Iñaki Úcar is an Assistant Professor at the Department of Statistics, University Carlos III de Madrid, and a Research Fellow at the university's Big Data Institute. He directs the Master in Computational Social Science program and is an advocate for open-source software, particularly contributing to R packages like simmer , units , and quantities . His expertise spans statistical software, high-performance computing, simulation, data visualization, and network analysis. He holds an MEng in Telecommunications Engineering and an MScEng in Communications from Universidad Pública de Navarra (2011-2013), followed by an MScEng and PhD in Telematic Engineering from University Carlos III de Madrid (2014-2018). His research focuses on ML interpretability, computational social science applications (e.g., misinformation, inequality), and network dynamics. Recent work includes analyzing gender gaps in academic productivity during the pandemic and developing polynomial-based neural network frameworks. His articles highlight interdisciplinary contributions to R tooling, neural network theory, and socio-technical systems analysis. He actively organizes the Statistics Reading Club and NICDA workshops. Despite no listed formal advisees, his open-source contributions and collaborative projects reflect strong academic engagement.
Matti Vuorre is an Assistant Professor in the Department of Social Psychology at Tilburg University's School of Social and Behavioral Sciences. His research focuses on psychological functioning in digital environments, particularly the role of video games in well-being. He emphasizes statistical methods, reproducibility, and transparency in research. Education: PhD (2018) and prior degrees from Columbia University. He teaches courses like Consumer Analytics Using Big Data and Experimental Research. His work is funded by institutions like Tilburg University and the Huo Family Foundation. Research interests include metacognition, methodology, and applied statistics, with a focus on large-scale datasets and experiments. He advocates for research integrity, adhering to the European Code of Conduct for Research Integrity. Key grants include support from the Economic and Social Research Council and the John Fell Fund. He advises on research practices and promotes open science initiatives.
Dr. Bhramar Mukherjee is the Anna M.R. Lauder Professor of Biostatistics and Senior Associate Dean of Public Health Data Science and Data Equity at Yale School of Public Health, with secondary appointments in Epidemiology and Statistics. Her research focuses on developing statistical methods for complex health data integration and analysis. Her research interests include Bayesian and frequentist methods for epidemiological data, electronic health record analysis, gene-environment interactions, health disparities research, and cancer epidemiology. She specializes in integrating genetic, environmental, and clinical data to address public health challenges. Dr. Mukherjee's recent publications demonstrate extensive work on selection bias in health data, COVID-19 disparities, polygenic risk scores, and applications of AI in precision health. Her methodological research consistently addresses real-world health challenges like cancer risk prediction and pandemic response. She has received numerous honors including the Marvin Zelen Award, Jerome Sacks Award, and election to the National Academy of Medicine. Her research is supported by NIH and NSF grants focusing on data integration methods and health equity applications. Dr. Mukherjee directs the Mukherjee Lab, which develops statistical methods for complex health data. She has supervised 20 doctoral students and 3 postdoctoral fellows, and serves in leadership roles at the Yale Cancer Center.
Dr. Prashant Joshi is an Associate Professor of Finance at Saint Martin's University's School of Business. He holds a PhD in Big Data and Analytics from the University of Essex, UK, along with BE, MBA, and PGDRM degrees. Previously, he served as Dean of the Faculty of Commerce and Management at Tarsadia University (India), Director of the School of Business at UT University, and Visiting Professor at Tianjin Polytechnic University (China), Cape Breton University (Canada), and I-Shou University (Taiwan). He received the Best Professor Appreciation Award at I-Shou University. His research focuses on behavioral finance, financial econometrics, computational finance, and business analytics. He teaches courses on econometrics, time series analysis, investment management, and research methodology, leveraging tools like RATS, EViews, Python, and Tableau. He has published six international books and over 42 research papers in journals, with projects spanning human development, data analytics, and entrepreneurship. Dr. Joshi has conducted academic visits in Germany, France, Switzerland, and Luxembourg. He is certified in Data Analysis by Frontline Solver Inc. and Macroecometrics Forecasting via IMF's EDX program. His work emphasizes maximizing students' potential and fostering academic excellence through collaboration with global institutions.
Alessandra RAFFAETA' serves as Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice, where her research bridges computer science with environmental applications through advanced mobility data analytics. Her research expertise spans several interconnected domains: Mobility Data Analytics and Trajectory Pattern Recognition Semantic Trajectories and Spatiotemporal Modeling Big Data Processing for Environmental Monitoring Marine Noise Pollution and Fisheries Management Privacy-Preserving Data Mining and Ethical Considerations Urban Mobility and Tourist Behavior Analysis Dr. RAFFAETA's scholarly output demonstrates a clear evolution from theoretical foundations in constraint logic programming to practical environmental applications. Her recent work (2021-2025) shows increasing focus on marine environmental challenges, particularly underwater noise characterization through semantic trajectories and analysis of fishing activities in the Adriatic Sea. A notable trend is her growing attention to ethical dimensions of mobility data usage, reflecting the field's maturation and societal implications. Her research methodology combines advanced trajectory data warehousing techniques with machine learning to extract meaningful patterns from complex spatiotemporal datasets. She has developed specialized frameworks for visual OLAP analysis on trajectory data and semantic enrichment of mobility information, with applications ranging from marine conservation to urban planning. Dr. RAFFAETA' actively contributes to the mobility data analytics community through workshops like the International Workshop on Big Mobility Data Analytics (BMDA) and collaborative research addressing real-world environmental challenges. Her work with marine scientists on fishing vessel tracking and with urban planners on Venice tourism patterns demonstrates strong interdisciplinary collaboration. Her laboratory work likely involves trajectory data management systems, semantic enrichment tools, and spatiotemporal analysis frameworks that enable both theoretical exploration and practical environmental monitoring applications.
Dr. Fadi Hirzalla is a Lecturer at the Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam. He is also the Head of Studies at the Erasmus Graduate School of Humanities (EGSH), where he leads the development and management of the PhD curriculum. His interdisciplinary work bridges political science, communication science, and social research methodology. His research centers on citizenship, digital media, multiculturalism, and methodological innovation in social sciences. He specializes in qualitative data analysis, set-theoretic methods (particularly QCA), survey design, and statistical research. His educational contributions extend to pedagogical innovations and addressing didactical challenges in higher education. The recent publications reflect a strong interdisciplinary trend, combining political communication, smart cities, vocational education, and mental health . His work often explores how digital transformation affects governance, identity, and inclusion. There is a consistent emphasis on qualitative and mixed-methods approaches, especially in understanding complex social phenomena. Dr. Hirzalla has contributed to significant scholarly discussions through publications in journals such as Frontiers in Psychiatry , Journal of Contemporary Central and Eastern Europe , and International Journal for Research in Vocational Education and Training . While no specific scientific awards are listed, his work has garnered citations and attention in academic and public spheres, including mentions in Wikipedia and social media. He supervises PhD training at EGSH and is involved in curriculum design and research capacity building. Though no direct mention of grants or individual students is made, his leadership role suggests active involvement in research coordination and academic mentoring. His collaboration network spans public administration, psychiatry, education, and urban informatics. Dr. Hirzalla is also associated with research on smart cities, particularly through collaborations with Liesbet van Zoonen, exploring citizen engagement and data practices in urban environments. His work promotes critical and reflective approaches to technology and governance.
Dr. Daniel Melser is a Senior Research Fellow in the Department of Econometrics and Business Statistics at Monash University. He concurrently serves as Associate Director (Quantitative) at NAB and previously held roles at ANZ as Senior Stress Testing and Forecasting Manager. His research focuses on economic measurement, real estate, credit risk modeling, and climate change impacts on housing. He completed his PhD at UNSW (2004) and has held positions in academia, government (e.g., Statistics New Zealand, UK Office for National Statistics), and industry (Moody’s Analytics). Education: PhD in Economics, UNSW, 2004 Research Interests: Economic measurement and index numbers Real estate, housing, urban, and regional economics Credit risk modeling, stress testing, and macroprudential policy Climate change vulnerability in housing systems His current projects include ARC-funded work on commercial real estate price indexes and a study on housing resilience costs. He has authored over 29 peer-reviewed articles and received awards for best papers in 2016 and 2017. Grants & Projects: ARC grant: Measuring the Commercial Real Estate Sector in Australia (2022–2025) Climate Change Vulnerability in Australia’s Housing System (2025–2027) Awards: European Real Estate Society Best Paper Award (2017) Kendrick Prize for Best Paper in Review of Income and Wealth (2016) Advising & Editorial: Associate Editor, Australian Economic Papers (2021–present) His work contributes to UN Sustainable Development Goals related to climate action, sustainable cities, and responsible consumption.
HUANG Dashan is an Associate Professor of Finance and PGR Coordinator at the Lee Kong Chian School of Business, Singapore Management University (SMU). He holds a Ph.D. in Finance from Washington University in St. Louis (2013), a Ph.D. in Engineering from Kyoto University (2007), and a B.S. in Mathematics from Lanzhou University (2002). His research focuses on Asset Pricing, Behavioral Finance, Big Data, and Machine Learning, with notable contributions to portfolio optimization, factor models, and market sentiment analysis. He has received prestigious awards such as the CIRF/CFRI Research Award (2020) and the WRDS Best Paper Award (2018). His work integrates econometric methods, machine learning, and behavioral theories to analyze financial markets. Recent studies include predicting bond returns using real-time macro data, exploring presidential approval ratings' impact on stock returns, and developing dimension-reduction techniques for factor models. Huang collaborates widely, publishing in top journals like Journal of Financial Economics and Management Science . Education : Ph.D. in Finance, Washington University in St. Louis (2013) Ph.D. in Engineering, Kyoto University (2007) M.A. in Management Science, Chinese Academy of Sciences (2004) B.S. in Mathematics, Lanzhou University (2002) Awards and Recognition include multiple conference best-paper awards, emphasizing his innovative contributions to empirical asset pricing and quantitative finance. His research has been supported by grants, and he advises students on topics spanning behavioral finance and computational methods. Huang is actively involved in interdisciplinary initiatives, bridging finance with computing and social sciences. His lab develops tools like the PLS Sentiment Index and PLS Disagreement Index, widely used in academic and applied research.
Dr. Yoshiharu Maeno is a Professor at the School of Interdisciplinary Mathematical Sciences, Meiji University, where he conducts interdisciplinary research at the intersection of mathematics, data science, and social systems. His work applies advanced mathematical frameworks to real-world complex phenomena in economics, finance, epidemiology, and social networks. Position: Professor Institution: Meiji University School: School of Interdisciplinary Mathematical Sciences Research Interests Dr. Maeno's research focuses on data science and engineering, complex systems theory, and stochastic process theory applied to economics and social sciences. He specializes in modeling reaction-diffusion processes to understand the dynamics of infectious disease spread, financial crises, and misinformation dissemination on social media. His work bridges theoretical mathematics with practical applications in societal challenges. Publication Trends His recent scholarly output demonstrates a consistent focus on applying reaction-diffusion and stochastic models to complex social and economic systems. The publications span interdisciplinary domains including econophysics, computational social science, and mathematical epidemiology, showing a strong trend toward data-driven modeling of real-world dynamic processes. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants While specific students or grant details are not listed, Dr. Maeno's active research program suggests involvement in mentoring graduate students and securing research funding. His participation in events like the Econophysics Colloquium and CSH Workshop indicates collaboration within the global complexity science community. Labs and Teams Dr. Maeno is associated with research initiatives focused on complex systems and data science, likely involving collaborative teams working on interdisciplinary projects. His involvement in workshops and colloquia suggests membership in international research networks studying complexity in economic and social systems.
Chudamani Poudyal is an Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida, College of Sciences. He earned his PhD in Mathematics from the University of Wisconsin-Milwaukee in 2018 and has held prior academic positions at Tennessee Tech University and the University of Wisconsin-Milwaukee. He is also an Associate of the Society of Actuaries (ASA). Education: PhD in Mathematics, University of Wisconsin-Milwaukee, 2018 MS in Mathematics, New Mexico State University, 2013 MA in Mathematics, Central Department of Mathematics, Tribhuvan University, Nepal, 2009 BA in Mathematics, Tribhuvan University, Nepal, 2005 BEd in Mathematics Education, Tribhuvan University, Nepal, 2004 His research lies at the intersection of Actuarial Science, Robust & Computational Statistics, Statistical Learning, Big Data Analytics, and Stochastic Optimization . He develops robust statistical methods for modeling insurance loss severity under challenging data conditions such as truncation and censoring. His work emphasizes reliable parameter estimation and predictive modeling in actuarial contexts. The recent publications demonstrate a consistent focus on robust parametric estimation for heavy-tailed distributions like lognormal and Pareto, especially under data limitations. His methodologies, such as trimmed and winsorized moments, aim to improve model reliability and reduce sensitivity to outliers in insurance data. Scientific Awards: Mark Lawrence Teply Award, University of Wisconsin-Milwaukee (Spring 2018) First Place Prize, Student Presentation, 52nd Actuarial Research Conference (Summer 2017) Erasmus Mundus Europe-Asia Scholarship for ALGANT Program (Spring 2011) Dr. Poudyal actively presents his research at major actuarial and statistical conferences, including the Actuarial Research Conference (ARC), Insurance: Mathematics and Economics (IME), and International Conference on Robust Statistics (ICORS). He has delivered invited talks at institutions like Purdue University, The Ohio State University, and Tribhuvan University. Though no advisees are listed, his engagement in graduate-level teaching and research suggests involvement in mentoring. He is affiliated with professional societies including the American Statistical Association (ASA), Society of Actuaries (SoA), and Casualty Actuarial Society (CAS).
Dr. Victor Emilio Troster is an Associate Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB), where he has been teaching since 2015. His academic focus spans financial econometrics, time series analysis, and econometric theory, with particular expertise in causality tests, quantile regression models, and financial asset forecasting. He teaches undergraduate and graduate courses including Econometrics, Survey Analysis and Multivariate Techniques, and Econometrics for Big Data across various business administration and economics programs. Ph.D. in Economics, Universidad Carlos III de Madrid, Spain (2015) Dr. Troster's research interests center on advanced econometric methods applied to financial markets and economic phenomena. His work focuses on developing and applying statistical techniques to analyze financial time series data, with particular attention to causality testing in quantile frameworks, specification testing for regression models, and forecasting of financial assets. His research bridges theoretical econometrics with practical applications in finance and economics, addressing complex questions in market behavior, risk analysis, and economic policy evaluation. His methodological contributions have significant implications for both academic research and practical financial decision-making. His publication record demonstrates consistent contributions to high-impact econometrics and finance journals, with a clear trajectory of increasingly sophisticated methodological work. His research spans both theoretical developments in econometric methodology and applied studies addressing contemporary financial and economic issues. The publications show a strong focus on time series analysis, with particular emphasis on nonlinear relationships, quantile-based approaches, and cross-market spillovers. His work frequently addresses methodological challenges in financial econometrics while providing insights relevant to practitioners and policymakers. Dr. Troster serves as a thesis advisor for the PhD in Applied Economics program at UIB. He has participated in competitive research projects funded by Spain's Ministry of Science and Innovation. His teaching portfolio spans multiple degree programs including Business Administration, Economics, and specialized Master's programs in Big Data Analysis and Tourism Economics. He is an active member of the Econometrics and Data Science (ECD) Consolidated R+D+I Group at UIB, contributing to the department's research infrastructure and collaborative projects. His work integrates traditional econometric approaches with emerging data science methodologies, positioning him at the intersection of established economic theory and contemporary analytical techniques.
Viswa Viswanathan is a Professor of Computing and Decision Sciences at the Stillman School of Business, Seton Hall University. He specializes in data analytics, machine learning, artificial intelligence, and software engineering. His research focuses on leveraging IT and analytics to enhance online learning environments, particularly through intelligent tutoring systems and educational technology innovation. He has authored numerous books on R programming, SAP certification, and business analytics course materials. Research Contributions: Dr. Viswanathan has published extensively in operations research, algorithm design, and educational technology. Notable work includes stochastic greedy algorithms, constraint-based intelligent tutoring architectures, and optimization models for pooled testing during pandemics. His work bridges theoretical computing with practical applications in business education. Teaching: Teaches advanced courses like Business Applications of Machine Learning, Business Intelligence, and Big Data Analytics. Known for integrating emerging technologies like Ruby on Rails and GPT-3 into pedagogical frameworks. Professional Impact: Co-developed SAP certification guides (TS410 and TERP10) widely used in enterprise education. His books on R programming (e.g., R Data Analysis Cookbook) are standard references in data science education.
Nancy Perrin is a Professor and Director of the Biostatistics and Methods Core at Johns Hopkins University, specializing in methodological approaches for real-world research settings. She has served as co-investigator on over 30 federally funded studies across healthcare, community, and international contexts using clinical trials and observational designs. Her educational qualifications include: PhD MA BA Dr. Perrin's research focuses on statistical methodologies for complex data environments. Key expertise areas feature prominently in her work: Longitudinal study design and analysis Clustered data modeling Psychometric validation techniques Messy data handling protocols PTSD and violence-related quantitative frameworks Big Data integration in public health Her methodological innovations support diverse federally funded initiatives through rigorous statistical frameworks. As Director of the Biostatistics and Methods Core, she leads a specialized team providing analytical infrastructure for multi-site research projects requiring advanced quantitative solutions in challenging real-world conditions.
William Revelle is a Professor of Psychology at Northwestern University , where he has held various leadership roles including Department Chair. His research spans personality theory, psychometrics, cognitive ability, and affective states, focusing on interactions between biological and situational factors. He leads the Personality, Motivation, and Cognition Laboratory and developed the psych package for R, widely adopted in psychological research. International Cognitive Ability Resource (ICAR) and Synthetic Aperture Personality Assessment (SAPA) projects NSF Grant SMA-1419324 for cognitive ability research Over 1.5 million participants in SAPA data collection Research Interests include: • Biological foundations of personality and motivation • Multivariate analysis of affective dynamics • Open-source psychometric tools and statistical modeling • Temporal patterns in personality-cognition interactions Scientific Leadership : President of ARP, ISSID, and ISIR Board member, Bulletin of the Atomic Scientists Keynote speaker at global conferences Teaching Legacy : • Courses in Research Methods, Psychometric Theory, and Structural Equation Modeling • Pioneering use of R software for psychological analysis • Development of interactive data collection platforms
Ke Yi is a Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), where he also serves as Director of the MSc Program in Big Data Technology. His research spans database theory and systems, query processing, data security and privacy, parallel and distributed algorithms, and computational geometry, with a focus on bridging theoretical guarantees with practical implementations. Dr. Yi earned his B.Eng. in Computer Science and Technology from Tsinghua University (1997-2001) and his Ph.D. in Computer Science from Duke University (2001-2006), advised by Professors Pankaj K. Agarwal and Lars Arge. Before joining HKUST in 2007, he was a Research Specialist at AT&T Labs-Research (2006-2007). His research interests center on database theory and systems with particular emphasis on query processing techniques, data security and privacy mechanisms, and efficient algorithms for big data. Yi's work consistently demonstrates the rich interdependence between theoretical foundations and practical implementations, favoring simple algorithms with elegant analyses that provide valuable insights for real-world applications. His research group has developed several notable system prototypes including Quorion (query optimization), DPSQL (differentially private SQL), SparkSQL+ (next-generation query planning), SecYan (secure query processing), CROWN/Cquirrel (continuous query processing), and XDB (online aggregation). Yi's publication record shows a clear evolution toward privacy-preserving database technologies, particularly differential privacy, with an increasing focus on practical implementations that maintain theoretical guarantees. His recent work has centered on query processing under differential privacy, secure multi-party computation for databases, and efficient algorithms for big data analytics, demonstrating consistent contributions to both theoretical foundations and practical systems. ACM SIGMOD Best Paper Award (2016, 2022) ACM PODS Test-of-Time Award (2022) ACM Distinguished Member (2021) Multiple ACM SIGMOD Best Paper Honorable Mentions Google Faculty Research Award (2010) HKUST School of Engineering Young Investigator Research Award (2012) Professor Yi has supervised numerous Ph.D. and MPhil students, many of whom have gone on to prestigious academic and industry positions at institutions including Nanyang Technological University, University of Waterloo, EPFL, Alibaba Cloud, and Google. His research has been generously supported by Hong Kong RGC, Alibaba, Huawei, ByteDance, Microsoft, and Google. In addition to his research leadership, Yi has made significant contributions to the academic community through editorial roles (ACM TODS, IEEE TKDE), program committee chairs (PODS 2026, ICDT 2021), and numerous service roles in top database conferences. His laboratory, the HKUST Database Lab (hkustDB), has become a leading center for database research in Asia, developing innovative prototypes that bridge theoretical database research with practical implementations. The lab maintains active GitHub repositories for their open-source projects and collaborates extensively with both academic and industry partners worldwide.
