جست‌وجوی استاد و پژوهشگر

Nicolle T Clementsمشاهده پروفایل

دانشیار

• Applied Statistics
• Agricultural Data Analysis
• Data Mining
• +۳ مورد دیگر

Nicolle T. Clements is an Associate Professor at Saint Joseph's University's Department of Decision and System Sciences and serves as Academic Coordinator for the MS in Business Intelligence & Analytics program. She holds a PhD in Statistics from Temple University, an M.S. in Statistics from Virginia Tech, and a B.S. in Mathematics from Millersville University. Her research focuses on applied statistical analysis in Pennsylvania's agricultural sector, including projects funded by the PA Department of Agriculture to evaluate initiatives like PA Preferred branding, urban agriculture trends, and small meat processing capacity. She teaches courses on statistics, data mining, and R programming across undergraduate, graduate, and executive education levels. Her research expertise spans statistical methodologies, agricultural data analysis, and interdisciplinary applications in public health and environmental science. Recent work addresses consumer perceptions of industrial hemp, vegetation monitoring via spatio-temporal models, and ethical dimensions of business intelligence storytelling. Dr. Clements has published extensively in journals like Journal of Criminal Justice , International Journal of Remote Sensing , and Journal of Addictive Behaviors . Her 2024 study on industrial hemp perceptions highlights her ongoing commitment to bridging statistical rigor with real-world agricultural challenges. She collaborates actively with policymakers and industry partners to translate statistical insights into actionable strategies.

Zhigen Zhaoمشاهده پروفایل

دانشیار

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +۵ مورد دیگر

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

Thomas Chrowder Chamberlinمشاهده پروفایل

استاد

• Geology
• Glaciology
• Planetary Science
• +۳ مورد دیگر

Thomas Chrowder Chamberlin (1843–1928) was an American geologist and educator. He served as a professor of geology at the University of Wisconsin (1873–1887) and later organized the geology department at the University of Chicago (1892–1918). He was president of the University of Wisconsin (1887–1892) and the Chicago Academy of Sciences (1898–1914). His research spanned glaciology, planetary formation, and scientific methodology. He authored foundational works on glacial stages, the planetesimal hypothesis with Forest Ray Moulton, and pioneered the 'method of multiple working hypotheses.' Education: B.A. in Classical Studies from Beloit College (1866) Graduate studies in geology at the University of Michigan (1868–1869) Research Interests: Chamberlin advanced understanding of Pleistocene glaciation, atmospheric carbon dioxide's role in climate, and the origins of the solar system. His methodologies emphasized rigorous hypothesis testing, influencing modern scientific practice. His work on glacial mapping in Wisconsin remains influential in geological surveying. Awards: Hayden Memorial Geological Award (1920) Penrose Gold Medal (1924) Penrose Medal (1927) Legacy: He founded the Journal of Geology (1893) and shaped academic geology through his interdisciplinary approach. His papers are archived at the University of Chicago and Beloit College. Buildings and geological landmarks bear his name, including Mount Chamberlin in California.

James Burridgeمشاهده پروفایل

استاد

• Probability
• Statistical Physics
• Language Evolution
• +۷ مورد دیگر

James Burridge is a Dr Reader in Probability and Statistical Physics at the University of Portsmouth, affiliated with the Faculty of Technology and the School of Mathematics & Physics. He is also associated with the Portsmouth AI and Data Science Centre and the Centre for Environmental and Renewable Energy Solutions. He is currently accepting PhD students and is actively funded by the Royal Society and the Faraday Institution. University: University of Portsmouth School: Faculty of Technology Department: School of Mathematics & Physics Research Centres: Portsmouth AI and Data Science Centre, Centre for Environmental and Renewable Energy Solutions He earned his Physics degree from the University of Warwick, followed by Maths Part III and a PhD in Statistical Physics from Cambridge University. After working in banking as a Quant and as a school teacher in Mathematics and Physics, he returned to academia in 2012. Education: BSc in Physics (University of Warwick) Postgraduate: Maths Part III (Cambridge) PhD in Statistical Physics (Cambridge University) His research lies at the intersection of probability, statistical physics, and real-world applications in social and ecological systems. Key interests include language evolution, speech analysis using machine learning, modeling of disease transmission, and energy systems like lithium-ion batteries. His work applies complex systems theory and spatial modeling to infer patterns in language change and public behavior during pandemics. He also contributes to the UN Sustainable Development Goals through environmental and renewable energy research. The recent articles show a strong trend toward interdisciplinary research combining physics-based modeling with linguistics and public health. His work leverages machine perception, latent space transformations, and neural networks to analyze speech, while also applying spatial games and statistical mechanics to understand societal responses to disease. This reflects a broad capability in modeling complex adaptive systems across domains. He has received significant recognition through research funding and media attention. His work has been cited in over 100 news outlets, including coverage on social media and video platforms, indicating high public and academic impact. Funding from the Royal Society Funding from the Faraday Institution Media coverage in 108 news outlets Multiple mentions in press and social media James Burridge leads and collaborates on multiple research projects, including battery modeling and speech technology applications. He supervises PhD students and has contributed to datasets and software tools such as a web application for live voice vowel inference. His collaborative network includes researchers from various disciplines, indicating a strong team-oriented research approach.

• Statistical Signal Processing
• Information Theory
• Machine Learning
• +۶ مورد دیگر

Yu Xiang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah since 2018. He holds a B.E. (highest distinction) from Xidian University (2008), a Ph.D. from UC San Diego (2015), and completed a postdoc at Harvard University (2015-2018). His research focuses on statistical signal processing, information theory, and machine learning, with applications in healthcare, IoT, and computational biology. Key areas include distributed testing, trustworthy AI under distribution shifts, and non-stationary time series analysis. Education: Ph.D., Electrical and Computer Engineering, UC San Diego (2015) B.E., Telecommunications Engineering, Xidian University (2008) His work emphasizes causal inference, invariant representation learning, and communication-efficient distributed systems. Recent studies explore Byzantine-robust testing, covariate shift adaptation, and causal discovery in mental health contexts. He has contributed to theoretical bounds in machine learning and signal processing. Grants & Labs: Active in federally funded projects on distributed inference and causal AI. Leads the Invariant Learning Lab at the University of Utah, focusing on interdisciplinary applications of statistical theory.

Eric Depiereuxمشاهده پروفایل

پژوهشگر

• Biology Education
• Statistical Inference Teaching
• Ecology Didactics
• +۲ مورد دیگر

Eric Depiereux is a Researcher at the University of Namur within the Research Unit in Biology Didactics . Holding a Doctor of Science degree since 1982, he has contributed extensively to Biology Education , Statistical Inference Teaching , and Bioinformatics Education over a 40+ year career. 2022: Impact of limb immobilization on bone mineral density and structure (Ovine model study) 2017: Association between catecholaminergic genes and impulsivity in dogs 2015: Adaptation of bioinformatics microarray analysis for rainbow trout toxicogenomics 2011: gViz - gene co-expression network visualization tool His work connects Biostatistics , Ecological Monitoring , and Digital Educational Tools while maintaining active supervision of PhD students and participation in Didactic Engineering projects. Scientific Awards Recipient of award on 15 March 1982 for research contributions Education & Supervision Doctor of Science: Critical use of PCA and cluster analysis for freshwater invertebrate samples Supervised: Bihin (PhD), Regnier (PhD), Vervoort (PhD)

Stefan Grunerمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Model Checking
• +۹ مورد دیگر

Stefan Gruner is a Professor in the Department of Computer Science, Faculty of Engineering, Built Environment and Information Technology, at the University of Pretoria, South Africa. He holds advanced degrees in Informatics (PhD, MSc) and Philosophy (MA), reflecting his interdisciplinary research that bridges formal software engineering, digital forensics, and the philosophy of technology. His work is widely published in international journals and conferences, and he is deeply involved in academic service, including supervision, examination, and program committees. Education: MA in Philosophy, Germany, 2010 PhD in Informatics, RWTH Aachen, Germany, 1999 MSc (Dipl.-Inform.) in Informatics, Germany, 1996 His research interests center on Formal Methods , Software Engineering , and the Philosophy of Computer Science . He has made significant contributions to parameterized and three-valued model checking, formal methods for railway systems, and the scientific foundations of digital forensics. His recent work explores historical semantics in software architecture and the philosophical underpinnings of informatics. His publications from the last few years reveal a strong trend towards formal verification, safety-critical systems, and the epistemological aspects of digital investigation and software engineering. Scientific Recognition: Biographic entry in Who's Who in the World (2010 edition) Member of the selection committee for the Annual Best Dissertation Award (EAPLS) Stefan Gruner is an active advisor and supervisor, having guided numerous MSc and PhD students to completion. He has secured research funding from the South African National Research Foundation (NRF) and international partners for projects on model-driven development, CSP protocol verification, and visualization of distributed algorithms. His academic service is extensive, including roles as program chair for workshops like FM+AM and SEFM, editor for conference proceedings, and external examiner for multiple universities. He has also been a visiting researcher at institutions in Bordeaux and Swansea. He was a central organizer of the Distributed Systems Research Group at the University of Pretoria and has fostered international collaborations, particularly within the formal methods and software engineering communities.

• Multidimensional Data Analysis
• Classification Problems
• Information Society
• +۴ مورد دیگر

Maria Adele Milioli is an Associate Professor at the Faculty of Economics, Department of Economic and Business Sciences, University of Parma. She has been a faculty member since 1998 and previously served as a researcher at the same institution from 1990 to 1998. She earned her PhD in Statistics from the University of Trento in 1988. Current Appointments: Associate Professor, University of Parma (1998-present) Past Appointments: Researcher, University of Parma (1990-1998) Her research focuses on multidimensional data analysis , with recent emphasis on classification problems in the context of the information society . She has co-authored over 40 scientific papers, primarily in business statistics and applied statistical inference. Key research areas include: Econometrics Statistical Modeling Financial Inclusion Social Equity Business Data Analysis She teaches Business Statistics (Modules 1-2) at the second cycle degree level in Business Administration and Statistics in Economics and Management programs across multiple academic years (2013-2025). Her publications reflect expertise in statistical methodology, economic applications, and financial policy analysis. Contact: mariaadele.milioli@unipr.it

Keiichi Kawaiمشاهده پروفایل

استاد

• Microeconomic Theory
• Industrial Organisation
• Economic Theory
• +۴ مورد دیگر

Keiichi Kawai is Professor of Economics at Keio University and concurrently a Senior Visiting Fellow at the University of New South Wales (UNSW) . His research spans microeconomic theory, industrial organisation and information economics, with a focus on strategic information disclosure, pricing, reputation and market design. Education Ph.D. in Economics, Northwestern University (2012) M.A. in Economics, Northwestern University (2007) M.A. in Economics, Kyoto University (2005) B.A. in Economics, Kyoto University (2003) Research Interests Dr Kawai’s work lies at the intersection of microeconomic theory , game theory and industrial organisation . He investigates how economic agents strategically disclose information, how markets cope with adverse selection and reputation, and how optimal pricing and contract mechanisms can be designed under uncertainty. His recent studies illuminate competitive persuasion games, refund policy design and the political economy of institutional kludges. Scientific Awards & Grants Discovery Early Career Researcher Award, Australian Research Council (2017-2019) Project Grant, Kyoto Institute of Economic Research (2019) Multiple UNSW Business School Research & Special Grants (2015-2021) Collegio Carlo Alberto Visiting Fellowship (2013) New Staff Research Start-up Grant, University of Queensland (2012) Numerous scholarships and fellowships including Japan-IMF, Ishizaka Foundation and JSPS grants Advising & Grants While no individual student names are listed in the provided material, Dr Kawai has supervised graduate research at UNSW and continues to mentor doctoral students at Keio University. He has been principal investigator on competitive grants exceeding AUD 500,000 and is regularly invited to present at premier theory conferences and seminars worldwide. Labs & Teams At Keio University he leads the Microeconomic Theory research group within the Faculty of Economics. While at UNSW he was affiliated with the School of Economics and participated in the UNSW Business School research community.

Eric Nalisnickمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۵ مورد دیگر

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.

Alex Karagrigoriouمشاهده پروفایل

دانشیار

• Applied Probability
• Statistical Modeling
• Biostatistics
• +۶ مورد دیگر

Alex Karagrigoriou is an Associate Professor of Statistics at the University of Piraeus, Department of Statistics and Insurance Science. He holds a BSc in Mathematics from the University of Patras, an MA and PhD in Mathematical Statistics from the University of Maryland, USA. His academic career includes roles at the University of Cyprus (1992–2014), the University of the Aegean (2014–2024), and adjunct positions at the Hellenic Open University (2014–present) and Mediterranean Institute of Management (1994–1998). Prior academic roles include work in the USA Department of Agriculture (1986–1992) and maritime sector experience (1980–1986). His research focuses on Applied Probability , Statistical Modeling , Reliability Theory , and Actuarial Mathematics . He has authored/co-authored over 100 journal articles, 10 edited volumes, and 40 conference proceedings, with notable contributions in divergence measures, semi-Markov processes, and risk modeling. His work has been cited over 1,400 times (h-index=17). Key achievements include supervising 3 postdocs, 6 PhDs, and 50 Master’s students, many of whom have received awards such as the Best Young Statistician Award (2007, 2018, 2021). His research trends emphasize stochastic modeling of complex systems, entropy-based inference, and interdependency patterns in econometric data. Labs/Teams: Active collaborator in interdisciplinary projects involving reliability engineering, biostatistics, and financial risk analysis. Serves as Co-Editor of Journal of Reliability and Statistical Studies and contributor to multiple international journals.

Francisco Gonzalez-Limaمشاهده پروفایل

استاد

• Behavioral Neuroscience
• Psychobiology
• Learning and Memory
• +۴ مورد دیگر

Francisco Gonzalez-Lima is the George I. Sanchez Centennial Professor in the Department of Psychology at the University of Texas at Austin's College of Liberal Arts. His academic career spans over four decades, including roles as a Humboldt Research Fellow in Germany and founding faculty at Ponce School of Medicine. He specializes in neurocognitive enhancement, functional neuroimaging, and brain stimulation, with over 400 peer-reviewed publications. His research pioneered FDG neuroimaging for learning functions and developed cytochrome oxidase histochemistry methods. Education: B.S. Biology (Tulane University, 1976), Ph.D. Anatomy & Neurobiology (University of Puerto Rico, 1980) Research focuses on mitochondrial photobiomodulation, transcranial laser stimulation, and neuroprotection. Notable achievements include organizing international conferences on neuroimaging and serving as Texas Consortium Director. Awards include the Distinguished Texas Scientist Award (2015) and Outstanding Achievement Award from the International Behavioral Neuroscience Society (2025). Recent articles emphasize photobiomodulation's applications in aging, epilepsy, and mental health. Active roles include Academic Director of the Texas Academy of Science and NIH BRAIN Initiative PI.

Douglas Steigerwaldمشاهده پروفایل

استاد

• Econometrics
• Economic Statistics
• Environmental Economics
• +۶ مورد دیگر

Professor Douglas Steigerwald is a Professor of Economics at the University of California, Santa Barbara, with a courtesy appointment in Statistics. He serves in the Department of Economics within the College of Letters & Science and leads the Econometrics Research Group. His work bridges economics with environmental science, computer science, geography, and statistics to develop innovative econometric methods. Professor Steigerwald received his B.A. from Pomona College and completed his Ph.D. in Economics at UC Berkeley, where he studied efficiency bounds for semiparametric estimators. After completing his doctoral studies, he immediately joined the faculty at UC Santa Barbara. Steigerwald's research focuses on frontier econometric methods applied to complex problems at the intersection of multiple disciplines. His work includes detecting latent economic events like market regimes and collusive behavior, developing likelihood ratio tests based on Gaussian processes, analyzing clustered data structures, and addressing statistical challenges when sample sizes approach population sizes. He also conducts applied research measuring journal subscription values and assessing water recycling benefits. His publication record reveals a consistent focus on econometric methodology with applications spanning environmental economics, agricultural markets, and cybersecurity. Recent work shows increasing emphasis on causal inference methods, clustered data analysis, and applications to environmental resource management. His interdisciplinary approach connects traditional econometrics with computer science and environmental science challenges. Directed American Economic Association's Summer Training Program Advised Sloan Foundation on graduate research Chair of NSF Graduate Research Fellowship Program in Economics Faculty representative on UCSB Foundation Board of Trustees As faculty director of the Econometrics Research Group, Steigerwald mentors graduate students across multiple departments engaged in applied econometric research. His external funding includes grants from the National Science Foundation and the U.S. Army Research Office for cybersecurity research conducted with the Computer Security Group. Professor Steigerwald teaches undergraduate and graduate courses in econometrics including Introduction to Econometrics (Econ 140A), Econometrics II (Econ 241B), and Advanced Econometrics I & II (Econ 245A & B), emphasizing practical applications of econometric methods to real-world problems.

Janice Scealyمشاهده پروفایل

دانشیار

• Compositional Data Analysis
• Directional Statistics
• Robust Statistics
• +۵ مورد دیگر

Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.

• Applied Probability
• Computational Statistics
• Machine Learning
• +۷ مورد دیگر

Andrew B. Duncan is a Senior Lecturer in Statistics and Data-Centric Engineering at Imperial College London's Department of Mathematics, part of the Faculty of Natural Sciences. He also leads the Data Centric Engineering Programme at the Alan Turing Institute. His research integrates applied probability, computational statistics, and machine learning to solve industrial challenges in areas like cellular biology, aerospace, and energy systems. He holds a PhD from the University of Warwick and previously lectured at the University of Sussex. Education: PhD in Mathematics, University of Warwick (2013), supervised by Andrew Stuart and Charlie Elliott Postdoctoral research at Imperial College London and the University of Oxford Research Interests: Focuses on developing statistical methodologies for complex engineering systems, including predictive health monitoring, computational statistics, and interdisciplinary applications. Key areas include uncertainty quantification, Bayesian inference, and energy-based models. Advising & Grants: Supervised multiple PhD students and postdocs, many now in academic and industry roles. Active in securing funding for projects on sensor optimization, digital twins, and data-centric engineering. Labs/Teams: Leads the Data Centric Engineering group at the Alan Turing Institute and collaborates with the Statistics section at Imperial College. His work bridges academia and industry, emphasizing practical applications of statistical methods.