Akanksha Negi is a Lecturer (Assistant Professor) in the Department of Econometrics and Business Statistics at Monash University, Australia. Her academic journey includes a PhD in Economics (2020) from Michigan State University, preceded by a BS and MS in Statistics from the University of Delhi, India. Education: PhD in Economics, Michigan State University (2020) BS and MS in Statistics, University of Delhi (India) Research Interests focus on Econometrics, particularly Causal Inference and Experimental Design. Her work addresses methodological challenges in treatment effect estimation, including misclassification, heterogeneity, and missing data, with applications to agricultural economics and network transaction costs. Trends in Publications highlight contributions to Difference-in-Differences, Doubly Robust Estimation, and M-Estimation frameworks. She also explores dynamics in agricultural markets, aligning with UN Sustainable Development Goals (SDGs) related to poverty reduction and economic prosperity.
Rosa Weber is a PartTime Lecturer at the Department of Sociology, Stockholm University, and a Principal Investigator for the Segregation Across Domains project funded by the Swedish Research Council for Health, Working Life and Welfare (4.8m SEK, 2023). She holds a PhD in Sociology from Stockholm University (2020) and has conducted postdoctoral research at the French Institute for Demographic Studies (3GEN project) and Sweden’s social sciences sector. Education: PhD in Sociology (2020) Stockholm University MSc in Sociology (2014) London School of Economics BA in Serbian/Croatian and Eastern European Studies (2013) University College London Her research examines social stratification, ethnic inequalities, and migration dynamics across Sweden, Finland, and France. Using administrative data and surveys like Trajectories and Origins 2 (TeO2) and CILS4EU, she analyzes: Workplace and neighborhood segregation impacts on refugee integration Gender-specific labor market barriers for migrants Education-occupation mismatch across migrant generations Circular migration patterns in Nordic free-mobility frameworks Deportation policy effects on migrant financial behavior Key findings include: Male migrants benefit more from social contacts than women in labor markets Refugee children show consistent integration patterns across life domains Increased deportations shift Mexican migrant financial strategies toward remittances Swedish 'Ghetto' policies worsen socio-demographic outcomes Education-occupation vertical mismatch declines across generations in France Her work appears in Demography , Research in Social Stratification and Mobility , and European Journal of Population . She actively participates in migration research networks and policy discussions, maintaining collaborations with institutions like Sciences Po and CREST.
Yingyao Hu is the Krieger-Eisenhower Professor of Economics and Vice Dean for Social Sciences at Johns Hopkins University. He holds a PhD in Economics from Johns Hopkins University (2003) and has been affiliated with the university since 2007. Previously, he was an assistant professor at the University of Texas at Austin (2003–2007). His research focuses on econometrics, empirical industrial organization, and labor economics, with a particular emphasis on measurement error models, latent variables, and dynamic discrete choice models. He has contributed to leading journals like American Economic Review , Econometrica , and Journal of Econometrics . Professor Hu has pioneered methods in nonparametric identification and estimation, addressing challenges in unobserved variables and misclassification errors. His work on labor economics includes correcting biases in unemployment rate measurements and analyzing China’s economic dynamics. He is a Fellow of the Journal of Econometrics and co-edited a special issue on measurement errors. Awards include the Denis J. Aigner Award (2022–2023) and the Kuznets Prize (2010–2012). Education: PhD (2003), MA in Economics (2001), MSE in Mathematical Sciences (2001) – Johns Hopkins University; Tsinghua University (B.E., 1994); Fudan University (M.A., 1997). Research Interests: Machine learning applications in econometrics, production function estimation, and policy analysis using satellite data. Awards: Denis J. Aigner Award, Kuznets Prize. Grants & Leadership: Oversees faculty policies as Vice Dean; coordinates departmental chair rotations and faculty mentoring. His lab, the JHU Workshop on Measurement Errors & Latent Variables, hosts interdisciplinary collaborations. Hu’s work bridges econometric theory with practical applications, influencing policy and empirical analysis in labor markets, industrial organization, and macroeconomic growth.
Giuseppina Guagnano is an Associate Professor at the Department of Methods and Models for Economy, Territory, and Finance at the University of Rome "La Sapienza". Her research focuses on measurement error models, two-step models, social capital, and tax evasion. Research Interests: Measurement error models, Two-step models, Social capital, Tax evasion Recent Publication Trends (2019–2025): 8/15 articles address undeclared work, tax evasion, and measurement error. 5/15 focus on social capital's impact on demographics and policy. 3/15 analyze social theater projects through quantitative surveys. Teaching: Leads courses in Statistics and Statistical Models for Business, with detailed grading policies and office hours (Tuesday 11:00–13:00). Provides extensive course materials on e-learning platforms, including R code, datasets, and lecture slides.
Dr. Mary Playdon is an Assistant Professor in the Nutrition & Integrative Physiology department at the University of Utah , with an adjunct appointment in Population Health Science. Her research focuses on understanding the role of diet and obesity in cancer development and prognosis, leveraging metabolomics to identify dietary biomarkers and metabolic pathways underlying cancer risk. Education & Professional Background: She holds a PhD in Chronic Disease Epidemiology from Yale University (2016), an MPH from Queensland University of Technology (2010), and a BS in Nutrition and Dietetics (2003). Her postdoctoral training at the National Cancer Institute (2018) further specialized her in metabolic epidemiology. Research Interests: Dr. Playdon’s work integrates metabolomics, epidemiology, and clinical studies to address gaps in dietary measurement accuracy. Key areas include: Developing dietary biomarkers for objective assessment of food intake Examining metabolic dysregulation (e.g., ceramides) in obesity-related cancers Studying gut microbiome interactions with eating behaviors Evaluating lifestyle interventions (diet, fasting, exercise) for cancer survivors Grants & Leadership: She leads multiple NIH-funded grants, including studies on ceramides in colorectal cancer risk and metabolic syndrome. She chairs the Diet Working Group in the NCI’s Metabolic Dysregulation and Cancer Risk (MeDoc) Consortium and participates in the NIH Consortium of Metabolomics Studies (COMETS). Recent Contributions: Over 80 peer-reviewed articles, with recent emphases on malnutrition in cancer patients, overnight fasting effects on weight, and the role of microbiome diversity in gastrointestinal cancers.
Emmanuel Mamatzakis is a Professor of Finance at the Birkbeck Business School, University of London, specializing in Accounting and Finance. He serves as Program Director of MSc Accounting and Finance and Director of the Accounting and Finance Research Centre. His academic leadership extends to the Accounting and Finance Subject Area Research Cluster where he acts as Lead Researcher. His educational background includes a DPhil in Economics from the University of London (Queen Mary College, 1999), an MSc in Economics from the University of Warwick (1994), and a BSc in Economics from the National and Kapodistrian University of Athens (1993). Mamatzakis' research focuses on empirical studies in accounting, banking, finance, and public finance with significant real-world economic impact. His primary research areas include International Macroeconomics, Applied Econometrics, and the intersection of culture, institutions, and financial reporting quality. Recent work examines how household debt behavior changed during the pandemic, the relationship between environmental performance and financial reporting, and the effects of board diversity on financial misreporting. His publication trends reveal a strong emphasis on methodological sophistication, particularly Bayesian modeling and neural networks applications to financial problems. Recent articles demonstrate growing interest in environmental, social, and governance (ESG) factors, cultural influences on financial reporting, and the economic consequences of geopolitical events like the Russian invasion of Ukraine. Fellow of the Higher Education Academy (November 2021) Mamatzakis has secured significant research funding, including a £102,974 ESRC grant for his project on household debt repayments during the COVID-19 pandemic. He actively supervises PhD students in banking, finance, international macroeconomics, applied accounting, and public finance. As an external examiner, journal reviewer (for ABS 3 and 4 journals), and reviewer for Commonwealth Scholarships and ESRC projects, he contributes extensively to the academic community. He holds influential positions including Board Member of the Hellenic Fiscal Council and participation in the European Securities and Markets Authority's Investor Trends and Research Working Group, connecting his research directly to policy formulation.
Rena Jones, PhD, MS is an Adjunct Assistant Professor in the Environmental Health Sciences department at Yale School of Public Health . Her research focuses on applying GIS and innovative methodologies to assess environmental exposures, particularly air and water pollutants, and their health impacts through epidemiologic studies . Her work addresses spatial variability in exposure-disease relationships, exposure misclassification, and long-term environmental carcinogen effects. Education : PhD and MS in Epidemiology from SUNY Albany School of Public Health Postdoctoral Training : National Cancer Institute (Cancer Epidemiology) Dr. Jones’ research interests span several critical areas in public health: Environmental exposure assessment using geospatial tools Cancer risk tied to pollutants (air, water) Health disparities linked to industrial and agricultural exposures Methodological rigor in spatial error and exposure modeling Long-term impact of mixed contaminant exposure Her recent publications (2024-2025) analyze associations between industrial emissions, water contaminants, and cancers (lung, endometrial, ovarian, breast) using large U.S. cohorts. Collaborative work explores COPD risk from ozone, greenspace effects on sleep, and wildfire exposure patterns. Key keywords include environmental epidemiology, GIS, exposure science, and health equity. Dr. Jones collaborates with researchers like Nicole Deziel , Ning Sun , and Vasilis Vasiliou on interdisciplinary projects. She has no listed scientific awards in the provided text but contributes to advancing environmental health policy through data-driven insights.
Mahsa Ghasemi is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette. She holds a B.Sc. in Mechanical Engineering from Sharif University of Technology (2014), an M.S.E. in Mechanical Engineering from The University of Texas at Austin (2017), and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2021). Her research focuses on task-oriented knowledge acquisition, online learning and control, human-robot interaction, trustworthy AI, and socially beneficial autonomy. She is affiliated with the Materials and Electrical Engineering Building at Purdue. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2014) M.S.E., Mechanical Engineering, UT Austin (2017) Ph.D., Electrical and Computer Engineering, UT Austin (2021) Her research interests span interdisciplinary areas including reinforcement learning, causal inference, control systems, and human-autonomy collaboration. Recent work emphasizes resilient cyber-physical systems, privacy-preserving multi-agent learning, and causal discovery in decision-making frameworks. Her articles address challenges in sensor selection, no-regret learning in bandits, and formal methods for autonomous systems. Publications highlight contributions to submodular optimization in hypothesis testing, robust sensor scheduling in intrusion detection, and adaptive experimental design for causal discovery. Her work bridges theoretical foundations with practical applications in robotics, cybersecurity, and AI ethics. No scientific awards or grants are explicitly listed in the provided data. She advises no listed students but collaborates on projects involving diverse planning and decision-making in constrained environments.
Yoonkyung Lee is a Professor of Statistics and Computer Science Engineering at The Ohio State University, affiliated with the Department of Statistics in the College of Arts and Sciences. She holds a PhD from the University of Wisconsin-Madison (2002). Her primary research focuses on statistical learning and multivariate analysis, with specializations in classification, kernel methods, and model stability. She has a courtesy appointment in Computer Science and Engineering since 2016 and served as a faculty co-director of the Translational Data Analytics Institute (2020–2022). Her work has been funded by the National Science Foundation (NSF), and she was elected a Fellow of the American Statistical Association in 2015. Her educational background includes a PhD in Statistics from the University of Wisconsin-Madison. Her research interests emphasize developing methodologies for latent structures in multivariate data, computational frameworks for model stability, and predictive modeling. Notable contributions include advancements in kernel discriminant analysis, Bayesian restricted likelihood methods, and sparse logistic tensor decomposition. Prof. Lee serves on editorial boards for journals such as Chemometrics and Intelligent Laboratory Systems , Econometrics and Statistics , and Journal of Machine Learning Research . Her articles span topics like support vector machines, quantile regression, and nonlinear embeddings, reflecting her expertise in bridging statistics and machine learning. Beyond research, she has advised numerous projects and contributed to interdisciplinary initiatives in translational data analytics.
Dr. Eric Boahen is an Associate Professor and Cluster Lead for Accounting, Finance, and Economics at the Royal Docks School of Business and Law, University of East London. He holds a PhD from the University of Sussex and is a Chartered Accountant with dual postgraduate degrees from the University of Hull and Henley Business School. His expertise spans financial reporting, corporate governance, earnings management, and the intersection of religion/culture with business practices. Education: BSc Accounting (University of Ghana), MBA (University of Hull), MSc (Henley Business School), PhD (Sussex University) Certifications: SFHEA (Senior Fellow), CMBE (Certified Management & Business Educator) His research explores how legal environments, cultural factors, modern slavery, and gender influence financial reporting quality. Recent work includes studies on litigation environments' impact on gender diversity in finance and religion's role in earnings management. He teaches advanced financial reporting and performance management at both undergraduate and postgraduate levels. Prior roles include Course Director for BSc Accounting & Finance at UEL, Curriculum Director in further education, and Audit Senior at PwC. He currently holds external roles at Coventry University and the University of Chichester.
Prof. Dr. Dr. Hanjo Hamann is a Professor at the Faculty of Law, Free University of Berlin, where he actively contributes to the Empirical Legal Studies Center (FUELS). His work focuses on advancing empirical methodologies within legal scholarship, with particular emphasis on scientometrics, digitalization of law, and interdisciplinary research approaches. His research interests span Empirical Legal Studies , Scientometrics in Law , and Machine Learning Applications in Legal Codification . Hamann has pioneered investigations into Scopus database misclassifications of German law journals, developed correspondence studies for detecting structural discrimination, and explored computational linguistics applications for legal text analysis. His work bridges traditional legal scholarship with innovative quantitative methods. Recent publications demonstrate strong trends toward digital legal scholarship and empirical validation of legal theories . His articles frequently address methodological challenges in measuring legal phenomena, with increasing focus on machine learning applications and big data approaches to legal text analysis. Hamann's work shows consistent engagement with European and transatlantic scholarly networks. Co-organized FUELS lecture series since December 2019 Contributed to European Empirical Legal Studies conferences Co-edited online symposia on legal didactics and empirical turns Authored influential critiques of scientometric methodologies in legal research Hamann actively participates in international academic networks, particularly through the Conference on Empirical Legal Studies in Europe (CELS/CElse). His collaborative work connects German legal scholarship with broader European and American research communities, focusing on methodological innovation and empirical validation of legal theories.
Juste Goungounga is an Associate Professor of Biostatistics and Health Data at the French School of Public Health (EHESP) and a researcher at the ARENES laboratory (UMR CNRS 6051) within the INSERM U1309 "Research on Health Services and Management" (RSMS) team. He previously worked at the Burgundy Digestive Cancer Registry/University of Burgundy (EPICAD Team - UMR 1231) as a postdoctoral researcher. Education: Doctor of Medicine (University of Ouagadougou), Master of Public Health (Aix Marseille University), PhD in Clinical Research and Public Health (Aix Marseille University) His research focuses on statistical methods in cancer epidemiology and non-communicable diseases (NCDs) , particularly: Cure models and time-to-cure estimators Excess hazard modeling (cluster heterogeneity, bias correction) Disease mapping techniques (Bayesian hierarchical models, cluster detection) Supervised classification methods (CART, PLS regression) R package development (xhaz) Application to population registries and clinical trials His work addresses health inequalities through quantitative frameworks, analyzing dynamics of NCD outcomes across socioeconomic and geographic dimensions. Articles highlight methodological innovations in survival analysis , spatial statistics , and clinical trial bias correction . Teaching and mentorship activities include: Lecturer in biostatistics and epidemiology Statistical programming instruction (R) Supervision of public health trainees He is affiliated with scientific societies such as the French Statistical Society (SFDS) , International Biometric Society , and International Society for Clinical Biostatistics (ISCB) . Current institutional affiliations include the Department of Quantitative Methods in Public Health (METIS) and the ARENES laboratory (UMR 6051) at Inserm U1309 RSMS team.
Dr. Gioia Mosler is a researcher at the Centre for Genomics and Child Health , part of the Faculty of Medicine and Dentistry at Queen Mary University of London . Holding a PhD in Environmental Epidemiology from Imperial College London, she leads the Global Research Group for Child Health and drives its community engagement initiatives. Her work focuses on respiratory and environmental health challenges in the UK and Africa, with projects including the MAIS (My Asthma In School) study, ACACIA (Achieving Control of Asthma in Children In Africa), CAPPA (Children’s Air Pollution Profiles in Africa), and the Children’s Environmental Health Clinic . Email: g.mosler@qmul.ac.uk Website: LinkedIn Profile Research Interests: Global respiratory health, particularly asthma in diverse populations Paediatric environmental health, focusing on air pollution exposure Behavioural health interventions using theatre, film, and games Research Trends: Her recent publications (2019-2024) emphasize asthma management in adolescents, exposure assessment to particulate matter in urban settings, and innovative community engagement methods like drama-based education. Studies span sub-Saharan Africa and the UK, addressing diagnostic gaps, pollution sources, and school-based interventions. Supervision: She supervises Andres Aharhel Mercado Velazquez and others in child health research.
Professor Baljit Sidhu is a Professor and Deputy Head (Education Innovation) at the University of Sydney. His academic background includes an MCom from the University of Otago and a PhD from the University of Sydney, with professional qualifications as a Fellow of CPA Australia (FCPA) and CPA. He teaches courses such as ACCT3011 Financial Accounting B, ACCT3013 Financial Statement Analysis, and BUSS4112 Accounting Honours A. His research focuses on accounting standards, climate risk, financial reporting, audit quality, and corporate disclosures. Notable contributions include studies on climate risk externalities in supply chains, the impact of IFRS adoption, and the role of independently certified industry-specific disclosures. Current research includes a project on 'Climate Change and Capital Markets' with student Sruthi Shanmuga Subramanian. He has secured grants such as the 2021 Australian Research Council Linkage Project on Climate Risk Disclosure. His work spans over two decades, with publications in journals like Journal of Accounting Literature , Accounting and Finance , and Abacus . Key themes include regulatory compliance, financial transparency, and the intersection of environmental factors with accounting practices. Professor Sidhu’s expertise is reflected in his extensive publication record and engagement with contemporary issues like ESG factors, sustainability accounting, and the implications of industry-specific reporting frameworks. His teaching and research emphasize innovation in education and practical applications of financial reporting principles.
Harvey A. Goldstein is a researcher specializing in statistical methodologies applied to education, public health, and data science. He has contributed to advancements in multilevel modeling, data linkage techniques, and the analysis of longitudinal data. His work focuses on addressing measurement errors, improving educational accountability systems, and enhancing the accuracy of linked healthcare data. Goldstein's research often intersects with policy, particularly in evaluating school performance metrics and addressing disparities in educational outcomes. He has collaborated on studies involving mindfulness interventions for educators, data anonymization methods, and the statistical analysis of birth cohort studies. His expertise also extends to the challenges of linking large administrative datasets and ensuring their reliability for research purposes.
