Wooyong Lee is a Lecturer in the Economics Discipline Group at the UTS Business School, University of Technology Sydney. He holds a PhD in Economics from the University of Chicago (2020), an MS in Statistics from the University of British Columbia (2014), and a BA in Economics and Statistics from Korea University (2012). His research focuses on econometrics and applied microeconomics, specializing in panel data methods, difference-in-differences frameworks, and dynamic models. He has developed methodologies addressing spillover effects in staggered DiD designs and partial identification in heterogeneous coefficient models. His work applies to real-world issues like lifecycle earnings dynamics and policy evaluation. Lee teaches econometrics at undergraduate and postgraduate levels and supervises research students. His publications appear in venues such as Statistical Inference for Stochastic Processes and peer-reviewed working papers. Research interests emphasize causal inference techniques, with contributions to handling unobserved heterogeneity and measurement errors in economic data. Ongoing work explores dynamic treatment choice models where treatment decisions respond to outcome shocks, challenging traditional parallel trends assumptions.
Werner Ploberger is Professor of Economics and Thomas H. Eliot Distinguished Professor in Arts & Sciences at Washington University in St. Louis. His research focuses on econometric methodology, statistical analysis, and time-series econometrics. He previously held positions at Vienna University of Technology, University of St. Andrews, and University of Rochester. His influential publications develop statistical tests for structural breaks in economic models, including CUSUM test modifications and stability assessments for regression models. Committee service includes PhD Admissions and PhD committees at Washington University.
Nikolai Roussanov is the Moise Y. Safra Associate Professor of Finance at the Wharton School, University of Pennsylvania, and a Faculty Research Fellow at the National Bureau of Economic Research. His research spans asset pricing, econometrics, household finance, and macroeconomics, with a focus on market dynamics and behavioral economic factors. His research interests include: Asset pricing anomalies and risk factor modeling Household financial decision-making under uncertainty Macroeconomic impacts on commodity and currency markets Behavioral finance and mental accounting mechanisms Recent publications analyze inflation risks across asset classes, corporate bond valuation, behavioral retirement strategies, and the role of leisure economics in declining work hours. His work frequently integrates empirical finance and econometric methodologies. Scientific contributions include: Faculty Research Fellow, National Bureau of Economic Research His scholarship bridges technical financial modeling with real-world economic phenomena, covering topics like oil price shocks, mortgage liquidity, and systemic market failures.
Dr. Tarek Sayed is a Professor at the University of British Columbia's Department of Civil Engineering within the Faculty of Applied Science. He specializes in Transportation Engineering, focusing on road safety analysis, traffic operations, and Intelligent Transportation Systems (ITS). He serves as the Director of the Bureau of Intelligent Transportation Systems and Freight Security (BITSAFS-Engineering) and Editor of the Canadian Journal of Civil Engineering . His research addresses three core areas: improving road safety evaluation techniques, enhancing safety through traffic operations and highway design analysis, and advancing ITS technologies. Notable contributions include frameworks for safety-audits of infrastructure projects like the Sea to Sky Highway and methodologies for evaluating transit signal priority systems in Vancouver. He has authored/co-authored over 250 publications and supervised 60 graduate students. Key Roles: Professor, BITSAFS Director, Journal Editor Awards: Tier 1 Canada Research Chair, Wilbur Smith Award, Sandford Fleming Award, Prince Michael International Road Safety Award Consulting: Global projects in traffic safety and ITS for agencies like ICBC, FHWA, and Ashghal His research integrates Bayesian statistical methods, extreme value theory, and machine learning to model traffic conflicts, pedestrian behavior, and safety interventions. Current projects include real-time safety optimization using autonomous vehicle data and analyzing shared space interactions between cyclists and pedestrians. Dr. Sayed chairs national/international committees, including the U.S. Transportation Research Board’s safety data committee. His work bridges academia and practice, influencing policy and infrastructure investment decisions worldwide.
Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Gareth Roberts is an Associate Professor in the Department of Linguistics at the University of Pennsylvania, where he serves as Graduate Chair. He is also a faculty member of the Psychology Graduate Group, founder and director of the Cultural Evolution of Language Lab, and co-director of the Social and Cultural Evolution Working Group at Penn. Roberts earned his PhD in Linguistics from the University of Edinburgh in 2010, following an MSc in Evolution of Language and Cognition (2006) and a BA in German and Russian (2003) from the University of Nottingham. His academic journey includes postdoctoral positions at Yeshiva University and the University of Stirling before joining Penn as an Assistant Professor in 2014, where he was promoted to Associate Professor in 2022. His research focuses on the role of social and communicative pressures in shaping language emergence and evolution. Roberts investigates fundamental questions about linguistic variant spread, phonological system structuring, and how communication shapes linguistic structure. His work bridges linguistics, cognitive science, and cultural evolution through innovative experimental approaches using artificial languages and laboratory simulations. Analysis of Roberts' recent publications reveals a consistent focus on experimental semiotics, with particular attention to social biases in language evolution, phonological organization, indexicality emergence, and the dynamics of linguistic variation. His work often combines computational modeling with human experiments to isolate specific mechanisms driving language change. Linguistic Society of America Cognitive Science Society Philological Society Cultural Evolution Society Roberts has successfully secured substantial research funding including an NSF PAC Grant ($102,648), Penn URF Research Grants ($12,155), MindCORE initiative grant ($600,000), and multiple smaller grants totaling over $20,000. His lab currently includes researchers investigating diverse topics from case and gender marking emergence to AI agent effects on group dynamics, linguistic and genetic data in British history, and phonological space organization. The Cultural Evolution of Language Lab, which Roberts founded and directs, conducts cutting-edge research using experimental semiotics methodologies. Current projects examine how social factors influence language change, the emergence of linguistic structure through communication, and the interaction between iconicity and combinatoriality in communication systems.
Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.
Steve Mussmann serves as an Assistant Professor in the School of Computer Science at the Georgia Institute of Technology, where he joined in Fall 2024. His research centers on data-centric machine learning, with emphasis on active labeling, data selection, and adaptive experimental design methodologies. He maintains active collaborations through Georgia Tech's Foundations of AI (FoAI) and ML@GT research groups. Mussmann earned his PhD in Computer Science from Stanford University in 2021 under Percy Liang's supervision, following a BS in Math, Statistics, and Computer Science from Purdue University in 2015. His professional trajectory includes a machine learning researcher role at Coactive AI and an IFDS postdoctoral fellowship at the University of Washington's Paul Allen School of Computer Science and Engineering. His research program investigates theoretical and practical aspects of data efficiency in machine learning systems, particularly focusing on active learning frameworks, statistical properties of data algorithms under concept drift, and task specification via prompts or demonstrations. Current projects address challenges in label-efficient training of large language models and multimodal dataset development. Analysis of his 15 most recent publications reveals a consistent focus on advancing data-centric methodologies, with increasing emphasis on large-scale applications like multimodal datasets and language model fine-tuning. His work bridges theoretical guarantees in experimental design with practical frameworks like LabelBench for benchmarking label efficiency. Mussmann has received recognition through the IFDS postdoctoral fellowship. His contributions to the field include foundational work on active learning theory and data selection algorithms. IFDS postdoctoral fellow He currently advises five graduate students including PhD candidates Kangping Hu (CS) and Hangyu Zhou (ML), alongside MS students Kabir Kang and Kalp Vyas, and undergraduate Saloni Bedi. Former advisee Wei-Liang (Edison) Liao completed BS research under his supervision. His teaching portfolio includes graduate courses CS 7545 (Machine Learning Theory) and CS 8803-DML (Data-centric Machine Learning). Mussmann operates within Georgia Tech's Foundations of AI initiative and ML@GT collective, which provide infrastructure for large-scale data-centric research. His lab develops open-source tools like LabelBench for reproducible evaluation of data selection techniques, with ongoing projects exploring video data exploration systems and adaptive finetuning frameworks for foundation models.
Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.
Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.
Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.
Peng Zhao is a tenure-track Assistant Professor in the Department of Applied Economics & Statistics at the University of Delaware, with affiliations to UD's Data Science Institute. His academic career includes a postdoctoral research position at Texas A&M University's Department of Statistics (August 2020–June 2023), following his Ph.D. training at Florida State University. Education Ph.D. in Statistics, Florida State University, 2020 B.S. in Statistics, Beijing Institute of Technology, 2015 Dr. Zhao's research focuses on cutting-edge statistical methodologies, including: High-dimensional statistical modeling and inference Network-based statistical analysis Multivariate data processing techniques Scalable Bayesian computational methods Nonparametric Bayesian approaches Dependency-aware statistical learning frameworks Optimization algorithms for complex models Regularization mechanisms in statistical learning He teaches graduate-level courses in regression analysis (STAT611) and mathematical statistics (STAT602) at the University of Delaware. Office location: Room 214, Townsend Hall, 531 S. College Avenue, Newark, DE 19716.
Anastasia Semykina is a Professor of Economics and Deputy Dean (Research and Innovation) at RMIT University's School of Economics, Finance & Marketing. She holds a PhD from Michigan State University (2006) and previously served as Charles and Joan Haworth Professor of Economics at Florida State University. Her expertise spans theoretical and applied econometrics, with a focus on panel data models, missing data estimation, and their application in labor economics, education economics, transition economies, and economic psychology. She teaches advanced econometrics and microeconomics courses at both undergraduate and graduate levels. Research Interests: Theoretical and Applied Econometrics Labor Economics Economics of Education Transition Economies Economics and Psychology Health Economics Her recent publications address topics such as panel data methodologies, healthcare cost-effectiveness analysis, and educational policy evaluation. She is actively involved in supervising PhD and Master's research students in econometrics and applied economics.
