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Donglin ZengView profile

Adjunct Professor

• Biostatistics
• Machine Learning
• Personalized Medicine
• +7 more

Dr. Donglin Zeng is an Adjunct Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds a PhD in Statistics from the University of Michigan (2001) and earlier degrees in Mathematics from the University of Science and Technology of China (B.S., 1993; M.S., 1995). His research focuses on modern empirical process theory, semiparametric efficiency, survival analysis, causal inference, and high-dimensional data. He has published over 100 papers in top-tier statistical journals and is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. Dr. Zeng has taught advanced courses in probability, statistical inference, and machine learning. His service includes roles on editorial boards and NIH study sections. His research interests span personalized medicine, machine learning applications in healthcare, biomarker discovery, and semiparametric modeling of clinical trial data. Notable contributions include methodologies for electronic health records analysis, survival data inference, and genetic association studies. His awards include the 2011 ASA Fellowship and 2010 IMS Fellowship, alongside early-career recognitions like the 2008 Roy Kuebler Fund Award. His work bridges theoretical statistics with practical applications, emphasizing rigorous inference in high-dimensional and complex data settings. Dr. Zeng's technical contributions include algorithms for semiparametric transformation models and meta-analysis simulations. He actively explores interdisciplinary collaborations, particularly in biomedical research and health informatics.

• Financial Econometrics
• High-Frequency Data Analysis
• Market Microstructure
• +4 more

Aleksey Kolokolov is an Associate Professor at the Manchester Business School, University of Manchester, specializing in financial econometrics and market microstructure analysis. His research focuses on high-frequency data analysis, jump detection in financial markets, liquidity modeling, and statistical methods applied to financial time series. His research interests span several key areas in modern finance: Developing statistical methods for analyzing high-frequency financial data Studying market microstructure and price formation processes Investigating liquidity dynamics and market stability during extreme events Applying econometric techniques to cryptocurrency markets, particularly Bitcoin Creating robust estimators for financial volatility and jump activity His work bridges theoretical econometrics with practical financial applications, often addressing methodological challenges in analyzing discontinuous trading patterns and irregular sampling schemes. Analysis of his publication record reveals a strong focus on methodological innovations in financial econometrics, with particular emphasis on jump detection, price staleness, and liquidity measurement. His most cited work, 'Nonstandard Errors' (Journal of Finance, 2024), represents a major collaborative effort addressing statistical challenges in finance research. His recent publications show increasing attention to cryptocurrency markets and the application of traditional financial econometric methods to these emerging asset classes. Kolokolov maintains an active research agenda with frequent collaborations, particularly with researchers like Roberto Renò, Federico M. Bandi, and Kim Christensen. His work appears in top finance journals and working paper series, demonstrating his significant contribution to the field of financial econometrics.

Rob SalomoneView profile

Research Fellow

• Statistics
• Machine Learning
• Computational Methods
• +3 more

Dr. Rob Salomone is a Senior Research Fellow in Data Science at the Queensland University of Technology's (QUT) Centre for Data Science. He earned his Ph.D. in Statistics from the University of Queensland in 2018, focusing on advanced Monte Carlo methodology. Before joining QUT, he held research fellowships at the University of Queensland and UNSW Sydney. His research bridges statistics and machine learning, with an emphasis on developing robust computational techniques for large datasets and complex models. He specializes in creating scientific models that integrate advanced machine learning, domain expertise, and tailored computational methods to address intricate mathematical challenges. Salomone’s work spans Bayesian computation, Monte Carlo methods, and applications in computational biology and environmental systems. His contributions include advancements in nested sampling, CRISPR guide RNA optimization, and graph neural networks for anomaly detection. He also explores federated learning and uncertainty quantification in high-dimensional settings. Notable collaborations include work on agent-based tumor growth models and spectral subsampling for time series analysis. Though no formal students are listed, his research impact is evident through interdisciplinary projects and methodological innovations.

Anirban BhattacharyaView profile

Professor

• Bayesian Statistics
• High-dimensional Data Analysis
• Bayesian Nonparametrics
• +3 more

Anirban Bhattacharya is a Professor and Patricia R. Smith and Dr. William B. Smith Faculty Fellow in the Department of Statistics at Texas A&M University, within the College of Arts & Sciences. His research focuses on Bayesian asymptotics, Bayesian nonparametrics, and high-dimensional data analysis. He holds a Ph.D. in Statistics from Duke University (2012) and Master's and Bachelor's degrees in Statistics from the Indian Statistical Institute (2008 and 2006). His academic journey includes advanced studies at prestigious institutions, blending theoretical rigor with computational innovation. Research interests emphasize robust statistical methods, scalable Bayesian computation, and applications in machine learning. Recent work explores challenges in high-dimensional inference, optimal transport, and fair clustering algorithms. Notable contributions include advancements in Gibbs posterior frameworks, constrained Gaussian processes, and variational inference techniques. He has been recognized with the prestigious Smith Fellowship, highlighting his impact in statistical methodology. His publications span foundational Bayesian theory to applied problems in physics and engineering. Bhattacharya’s work often bridges computational efficiency and statistical accuracy, addressing modern challenges in big data analysis. Ongoing research includes developing robust estimators for complex metric spaces and exploring fair machine learning methodologies.

• Skeletal Biology
• Human Evolution
• Morphological Adaptation
• +5 more

Mark Hubbe is a Professor at The Ohio State University, specializing in skeletal biology and human evolution. His primary research focuses on morphological adaptation and modern human dispersion, particularly in South America during the Holocene. A secondary focus examines skeletal measures of health and lifestyle in prehistoric Andean societies. He leads the Human Evolution, Adaptation, and Dispersion Studies (HEADS) initiative, fostering interdisciplinary collaboration across institutions globally. His current graduate students include Megan Duncanson, Alexis Dzubak, and others, co-advised with experts in related fields. Hubbe collaborates with researchers in Brazil, Germany, and the U.S. to study topics such as dental metrics, climate adaptation, and social stratification in ancient populations. His work integrates morphometrics, isotopic analysis, and archaeological evidence to explore human biological diversity and historical lifeways. Key projects include analyzing Tiwanaku-period social structures in northern Chile and investigating the biological impacts of industrialization in historical populations. His research emphasizes quantitative methods, including 3D craniometric analysis and morphological clustering algorithms, to address questions about human migration and adaptation. Hubbe’s contributions span over 150 articles, with recent work focusing on topics like craniofacial climate adaptation, dental stress indicators, and the application of bioarchaeological theory. His studies frequently highlight the interplay between biological and cultural factors in shaping human populations.

• Biogeography
• Biodiversity Conservation
• Ecological Modeling
• +3 more

Benjamin Shipley is a Postdoctoral Research Fellow at the University of Oxford, focusing on biogeography, species distributions, and conservation biology. His work examines how species and communities organize across space and time, with a particular emphasis on mammalian endemism and the drivers of biodiversity patterns. He completed his Ph.D. at Georgia Institute of Technology, where he studied global patterns of small-ranged species (endemics) and their environmental and evolutionary drivers. His research integrates paleontological and modern datasets to explore long-term ecological dynamics, such as the relationship between species richness and occupancy. Key methodologies include species distribution modeling (e.g., megaSDM framework), functional trait analysis, and spatial ecology. His publications highlight the importance of environmental conditions, habitat constraints, and evolutionary processes in shaping biodiversity. Recent work emphasizes conservation priorities, particularly in identifying endemism hotspots and improving model accuracy for conservation decision-making. While his research spans multiple ecosystems—from African mammals to Panamanian avian communities—Shipley’s contributions bridge theoretical ecology with applied conservation challenges. His articles explore topics like climatic niche divergence in island endemics, the role of topography in range sizes, and the legacy of Pleistocene megafaunal extinctions on modern plant biomes. Though no awards or grants are explicitly listed, his work reflects collaborations with institutions like Georgia Tech and Oxford, addressing global biodiversity challenges through interdisciplinary approaches.

Jerome SanesView profile

Professor

• Motor Neuroscience
• Brain Mechanisms of Voluntary Movement
• Motor Learning
• +7 more

Jerome Sanes is a Professor of Neuroscience at Brown University, where he also serves as the Director of MRI Research. His academic career spans several decades, beginning with graduate studies at the University of Rochester and post-doctoral work at the National Institute of Mental Health. From 1997-2000, he directed the Functional Neuroimaging Laboratory at Foundation Santa Lucia in Rome, Italy. He has published over 90 peer-reviewed papers and served on editorial boards of major neuroscience journals including the Journal of Neuroscience and NeuroImage. Dr. Sanes received his PhD in 1979 and MA in 1977 from the University of Rochester, and his BA in 1974 from the State University of New York at Binghamton. His educational background laid the foundation for his research career focused on understanding brain mechanisms underlying movement and cognition. Dr. Sanes' research initially focused on brain mechanisms of voluntary movement and motor learning, with particular interest in how multiple brain regions including the frontal and parietal lobes, basal ganglia, and cerebellum coordinate to produce skilled movements. More recently, his research has expanded to investigate non-image forming visual pathways and their influence on human behavior and cognition. His laboratory employs advanced neuroimaging techniques including functional Magnetic Resonance Imaging and Electroencephalography to study these questions. His recent publications demonstrate a continued focus on motor neuroscience while expanding into new areas including the effects of light on mood and cognition, neural mechanisms in Alzheimer's disease, and social neuroscience examining neural responses to peer interactions. This evolution reflects both the maturation of his research program and the interdisciplinary nature of modern neuroscience. United States Public Health Service Trainee Rush Rhees Fellow, University of Rochester National Research Service Award Honorary Master of Arts, Brown University Dr. Sanes has secured substantial research funding throughout his career, currently serving as PI on an NIH COBRE Center for Central Nervous System Function grant totaling approximately $7.5 million. He also serves as Co-PI on an NIH grant for developing Quantum Magnetic Tunneling Junction Sensor Arrays for Brain MEG, and as Co-I on a US Veterans Administration Center for Neurorestoration and Neurorehabilitation grant. His past funding includes significant awards from the National Science Foundation, Department of Energy, and DARPA, demonstrating the breadth and impact of his research program. As Director of the Brown University MRI Research Facility, Dr. Sanes oversees a major neuroimaging resource that supports numerous research projects across the university. His laboratory has been at the forefront of investigating brain mechanisms of voluntary movement, motor learning, and action intention, contributing significantly to our understanding of how the brain controls skilled behavior.

• Essential Oils
• Phytochemistry
• Pharmacology
• +3 more

Prof. Betül Demirci is a distinguished faculty member at Anadolu University's Faculty of Pharmacy, Department of Pharmaceutical Sciences, where she has held the rank of Professor since 2010. Her academic journey at Anadolu University includes roles as Associate Professor (2005-2010), Assistant Professor (1999-2005), and Research Assistant (1992-1999). Her educational background comprises: Doctorate in Pharmacognosy from Anadolu University (1999) Postgraduate degree in Pharmacognosy from Anadolu University (1994) Undergraduate degree in Pharmacy from Anadolu University (1992) Prof. Demirci's research focuses extensively on essential oils , phytochemistry , and biological activities of natural products . She investigates the antimicrobial, antiviral, and pharmacological properties of plant-derived compounds, with emphasis on endemic Turkish species. Her work bridges ethnopharmacology and modern drug discovery, particularly in validating traditional plant uses through chemical and bioactivity analyses. Her recent publications (2024-2025) demonstrate a consistent focus on: Chemical profiling of essential oils from diverse plant genera (Salvia, Melaleuca, Origanum, etc.) Evaluation of synergistic antimicrobial/antiviral effects Multivariate statistical analysis of phytochemical data Biological validation of traditional medicinal plants This reflects her interdisciplinary approach combining phytochemistry, microbiology, and data science. Prof. Demirci has advised 13 theses and contributed to 29 research projects. While specific grants are not detailed, her prolific output indicates sustained research funding. No explicit lab/team structures are described, but collaborations are evident in multi-author publications.

Ann BrandweinView profile

Professor

• Multivariate Estimation
• Decision Theory
• Business Statistics
• +6 more

Ann Brandwein is a Professor at the Zicklin School of Business , Baruch College (CUNY), within the Department of Information Systems and Statistics. With a career spanning over five decades, her expertise lies in Multivariate Estimation and Decision Theory , focusing on advanced statistical methodologies and their applications in business and social sciences. She has taught courses such as Applied Probability and Managerial Statistics since the 1970s and is actively involved in doctoral education and curriculum development. Education Ph.D., Statistics, Rutgers University (1975) M.S., Statistics, Rutgers University (1973) B.S., Mathematics, City College of New York (1971) Her research bridges Stein estimation under spherical symmetry with studies on societal trends, including alliteration in mate selection and quantitative evaluations of peer tutoring programs . She has received multiple honors for teaching excellence and contributed to academic governance through roles in committees like the Zicklin Graduate Curriculum Committee and Baruch College Faculty Senate. Scientific Awards Award for Teaching Excellence, Zicklin School of Business (2018) Presidential Excellence Award for Distinguished Service (2010) Honorary Faculty Member, Beta Gamma Sigma Honor Society Outstanding Young Women in America (1982) Brandwein has secured grants like the PSC-CUNY-funded 'Stein Estimation for Spherically Symmetric Distributions; Recent Developments (2009-2010) and served as an advisor for graduate programs in Quantitative Methods. Her work has been published in journals such as Statistical Science and Annals of Statistics , reflecting her multidisciplinary impact.

Stanislav VolgushevView profile

Associate Professor

• Data Science
• Probability
• Theoretical Statistics
• +7 more

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

• Applied Mathematics
• Probability
• Statistics
• +5 more

Marie-Eliette DURY is an Associate Professor in Applied Mathematics at the School of Economics, University of Clermont Auvergne (UCA). She serves as Head of the Economics and Modern Languages Degree program and is an IPPA Trainer and EE Ambassador at the IPPA Center. Her research bridges probability, statistics, and quantitative finance, with a focus on financial market efficiency, volatility modeling, and climate-related risk assessment in developing economies. Key Affiliations: Centre d'Études et de Recherches sur le Développement International (CERDI), Laboratoire de Mathématiques de Clermont Research Interests: Her work spans extreme value theory , stable distributions , multifractional Brownian motion , and index insurance mechanisms . She specializes in volatility forecasting for financial and commodity markets, particularly gold and nickel, and develops spatially consistent drought hazard models for West Africa. Recent Article Trends: DURY’s publications (2015–2024) emphasize stochastic processes (GARCH/ARCH, stable models), climate risk quantification (droughts, extreme precipitation), and financial market anomalies (Shanghai vs. Shenzhen stock markets). Her methodologies integrate wavelet decomposition, p-variations, and mixture models. Grants & Projects: Involved in ANR projects CAP 20-25 and Design of Well Being Monitoring Systems , alongside CNRS-funded collaborations. Collaborations: Frequently co-authors with Bing Xiao and Nourddine Azzaoui on cross-disciplinary topics.

Rand LowView profile

Associate Professor

• Portfolio Optimization
• Risk Management
• Quantitative Finance
• +5 more

Rand Low serves as Honorary Associate Professor at the University of Queensland's School of Business and Associate Professor of Quantitative Finance at Bond Business School. His expertise bridges academia and industry with extensive experience leading quantitative research teams at Bank of America Merrill Lynch and BlackRock in New York City. He actively contributes to financial discourse through media engagements with major outlets including Australian Financial Review, BBC, and ABC News, while advising governmental bodies on bond market development. Dr Low holds a Doctor of Philosophy from the University of Queensland. His educational background includes Chartered Engineer designation from Engineers Australia earned during engineering management roles at Honeywell on landmark projects including GOMA and Mater Mothers' Hospital. Professor Low's research spans four critical domains in quantitative finance: Portfolio Optimization: Specializing in multi-asset strategies (commodities, fixed income, private equity) using vine copulas for crisis-period dependence modeling Risk Management: Developing frameworks for credit/market/operational risk including VaR/CVaR metrics and regulatory model validation Digital Assets: Pioneering research on cryptocurrency derivatives, blockchain applications, and ESG integration in digital finance Systematic Trading: Creating factor-based and event-driven strategies validated through neural network analysis of commodity risk premia His 15 most recent publications reveal three dominant trends: increasing integration of machine learning in commodity risk analysis (2023-2025), growing emphasis on ESG factors in credit rating determinants (2024), and innovative applications of blockchain technology beyond finance into conservation (2025). The research consistently addresses volatility spillovers during crises while expanding into digital asset derivatives and sustainable investment frameworks. Professor Low's scientific recognition includes: GSITA Award and 3MT Competition Winner during PhD studies Dean's Award for Research Higher Degree Excellence Australia Awards - Endeavour Fellowship Australian Institute of Business and Economics (AIBE) Scholar designation Research Fellowship on Portfolio Optimization & Risk Management Techniques He actively supervises doctoral candidates in corporate credit risk modeling, robo-advisor development, and multi-asset optimization. His funded research includes the Accounting and Finance Association of Australia and New Zealand grant (2014-2015) on risk-adjusted retirement metrics and the UQ Postdoctoral Research Fellowship (2014-2016) on crisis-period portfolio optimization. Industry collaborations include advisory roles for BitOrb (Digital Assets Exchange) and Australian Bond Exchange. Professor Low leads cross-sector initiatives connecting academic research with financial practice, having directed quantitative teams at top-tier institutions where he defended model development practices to US regulators including the Federal Reserve. His current focus involves applying statistical learning to automate investment processes across cryptocurrency, commodities, and corporate credit markets.

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +4 more

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

• warehouse systems
• modern computer technologies
• technical means of traffic management

Serhiy Vasyliovych Hrytsay serves as a Senior Lecturer in the Department of Transport Technologies at Zaporizhzhia Polytechnic University (formerly Zaporizhia State Technical University). With over 25 years of academic service since 1996, he graduated with honors from the university's Faculty of Automechanics in 1996, specializing in 'Organization of transportation and management in transport' with the qualification of 'engineer in organization and management'. His scientific interests focus on warehouse systems, modern computer technologies in transportation, and technical means of traffic management. Professor Hrytsay teaches 'Fundamentals of Systems and Control Theory' and 'Warehouse systems and complexes,' demonstrating his expertise across both theoretical and practical aspects of transportation engineering. His publication portfolio reveals significant contributions to transportation engineering, particularly in recent years (2016-2021) with research spanning vehicle operation efficiency, railway cargo temperature maintenance, freight flow analysis, and pedestrian crossing optimization. These works demonstrate a consistent focus on practical transportation challenges with emphasis on efficiency, safety, and system optimization. Certificate from Zaporizhia City Council for contribution to specialist preparation (2021) Honorary Certificate from ZNTU for scientific contributions (2014) Honorary Certificate from district administration for transport industry training (2012) Rector's gratitude for student olympiad contributions (2007) ZNTU Certificate for scientific supervision excellence (2006) Regional Administration gratitude for professional work (2006) Professor Hrytsay maintains an active research profile with 40 scientific works including 6 patents, 1 monograph, and 1 textbook. His international scholarly presence is evidenced by accounts on ORCID (0000-0001-7055-6977), Web of Science (G-9767-2019), Scopus (57193439947), and Google Scholar.

Niklas DonthView profile

Lecturer

• Political Sociology
• Political Psychology
• Election Research
• +7 more

Niklas Donth is a Lecturer and PhD student in Political Science at the University of Stuttgart, affiliated with the Institute of Social Sciences, Department of Political Systems and Political Sociology. Since December 2023, he has served as an Academic Associate at the university while completing his doctoral research. His research focuses on how individuals form political preferences, attitudes, and opinions, with particular interest in the strategic intersections between party strategies and voting behavior. His doctoral project examines the role of left-authoritarian voters in the electoral contest between social democracy and the populist radical right in Europe. Methodologically, he specializes in quantitative empirical applications of Bayesian statistics, causal machine learning, and modern causal analysis approaches. Dr. Donth teaches courses including Parties in Competition, Multivariate Data Analysis with R, and Populism and Radicalism in Germany. His technical expertise is evident in his GitHub contributions to European Social Survey (ESS) data analysis tools and party linking projects, demonstrating his commitment to advancing methodological approaches in political science research. He holds a Master's degree in Political Science from the University of Mannheim (2021-2023) and a Bachelor's degree in Political Science and Psychology from Heidelberg University (2017-2021). His methodological training includes multiple specialized workshops from GESIS on causal mediation analysis, multiverse analysis, and comparative social research with multi-group SEM.