling , and graphical representation . Themes include convex geometry , high-dimensional data analysis , and environmental systems modeling , with applications in hydrology , machine learning , and signal processing . INFORMS Optimization Society Prize Sloan Research Fellow AFOSR Young Investigator Award NSF CAREER Award Okawa Research Grant Young Researcher Prize Chandrasekaran has advised students and postdocs including Armeen Taeb (University of Washington), Yong Sheng Soh (National University of Singapore), and Kevin Shu (Caltech Postdoc). His courses include Mathematical Optimization, Mathematics of Electrical Engineering, and Great Ideas in Data Science.

• Biostatistics
• Neuroimaging
• Functional Connectivity
• +۷ مورد دیگر

Dustin Pluta is an Assistant Professor in the Department of Biostatistics, Data Science, & Epidemiology at the School of Public Health, Augusta University. His academic work bridges biostatistics, neuroimaging, and data science, with a focus on developing advanced statistical methods for neuroscience and public health applications. Education: Ph.D. in Statistics, University of California, Irvine, 2020 M.S. in Applied Statistics, California State University, Long Beach, 2015 M.A. in Mathematics, University of California, Davis, 2008 His research interests lie at the intersection of biostatistics , functional brain connectivity , Bayesian modeling , and imaging genetics . He develops and applies statistical techniques to improve the quality and interpretability of neuroimaging data, particularly in the context of mental health and addiction behaviors. His work often involves Gaussian processes, penalized regression, and multivariate association testing. The recent publications reflect a strong trend in neuroimaging statistics , with emphasis on enhancing trial-level ERP analysis, modeling dynamic functional connectivity, and integrating genetic and brain imaging data. These works span disciplines including neuroscience, psychiatry, and biostatistics, demonstrating methodological innovation and application to real-world health problems such as e-cigarette use vulnerability. Scientific Awards and Honors: Distinguished Student Paper Competition Award, ENAR, International Biometric Society, 2018 Student Paper Competition Award, Joint Statistical Meetings, Statistics in Imaging, 2018 Robert L. Newcomb Memorial Endowed Graduate Student Award, UC Irvine, 2016 Summer Fellowship, UC Irvine, 2016 Chair’s Award, UC Irvine Department of Statistics, 2015 Dustin Pluta is actively involved in academic service, including serving as Committee Chair of the Journal Club and participating in faculty hiring and departmental meetings. He is also a reviewer for the journal Psychophysiology . He teaches graduate courses such as Applied Linear Models , Modern Methods of Multivariate Analysis , and the Data Science Capstone . While specific grant funding is not listed, his research output suggests active engagement in sponsored scholarly work. He contributes to a collaborative research environment within the Department of Biostatistics, Data Science, & Epidemiology, where interdisciplinary projects involving neuroscience, public health, and data science are central to the academic mission.

Eugene Katsevichمشاهده پروفایل

استادیار

• Genetics
• Genomics
• High-dimensional Statistics
• +۳ مورد دیگر

Eugene Katsevich is an Assistant Professor at the University of Pennsylvania's Wharton Department of Statistics and Data Science. His research spans statistical theory, methodology, and applications in genomics and computational biology. Education: Ph.D. in Statistics (Stanford, 2019), A.B. in Mathematics (Princeton, 2014) His work focuses on: High-dimensional variable selection and multiple testing Conditional independence testing and computational efficiency Applications to genetics and genomics Development of statistical software for biological discovery Recent publications address: Robust differential expression analysis in single-cell CRISPR screens Theoretical advances in false discovery rate control Multi-resolution causal variant localization CRISPRi-based enhancer-gene mapping Scientific awards include: Wharton Undergraduate Teaching Excellence Award (2021) He teaches courses in modern data mining (STAT4710/5710) and statistical methodology (STAT9610), and leads the Katsevich Lab at UPenn, funded by NSF and Analytics at Wharton. His research develops methods like KnockoffZoom and SCEPTRE for genome-wide analysis and single-cell CRISPR data.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Stochastic Differential Equations
• +۳ مورد دیگر

Professor Han Cheng Lie is a faculty member at the Institut für Mathematik , Universität Potsdam , specialising in Uncertainty Quantification . His research focuses on Bayesian inference, inverse problems, and probabilistic numerical methods. Contact: han.lie@uni-potsdam.de Location: Campus Golm, Haus 9, Karl-Liebknecht-Str. 24-25, D-14476 Potsdam OT Golm, Germany His work addresses mathematical challenges in Bayesian inverse problems , including stability analysis of posteriors, low-rank approximations for Gaussian posteriors, and randomized algorithms for differential equations. He has contributed to Markov chain Monte Carlo on manifolds and transition path theory for diffusion processes. Recent publications highlight advances in dimension-independent sampling , probabilistic integrators , and model error mitigation in Bayesian frameworks. His methodological innovations span applications in computational statistics , molecular dynamics , and geophysical fluid dynamics . He organizes academic events like the Potsdam Data Assimilation Days and has presented at international workshops including Oberwolfach and SIAM conferences .

Galin Jonesمشاهده پروفایل

استاد

• Markov Chain Monte Carlo
• Bayesian Statistics
• Computational Statistics
• +۳ مورد دیگر

Galin Jones is a Professor and Director of the Statistics program at the University of Minnesota Twin Cities. His work bridges computational statistics and astrophysics, with a focus on Markov Chain Monte Carlo (MCMC) methods, Bayesian inference, and multi-messenger astrophysics. Key research areas: Markov Chain Monte Carlo, Bayesian statistics, convergence analysis, and astrophysical data modeling Recent publications highlight methodological advancements in MCMC convergence rates, Bayesian variable selection for autoregressive models, and applications to supernova studies and multi-messenger astrophysics. His 2024–2025 articles emphasize computational efficiency and theoretical frameworks for statistical algorithms. Current grants include active NSF funding (2019–2025 and 2022–2025) for multi-messenger astrophysics research training and tractable inference methods. Previous NSF and NIH projects focused on MCMC output analysis and large-scale statistical challenges. NRT-WOU: Multi-Messenger Astrophysics (NSF, 2019–2025) Tractable Inference for Astrophysics (NSF, 2022–2025) Methodological Frameworks (NSF, 2013–2016) Collaborations span institutions like Johns Hopkins University and the National Institutes of Health, addressing statistical methodologies for astrophysical and biomedical research.

• Computational Astrophysics
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Joshua S. Speagle is an Assistant Professor at the University of Toronto , specializing in computational astrophysics, Bayesian statistics, and machine learning applications to astronomical problems. His work bridges theoretical modeling with practical software development, notably as the creator of the dynesty package for dynamic nested sampling in Bayesian inference. Research Interests : Computational astrophysics and statistical methods Bayesian inference and dynamic nested sampling Stellar evolution and galaxy formation Machine learning for astronomical data analysis Recent Publications (2025–2024) focus on generative models for inverse problems, galaxy formation at high redshift, stellar oscillation analysis, and simulation-based inference techniques. His software tools like dynesty and brutus are widely used in the astronomical community for Bayesian posterior and evidence estimation. Contact: j.speagle@utoronto.ca | Personal Website | ORCID: 0000-0003-2573-9832

Gracia Yunruo Dongمشاهده پروفایل

استادیار

• Healthcare Equity
• Population Health
• Computational Statistics
• +۷ مورد دیگر

Gracia Yunruo Dong is an Assistant Professor in the Teaching Stream at the University of Toronto, affiliated with the Department of Cell & Systems Biology and the Department of Statistical Sciences. She also collaborates with Island Health on healthcare equity research. PhD, MMath, and BMath in Statistics from the University of Waterloo CANSSI Distinguished Postdoctoral Fellow (2022–2024) at University of Toronto and University of Victoria Her research spans Computational Statistics , Healthcare Equity , Quasi-Monte Carlo Methods , and Population Health , with a focus on marginalized populations. She develops capture-recapture models for homeless populations, applies case-crossover designs to environmental health, and explores quasi-random number generation for high-dimensional integration. Her publications reflect interdisciplinary collaboration, combining Bioinformatics , Public Health , and Statistical Computing . Recent work addresses gaps in healthcare access, overdose prediction, and simulation experiments. CANSSI Distinguished Postdoctoral Fellowship NSERC funding for SMMEID network Dong teaches courses such as Epidemiology of Health & Disease and The Practice of Statistics I , integrating real-world applications into her instruction. She collaborates with researchers like Patrick Brown and Laura Cowen, applying statistical methods to ecological and human health challenges. Her affiliations with Vancouver Island Health Authority’s Applied Health Data Analytics team highlight her commitment to addressing health disparities through data-driven solutions.

Alexander Hägeleمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Artificial Intelligence
• +۱ مورد دیگر

Alexander Hägele is a researcher at the Laboratoire d'apprentissage automatique et d'optimisation (Machine Learning and Optimization Laboratory, MLO) within the College of Computer and Communication Sciences at École polytechnique fédérale de Lausanne (EPFL). His work focuses on theoretical and practical aspects of machine learning optimization.

Liuمشاهده پروفایل

استادیار

• Statistical Modeling
• Psychological Data Analysis
• Behavioral Data Analysis
• +۵ مورد دیگر

Dr. Liu is an Assistant Professor in the Department of Psychological Sciences within the School of Social Sciences, Humanities and Arts at the University of California Merced. She earned her doctoral degree in Quantitative Psychology from the University of Notre Dame in 2018 and holds additional degrees in Applied Mathematics. Dr. Liu's research focuses on statistical modeling of psychological and behavioral data, with particular expertise in developing and applying modern statistical approaches for analyzing complex datasets. Her work addresses challenges in high-dimensional data, longitudinal/time series analysis, and categorical data. A significant portion of her recent research involves structural equation modeling of social networks, creating bridges between human behaviors and network structures. She also conducts methodological research on Bayesian inference in structural equation modeling and investigates issues related to dishonest responses in survey data, study design, and power analysis. At the core of her research is the application of advanced statistics to achieve a more proper understanding of human behaviors. Dr. Liu's publication record demonstrates expertise in mathematical and statistical approaches, with recent work spanning from 2018 to 2024. Her research interests encompass statistical methodology development with applications to psychological and behavioral phenomena, particularly focusing on innovative approaches to handle messy real-world data. Dr. Liu's work represents an important contribution to the methodological foundation of psychological research, providing researchers with advanced tools to analyze complex behavioral data and extract meaningful insights about human cognition and behavior.