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Erin SchliepView profile

Associate Professor

• Environmental Statistics
• Bayesian Statistics
• Spatial Statistics
• +3 more

Erin Schliep is an Associate Professor in the Department of Statistics at North Carolina State University (NCSU), with a joint appointment as a Research Associate at Duke University. She holds a Ph.D. in Statistics from Colorado State University (2013). Her research focuses on Bayesian statistics, spatiotemporal modeling, and environmental applications, including ecological dynamics and sports analytics. Her work addresses challenges in climate change impacts, species distribution modeling, and marine conservation. Key research areas include spatiotemporal analysis of ecological systems, data fusion methods for environmental monitoring, and causal inference in spatial contexts. She has contributed to studies on lake productivity responses to climate, whale calling patterns, and NBA player performance metrics. Recent projects include NSF-funded work on modeling and uncertainty quantification in life sciences. Her publications span high-impact journals like JASA, PNAS, and Ecology, addressing topics such as marine mammal abundance estimation, algal biomass changes in lakes, and spatial causal inference for marine protected areas. She advises students and collaborates widely, exemplified by her role in the ContLimno team analyzing freshwater ecosystems. Erin’s expertise extends to methodological advancements, including hierarchical modeling, Hawkes processes, and machine learning integration. She emphasizes interdisciplinary applications, bridging statistics with ecology, environmental science, and sports analytics.

Giuseppe VinciView profile

Assistant Professor

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +8 more

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +4 more

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Douglas AllaireView profile

Associate Professor

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +4 more

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Daniel KowalView profile

Associate Professor

• Bayesian Statistics
• Environmental Health and Policy
• Public Health
• +9 more

Daniel Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, effective July 2024. Previously, he served as the Dobelman Chair Assistant Professor at Rice University. His research focuses on Bayesian methodology for complex dependent data, including functional, time series, and spatial datasets, with applications in environmental health, epidemiology, finance, and astronomy. He develops scalable algorithms for high-dimensional data and interpretable uncertainty quantification. His work has been recognized with the Blackwell-Rosenbluth Award (2021) and ARO Young Investigator Award (2020). Education: Ph.D. in Statistics (Cornell University), M.S. in Statistics (Cornell University), B.A. in Mathematics (Washington University in St. Louis). Research interests include Bayesian models for prediction/inference, decision theory, discrete data analysis, and scalable approximations. Key areas of application: environmental health policy, wearable devices, economics, biomedical engineering, and astronomy. Recent grants include NSF-funded projects on adaptive dependent data models and Army Research Office initiatives on Bayesian approximations. He has supervised multiple Ph.D. students, including Yunan Gao, Thomas Sun, and Brian King. His software contributions include R packages like countSTAR, SeBR, and lmabc for Bayesian regression and data synthesis. Awards include Lindley Prize Honorable Mention (2024), Arnold Zellner Thesis Award (2018), and numerous student paper awards from ASA sections.

Sze Ming LeeView profile

Researcher

• Social Statistics
• Large-Scale Data Analysis
• Latent Variable Modelling
• +3 more

Sze Ming Lee is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on Social Statistics, with expertise in large-scale data analysis, latent variable modelling, survival analysis, and quantile regression. Lee holds a BSc in Mathematics, an MPhil in Risk Management Science, and a postgraduate diploma in Education, all from the Chinese University of Hong Kong. His academic supervisors are Dr. Yunxiao Chen and Professor Fiona Steele. His research interests emphasize methodological advancements in statistical modelling, particularly in handling high-dimensional data and latent variables. Recent publications (2023-2025) highlight contributions to quantile regression, factor analysis stability, and longitudinal data methodologies. Lee’s work bridges theoretical statistics with practical applications in social sciences and biostatistics. No scientific awards or grants are explicitly mentioned in the provided texts. He is affiliated with LSE’s Social Statistics research group and contributes to the department’s focus on data-driven social science research.

• Multivariate Survival Analysis
• Empirical Processes Theory
• Longitudinal Data Analysis
• +3 more

Changyong Feng, PhD, is a Professor of Biostatistics and Computational Biology, Anesthesiology, and Dentistry at the University of Rochester Medical Center. He serves as Co-Director of the Department of Dentistry and holds a faculty position in the Department of Biostatistics and Computational Biology. He earned his PhD in Statistics from the University of Rochester in 2002. Dr. Feng’s research focuses on multivariate survival analysis, empirical processes theory, longitudinal data analysis, and statistical methods in epidemiology and clinical trials. His work spans interdisciplinary collaborations, including studies on anticoagulant efficacy in cardiovascular surgery, microbiome data analysis, and behavioral predictors of oral health. He has contributed to clinical trials, anesthesiology practices, and veterinary pharmacology. His expertise includes developing statistical models for complex biomedical data. Dr. Feng is affiliated with multiple departments at URMC, reflecting his role in bridging statistical methodologies with clinical and translational research. His publications address diverse topics from drug dosing optimization to influenza antibody dynamics. His research underscores the application of advanced statistical techniques to real-world medical challenges.

Yu ChengView profile

Professor

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +6 more

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Jay GottfriedView profile

Professor

• Human Olfactory Neuroscience
• Cognitive Neuroscience
• Behavioral Neuroscience
• +3 more

Jay Gottfried is the Arthur H. Rubenstein University Professor at the University of Pennsylvania's Perelman School of Medicine. His research focuses on human olfactory neuroscience, investigating how odor perception is processed in limbic circuits related to emotion, learning, and memory. He employs advanced techniques like functional MRI (fMRI) and intracranial EEG to study olfactory systems, with recent work exploring olfactory navigation using grid-like neural representations and interactions between sensory cortices. Education: A.B. in Molecular Biology from Princeton University; M.D. and Ph.D. in Physiology and Neuroscience from NYU School of Medicine. Postdoctoral training in olfactory neuroscience at University College London. Research Interests include odor coding, oscillations in the piriform cortex, and translational studies linking olfactory dysfunction to neurodegenerative diseases. Notable projects involve odor categorization, fear learning, and sleep-dependent memory consolidation. His lab bridges human and rodent studies to elucidate olfactory mechanisms. Key findings include temporal segregation of nostril odor signals, grid-cell-like representations in olfactory navigation, and the role of piriform cortex in integrating sensory information. He collaborates on developing high-resolution imaging and EEG techniques to map olfactory circuits.

Bin PengView profile

Professor

• Econometrics
• Panel Data Analysis
• Time Series Analysis
• +3 more

Bin Peng is a Professor in the Department of Econometrics and Business Statistics at Monash University. His research focuses on developing novel econometric models and methods, particularly in panel data analysis, time series econometrics, and climate data modeling. He holds a PhD in Econometrics from Monash University (2013) under Professors Giovanni Forchini and Don Poskitt, preceded by a BSc in Mathematics from Nanjing University (2007). His work addresses structural changes in factor models, time-varying parameters in vector error-correction frameworks, and productivity convergence in manufacturing sectors. Key contributions include nonparametric panel models for climate data and methodologies for handling interactive effects in panel data with general factors. Peng has received multiple Dean’s Awards, including the 2021 Early Career Research Excellence Award, 2023 Commendation for Excellence, and 2024 Researcher of the Year. He leads a 2021–2025 project on modeling time trends in panel data, funded by Monash University. His recent articles (2021–2025) emphasize methodological advancements in econometric theory, applied to climate science, economic growth, and macroeconomic policy.

Ingrid Hobæk HaffView profile

Associate Professor

• Multivariate statistics
• Copulae
• Skew distributions
• +3 more

Ingrid Hobæk Haff is an Associate Professor in insurance mathematics and statistics at the Department of Mathematics, University of Oslo since 2015. She holds a master's degree in Industrial Mathematics from NTNU (2002) and a PhD from the Statistics for Innovation center (2008–2012), with a 20% position as a research scientist at the Norwegian Computing Centre. Previously, she worked there as a research scientist and senior scientist. Her research interests focus on multivariate statistics, copulae, skew and heavy-tailed distributions, and applications in insurance and finance. She has contributed to advancements in statistical modeling, particularly in copula constructions and their applications to risk assessment and extreme value analysis. Key awards include the Sverdrup award for young scientists and the mathematical award of Hanna og John Olav Stubban . She is affiliated with the Statistics and Data Science research group and the completed Stochastics of Renewable Energy Markets (STORE) project. Her work spans interdisciplinary collaborations, including applications in immunology and bioinformatics, leveraging machine learning for antibody-antigen interaction studies and synthetic data generation.

• Sensory Archaeology
• Material Culture
• Early Medieval Archaeology
• +4 more

Matthias Friedrich is affiliated with the Department of Prehistoric and Historical Archaeology at the University of Vienna. He teaches courses such as Sensory Archaeology in Prehistory and Historical Archaeology, Premodern Material Culture and the Senses, and Art and Archaeology in Late Antiquity and the Early Middle Ages. His research focuses on sensory experiences in material culture, early medieval and late antique studies, Germanic cultural categories, and archaeological chronology. He has published extensively on topics including Merovingian period chronology, visual culture, and burial practices in Southern Germany. Key research interests include the intersection of sensory perception and material objects, the reinterpretation of 'Germanic' identity in historical contexts, and the application of chronology frameworks to early medieval artifacts. His work bridges archaeological theory with empirical analysis, particularly in funerary archaeology and regional studies.

Renate Delucchi DanhierView profile

Researcher

• Psycholinguistics
• Comparative Linguistics
• Multilingualism
• +12 more

Renate Delucchi Danhier is a researcher at the Faculty of Cultural Studies, Technical University of Dortmund, where she has held positions as Temporary Academic Councilor (2018–2026) and Research Assistant (2015–2018). She holds a Ph.D. in Comparative Linguistics from Heidelberg University and an M.A. in German as a Foreign Language, Classical Archaeology, and Egyptology. Doctorate: Comparative Linguistics, Heidelberg University (2015) Master of Arts: German as a Foreign Language Philology, Classical Archaeology, Egyptology, Heidelberg University (2008) Her research focuses on psycholinguistics, multilingualism, bilingual education, language and cognition, and the role of language in inclusion. She investigates how linguistic structures affect perception, particularly in spatial and educational contexts. Her work often integrates eye-tracking methodologies and interdisciplinary collaboration, especially with Barbara Mertins. The 15 most recent publications reveal a strong trend in psycholinguistics, with emphasis on comparative analysis of German and Spanish, syntactic features in educational texts, and multilingual cognition. Her recent work extends into data visualization and diversity in cognition, showing a growing interest in humanistic data representation. She has contributed to significant interdisciplinary projects such as 'Psycholinguistic Foundations of Inclusion' and co-edited special issues on diversity in cognition. Renate Delucchi Danhier has not received any explicitly mentioned scientific awards. She has not listed any advisees or students. There is no information about labs or research teams she leads, but her collaborative publications suggest active participation in research groups focused on language, cognition, and education.

Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

• Analytical Chemistry
• Bioinformatics
• Environmental Sciences
• +7 more

Dr. Beatriz Galindo-Prieto is a Researcher at Imperial College London's School of Public Health (Faculty of Medicine), part of the Environmental Toxicology Group within the Environmental Research Group. She specializes in developing multiblock/multi-omics and multivariate data analysis methods, with a focus on big data interpretation, variable selection, and applications in environmental health and bioinformatics. Her current projects include analyzing air pollution effects on health via RNA-seq datasets (UKRI NERC-funded) and policy studies on urban air quality. Affiliations include the MRC Centre for Environment and Health, and NIHR Health Protection Research Units in Environmental Exposures and Chemical/Radiation Hazards. Education: MSc in Chemistry (University of the Balearic Islands) and PhD in Chemometrics (Umeå University, Sweden). Her PhD involved novel variable selection algorithms for multivariate data (VIPOPLS, VIPO2PLS, MB-VIOP). Post-PhD roles include postdoctoral fellowships at NTNU (Norway) and Weill Cornell Medical College (USA), focusing on machine learning for big data in neuroscience and epidemiology. She has taught undergraduate/graduate courses and co-supervised students. Research Interests: Combining chemometrics, bioinformatics, and machine learning to address societal challenges. Key areas include multivariate data analysis for environmental systems, neurodegenerative diseases (Alzheimer’s), and real-time epidemic modeling (Ebola, COVID-19). She emphasizes interdisciplinary collaboration across analytical chemistry, systems biology, and public health policy. Grants & Funding: UKRI NERC, ERCIM (Big Data Cybernetics project), and institutional support from Imperial College London. Her work bridges academia and real-world applications, aiming to translate computational methods into actionable insights for health and environmental policy. Labs/Teams: Active in the Environmental Toxicology Group at Imperial, collaborating with the MRC Centre and NIHR units. Her lab develops novel methodologies for data fusion, visualization, and predictive modeling in complex systems.