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Moosa TatarView profile

Assistant Professor

• Public Health
• Data Science
• Econometrics
• +5 more

Moosa Tatar is an Assistant Professor at the University of Houston College of Pharmacy , specializing in Pharmaceutical Health Outcomes and Policy . His research combines data science and applied econometrics to analyze public health policies, focusing on drug misuse , metabolic syndrome , liver diseases , and COVID-19 disparities. He holds dual PhDs in Health Services Research (University of Nebraska Medical Center) and Economics (University of Mazandaran), with expertise in machine learning , health equity , and vaccine policy modeling . PhD, Health Services Research, University of Nebraska Medical Center PhD, Economics, University of Mazandaran MA, Economics, Azad University BA, Economics, University of Sistan and Baluchistan His work addresses critical public health challenges through large-scale dataset analysis , including excess mortality during pandemics , vaccine inequality , and economic impacts of health policies . Key themes include social vulnerability , health disparities , and policy evaluation using machine learning and econometric modeling . Tatar's research has informed global and national health interventions related to prescription drug monitoring and hepatitis C screening . He is affiliated with the Society for Medical Decision Making , AcademyHealth , and American Public Health Association , serving as an ad hoc reviewer for journals like JAMA Pediatrics and Value in Health Regional Issues . His contact email is mtatar@Central.UH.EDU .

Jonathan L. AuerbachView profile

Assistant Professor

• Data Science
• Causal Inference
• Survey Methods
• +6 more

Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .

Farzad SabzikarView profile

Associate Professor

• Stochastic Processes
• Fractional Models
• Optimization Algorithms
• +2 more

Farzad Sabzikar is an Associate Professor in the Department of Statistics at Iowa State University, specializing in stochastic processes, fractional models, and optimization algorithms. He integrates mathematical theory with applications in machine learning and time series analysis. Education: PhD in Statistics (Michigan State University, 2014), MS in Mathematics (Sharif University, 2009), BS in Mathematics (Isfahan University of Technology, 2006) His research bridges fractional calculus and statistical modeling, focusing on tempered processes and their applications in turbulence analysis, geophysical flows, and high-frequency data. He employs wavelet methods and asymptotic theory to study heavy-tailed phenomena and long-range dependencies. Recent publications emphasize tempered fractional Brownian motion, stable noise modeling, and functional data analysis. Key trends include transient anomalous diffusion, machine learning for cognitive decline classification, and optimized signal processing techniques. Scientific Awards: None listed His work has implications for machine learning, geophysics, and astrophysics, though no formal advising, grant, or lab affiliations are detailed in available sources.

Dean DeCockView profile

Professor

• Statistics
• Regression Analysis
• Data Science
• +2 more

Dean DeCock is a Professor of Statistics at Truman State University with expertise in statistical analysis, regression modeling, and data science applications. His academic background includes a Ph.D. in Statistics & Industrial Engineering from Iowa State University. Dr. DeCock's research interests span housing market analysis and sports analytics, demonstrating his ability to apply statistical methods to diverse real-world problems. He has made significant contributions to statistical education through the creation of teaching datasets and analytical frameworks. His most recent work focuses on analyzing Caitlin Clark's unprecedented dual achievements in NCAA basketball scoring and assisting records, showcasing innovative applications of statistical methods in sports analytics. This research highlights his ability to extract meaningful insights from complex datasets. Dr. DeCock is best known for developing the Ames Housing Data Set during his 2011 sabbatical, which has become a standard teaching resource in statistics and data science education worldwide. This dataset is widely used for teaching regression analysis and machine learning concepts. His work has been published in the Journal of Statistical Education, and his datasets are incorporated into major statistical software packages including R, SAS, and Minitab, demonstrating the practical impact of his contributions to the field.

• Actuarial Science
• Insurance
• Risk Management
• +3 more

Gee Lee is an Associate Professor in the Department of Statistics & Probability and the Department of Mathematics at Michigan State University. His work bridges actuarial science with advanced statistical and machine learning methodologies, focusing on practical applications for insurance risk modeling. PhD, University of Wisconsin-Madison Associate (ASA), Society of Actuaries His research centers on insurance loss modeling for rate-making and loss reserving, multivariate insurance coverage optimization, dependence structure analysis, and integrating machine learning into actuarial frameworks. Current projects include deep neural networks for claim prediction, unstructured data analysis, and multivariate coverage optimization. Recent publications highlight trends in crop insurance modeling (2025), regularization techniques (2024), multivariate risk retention strategies (2023), textual data analysis (2022), copula regression (2022), and reinsurance game theory (2022). Earlier works explore shrinkage methods, word embeddings, longitudinal claims, healthcare data, and deductible ratemaking. Gee Lee supervises MS and PhD students in actuarial science. Former advisees include Leonard Korreshi (2024), Qiaozhen Qian (2023), and Scott Manski (2020, co-advised). He also supports undergraduate research through MSU’s REU program and directed study courses.

Wai Kiong Oswald ChongView profile

Associate Professor

• Artificial Intelligence
• Engineering Systems
• Optimization
• +10 more

Dr. Wai Kiong Oswald Chong is an Associate Professor at Arizona State University's School of Sustainable Engineering and the Built Environment, with a dual affiliation as Senior Global Futures Scientist at the Global Futures Scientists and Scholars program. He holds a PhD in Civil Engineering from the University of Texas-Austin, MSc and BSc in Building from the National University of Singapore, and focuses on integrating artificial intelligence with sustainable engineering systems. PhD (2005): Civil Engineering, University of Texas-Austin MSc (1999) & BSc (1997): National University of Singapore His research bridges lunar construction with Earth-bound sustainable systems, covering topics like: Space habitat modularization Resource circularity systems AI-enhanced building codes Climate-resilient infrastructure Advanced energy modeling Construction supply chain optimization Publications demonstrate consistent focus on: Semiconductor facility HVAC optimization Building energy consumption anomalies Life cycle assessment frameworks Construction risk management Deconstruction and material reuse AI-driven system modeling Current research projects include: Lunar MVI (Moon Village Initiative) Semiconductor fab design optimization Human-AI knowledge interfaces Thermal insulation systems for extreme environments Smart grid energy modeling

Yikun BanView profile

Associate Professor

• Reinforcement Learning
• Data Mining
• Neural Networks
• +7 more

Yikun Ban is a tenure-track Associate Professor in the School of Computer Science and Engineering at Beihang University, where he is a member of the State Key Laboratory of Software Development Environment. He earned his PhD in Computer Science from the University of Illinois Urbana-Champaign (2023), MS in Computer Science from Peking University (2019), and BS in Software Engineering from Wuhan University (2016). PhD: University of Illinois Urbana-Champaign (2023) MS: Peking University (2019) BS: Wuhan University (2016) His research focuses on principled algorithms for reinforcement learning with human feedback, neural contextual bandits, and exploration-exploitation problems. He develops frameworks combining deep learning with bandit theory for applications in recommendation systems, disinformation detection, and dynamic graph learning. Recent publications address: Robust neural contextual bandits (NeurIPS 2024) Graph neural bandits (KDD 2023) Meta-learning for bandit scheduling (NeurIPS 2023) Clustering in contextual bandits (WWW 2021, AAAI 2021) Honors include the NeurIPS Scholar Award and recognition as ICML Outstanding Reviewer . His open-source LOCB repository provides Python implementations for contextual multi-armed bandit algorithms with local clustering, supporting applications in recommendation systems and online learning.

Christopher ContrerasView profile

Assistant Professor

• Criminology
• Criminal Justice
• Community Crime
• +5 more

Christopher Contreras is an Assistant Professor in the Department of Sociology at the University of Massachusetts Boston, where he has established himself as a prominent researcher in criminology and spatial analysis of crime. His academic journey includes a PhD from the University of California - Irvine, which provided the foundation for his expertise in quantitative methods and criminological theory. Dr. Contreras's research focuses on the application of criminological theory to ecological studies of social problems, particularly examining antisocial deviant behaviors such as criminal violence, drug abuse, drug trafficking, and suicide mortality. His methodological approach emphasizes spatial techniques that model geographic patterns of crime and population health. His work spans diverse areas including communities and crime, criminological theory, drug abuse, drug markets, and quantitative methods, with particular attention to how physical environments influence criminal behavior. His publication record demonstrates significant contributions to the field, with recent work examining occupational crimes in Macau's casino industry, neighborhood walkability and crime relationships, spatiotemporal dynamics of drug activity and crime rates, and the impact of medical marijuana dispensaries on urban crime patterns. These studies showcase his ability to address complex criminological questions using sophisticated spatial and quantitative methodologies. American Society of Criminology awards Society for the Study of Social Problems awards Western Society of Criminology awards Dr. Contreras's research has received recognition from multiple prestigious criminology organizations, reflecting the significance and impact of his work. His current research agenda appears to be expanding the understanding of how spatial contexts shape criminal behavior and social problems, with particular attention to specialized environments like casino industries and urban neighborhoods. His methodological expertise in spatial analysis provides valuable tools for understanding the geographic dimensions of crime and social issues.

Wolfgang HillertView profile

Professor

• Accelerator Physics
• Superconducting Accelerator Technology
• Free-Electron Lasers (FEL)
• +4 more

Prof. Dr. Wolfgang Hillert is a leading physicist at the University of Hamburg , serving as the Bjørn-Wiik Professor for Accelerator Physics since 2016. Affiliated with the Institute of Experimental Physics under the Faculty of Mathematics, Informatics and Natural Sciences, he specializes in Accelerator Physics , Superconducting Accelerator Technology , and Free-Electron Lasers (FEL) . His work focuses on polarized electron beams, SRF cavity optimization, and gravitational wave detection methods. Education: Physics degree from University of Bonn (1987), Promotion in Atmospheric Physics (1992), Habilitation in Physics (2001) Leadership Roles: Head of Accelerator Physics Group (2016–present), Managing Director of Institute of Experimental Physics (2019–2021) Research Trends: His recent work spans superconducting RF cavities for gravitational wave detectors ( 2025 ), resonant slow extraction in electron boosters, and atomic layer deposition of superconducting thin films. Publications highlight advancements in beam dynamics , cryogenic systems , and terahertz generation . Teaching & Outreach: He has lectured on Accelerator Physics since 2002 and engaged in public science communication, including talks on Physics of Music (2005–2021) and teacher training programs at DESY. Labs & Collaborations: Leads the Accelerator Physics Group at DESY, collaborates on projects like XFELO and BGO-OD beamline , and contributes to international schools (CAS) and symposia.

Matias D. CattaneoView profile

Professor

• Mathematical statistics
• Econometrics
• Data science
• +7 more

Matias D. Cattaneo is a Professor in the Department of Operations Research and Financial Engineering at Princeton University , with affiliated roles in the School of Public and International Affairs , Economics Department , Latin American Studies Program , Data-Driven Social Science , AI at Princeton , and Center for Statistics and Machine Learning . He serves as an Amazon Scholar and collaborates with global organizations. Education : Ph.D. in Economics (2008) and M.A. in Statistics (2005) from UC Berkeley, Master in Economics (2003) from Universidad Torcuato Di Tella, Licentiate in Economics (2000) from Universidad de Buenos Aires. Research focuses on interdisciplinary challenges in social, behavioral, and biomedical sciences, combining econometrics, statistics, data science, and causal inference. His methodological work includes regression discontinuity designs, synthetic control methods, and local polynomial estimation, with applications to decision-making under uncertainty. Scientific recognition : Elected Fellow of the American Statistical Association Elected Fellow of the Institute of Mathematical Statistics Elected Fellow of the International Association for Applied Econometrics Elected Member of the International Statistical Institute Software contributions include R packages rdhte , scpi , and lpcde , freely available on GitHub. His GitHub activity includes 344 contributions in the last year, with active repositories on regression discontinuity and synthetic control methods.

Feng ChenView profile

Associate Professor

• Nonparametric and semiparametric statistics
• Point processes and self-exciting processes
• Statistical computing
• +3 more

Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies

• Integrated Circuits
• Signal Processing
• Data Analytics
• +4 more

Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.

Ali ÇarkoğluView profile

Professor

• Comparative Politics
• Political Parties
• Electoral Systems
• +3 more

Dr. Ali Çarkoğlu is Professor of Political Science at Koc University in Istanbul, Turkey, specializing in comparative politics, electoral systems, and political communication. Holding a PhD from SUNY Binghamton (1994), he has previously taught at Boğaziçi and Sabancı Universities. Doctoral Degree: State University of New York-Binghamton (1994) Master's Degree: Rutgers University (1989) Bachelor's Degree: Boğaziçi University (1986) His research focuses on political polarization in multiparty systems, misinformation dynamics in authoritarian regimes, and the emotional mobilization effects of populist rhetoric. Recent studies examine partisan bias in pandemic-era conspiracy theories and electoral alliance challenges in Turkey's 2023 presidential election. Notable scientific achievements include the 2013 Wapor Best Article Award. He has developed innovative network-based approaches for measuring party polarization and conducted experimental analyses of voting advice applications' impact in constrained media environments.

Dan A. BlackView profile

Professor

• Labor Economics
• Economic Demography
• Urban Economics
• +2 more

Dan A. Black is a Professor at the University of Chicago Harris School of Public Policy , a Senior Fellow at the National Opinion Research Center (NORC) , and a Research Fellow at IZA . His work bridges labor economics, economic demography, urban economics, and applied econometrics, with a focus on policy-relevant empirical analysis. Education: BA and MA in History from the University of Kansas; MS and PhD in Economics from Purdue University. Black’s research explores labor market dynamics, including wage gaps, migration, human capital, and policy impacts. His methodological expertise spans econometrics, causal inference, and data reliability. He has also contributed to studies on racial and gender disparities, urban inequality, and public health economics. His recent publications highlight interdisciplinary trends: labor economics (training, job lock, wage disparities), demography (mortality estimation, migration), and urban economics (inequality, policy evaluation). Methodological themes include instrumental variables , matching estimators , and measurement error . Scientific Awards: Fellow of the Society of Labor Economists Senior Fellow at NORC Research Fellow at IZA Black has served on editorial boards for the Journal of Labor Economics , Labour Economics , and Journal of Urban Economics . He has advised panels for the U.S. Census Bureau, Department of Education, and Environmental Protection Agency, and consulted for governments in New Zealand, Australia, and the Inter-American Development Bank. His affiliations include the Committee on Quantitative Methods in Social, Behavioral, and Health Sciences at the University of Chicago.

Taro MienoView profile

Associate Professor

• Precision Agriculture
• Water Resource Management
• Groundwater Economics
• +7 more

Taro Mieno is an Associate Professor in the Department of Agricultural Economics at the University of Nebraska-Lincoln, specializing in precision agriculture and water resource economics. His work bridges agricultural policy, spatial econometrics, and environmental sustainability. University: University of Nebraska-Lincoln Department: Agricultural Economics Academic Rank: Associate Professor Research Interests: Precision agriculture and variable rate technologies Groundwater economics and drought resilience On-farm experimentation and data-driven decision making Agricultural policy and environmental externalities Machine learning applications in agronomic optimization Soil-water interactions and resource management Email: tmieno2@unl.edu