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Pedro Miguel Soares BrincaView profile

Associate Professor

• Macroeconomics
• Business Cycle Accounting
• Dynamic Stochastic General Equilibrium Models
• +7 more

Pedro Miguel Soares Brinca is an Associate Professor at the NOVA School of Business and Economics (Nova SBE), where he has been a faculty member since Fall 2015. He holds a PhD in Economics from Stockholm University and is actively engaged in research, teaching, and consultancy. He is accepting PhD students and maintains external academic affiliations. PhD in Economics, Stockholm University (2013) Master in Economics, Stockholm University Bachelor in Economics and Business, Stockholm University His research focuses on macroeconomic modeling, particularly business cycle fluctuations using dynamic stochastic general equilibrium (DSGE) models and the impact of micro-heterogeneity on macroeconomic aggregates. He has contributed significantly to the field of Business Cycle Accounting and has published in leading journals such as the Journal of Monetary Economics and Macroeconomic Dynamics . His recent work explores asset liquidity, fiscal policy, and price personalization in digital markets. The trends in his recent publications (2024–2025) reflect a continued focus on business cycle analysis, methodological reviews in macroeconomics, and emerging topics like algorithmic pricing and socio-economic inequality. His interdisciplinary approach combines theoretical modeling with empirical validation, often applied to real-world policy issues. Notable scientific contributions include: Chapter in the Handbook of Macroeconomics (2nd edition) on Business Cycle Accounting Co-authored research on Mexican business cycles and asset liquidity Contributions to understanding labor demand in sports and economic inequality Pedro Brinca has supervised PhD students, including Valter Nóbrega, and has participated in consultancy projects on consumer credit, labor market requalification, and non-performing loans in Portugal. He teaches Macroeconomics at the undergraduate level and Economics for Managers in executive education programs such as The Lisbon MBA. He has also been involved in public engagement, contributing to media discussions on economic growth forecasts and World Savings Day.

• Labor Economics
• Flexible Labor Contracts
• Wage Determination
• +5 more

Dr. José Carreño Bustos is a Researcher at the Department of Economics within the Tilburg School of Economics and Management (TiSEM) at Tilburg University. His academic work focuses on labor economics, particularly examining the dynamics of flexible labor contracts and their implications for wage determination and macroeconomic stability. His research utilizes comprehensive worker-firm panel data from the Netherlands and Eurozone countries, applying advanced econometric techniques to address critical questions about labor market institutions. Dr. Carreño Bustos completed his Doctor of Philosophy at Tilburg University in March 2024 under the supervision of Professor Harry Huizinga and Dr. Burak Uras. His doctoral dissertation, titled "Three essays on wage compensation and flexible contracts," represents a significant contribution to understanding labor market dynamics in dual labor market economies. Dr. Carreño Bustos' research primarily investigates how flexible labor contracts affect wage structures, business cycle dynamics, and overall economic welfare. His work examines the interaction between ICT capital and skill complementarity on finance wage premiums, the stabilization role of flexible contracts during business cycles, and the macro welfare implications of flexible labor arrangements in currency union economies. He employs sophisticated modeling approaches including New Keynesian frameworks and Dynamic Stochastic General Equilibrium (DSGE) models to analyze these complex phenomena. Analysis of Dr. Carreño Bustos' publications reveals a consistent research trajectory focused on understanding the nuanced effects of flexible labor arrangements. His work demonstrates that while flexible contracts can serve important macroeconomic stabilization functions, they also create significant employment instability for flexible workers themselves. His research particularly emphasizes the context-dependent nature of these effects, showing how optimal labor market flexibility varies substantially between currency union economies like the Eurozone and countries with independent monetary policies. Dr. Carreño Bustos has made significant contributions to understanding the finance wage premium in the Netherlands, demonstrating how ICT capital spending and high-skilled worker proportion significantly diminish these premiums. His work provides essential policy insights for countries with dual labor markets, revealing a non-monotonic relationship between welfare and the prevalence of flexible contracts.

Varun Ravi VarmaView profile

Research Fellow

• Reinforcement Learning
• Explainable Artificial Intelligence
• Algorithmics

Varun Ravi Varma is a PhD Research Fellow in the Department of Information and Communication Technology at the University of Agder, Norway, with contact details including email varun.ravi.varma@uia.no and office A2122 in Grimstad. His research expertise spans: Reinforcement Learning (decision-making through trial-and-error systems) Explainable Artificial Intelligence (transparent AI decision processes) Algorithmics (efficient computational method design) Varun's work centers on analytical frameworks for small and incomplete datasets, exemplified by his 2023 SaNDA publication in Information Sciences. This research addresses critical gaps in data science where traditional methods fail with limited or imperfect data, contributing to more resilient AI applications across domains requiring robust analysis under constraints. His single verified publication demonstrates focused output in high-impact venues, while international co-authorship confirms cross-border research engagement. As a doctoral candidate, he operates within the university's research infrastructure without independent supervision capacity. Varun's position as a Research Fellow indicates institutional funding for his doctoral work, though specific grants aren't detailed. He contributes to the Department of Information and Communication Technology's research ecosystem in artificial intelligence and data science, with potential for expanding his methodological contributions to real-world data challenges.

Toomas HinnosaarView profile

Associate Professor

• Economics
• Game Theory
• Industrial Organization
• +4 more

Toomas Hinnosaar is an Associate Professor in Economics at the University of Nottingham, holding a PhD from Northwestern University. He specializes in game theory, mechanism design, and behavioral economics, with a focus on strategic interactions, market structures, and digital public goods. His research explores sequential contests, network pricing, and the impact of online information on real-world outcomes. Key findings include the role of transparency in strategic environments and the economic implications of social media dynamics. His work has been featured in major outlets like the Guardian , BBC Radio , and VoxEU . Scientific recognition includes selection for the Journal of the European Economic Association 's Virtual Issue: Editor's Choice Collection (2019). His methodological contributions span randomized field experiments, auction theory, and dynamic pricing models.

• Artificial Intelligence
• Behavioral Analytics
• Health Informatics
• +3 more

Yuchen Wang is an Assistant Professor in the Department of Management Science & Information Systems at UMass Boston . Their research bridges artificial intelligence, behavioral modeling, and data science, focusing on applications in healthcare, sustainability, and social systems. Key research themes: AI ethics in healthcare, behavioral drivers for eco-friendly technology adoption, and predictive modeling in public health/social issues Email: Not explicitly listed on public profile Recent work spans: Healthcare AI : Examining trust dynamics in medical applications Sustainability : Consumer behavior analysis for electric vehicle adoption Public Health : Methodologies for analyzing incomplete medical datasets Social Systems : Blockchain applications for online safety

Mao XiaojieView profile

Associate Professor

• Causal inference
• Data-driven optimization decision-making
• Machine Learning
• +3 more

Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .

• Industrial Organization
• Organizational Economics
• Game Theory
• +4 more

Andrew Wait is an Associate Professor at the School of Economics , University of Sydney , with a PhD from Australian National University and a Bachelor of Economics (Honours) from University of Adelaide. His research spans organizational economics, industrial organization, and game theory. Key areas: industrial organization, organizational economics, game theory, contract theory, innovation, and corporate governance. His publications focus on strategic decision-making in firms, market entry dynamics, sequential investment, and delegation models. Recent work explores diversity dynamics, deep learning applications in energy markets, and power structures in teams. Scientific contributions include co-founding the Annual Organizational Economics Workshop and collaborating with Vladimir Smirnov and Kieron Meagher on organizational structures and trust dynamics.

Pradeep MurukannaiahView profile

Associate Professor

• Natural Language Processing
• Multi-Agent Systems
• Hybrid Intelligence
• +7 more

Pradeep Murukannaiah is an Associate Professor in the Interactive Intelligence group at the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft). He co-directs the Hippo Lab, a Delft AI lab focused on AI for fair, efficient, and interpretable analysis of climate policies. He also holds leadership roles as Use Cases Coordinator and Diversity Co-Chair in the Hybrid Intelligence center, and serves as Master Coordinator for the MSc in Data Science and Artificial Intelligence Technology (DSAIT). Dr. Murukannaiah received his PhD in Computer Science from North Carolina State University in 2016. Prior to joining TU Delft, he served as an Assistant Professor at Rochester Institute of Technology (2017-2019), completed an internship at Google, and worked as a Software Engineer at Alcatel-Lucent. His research centers on engineering socially intelligent agents through three interconnected thrusts: Natural Language Processing (focusing on argument mining, value alignment, and claim analysis), Multi-Agent Systems (exploring negotiation, social choice, and multi-objective reinforcement learning), and Hybrid Intelligence (developing frameworks for human-AI synergy in decision making). Cross-cutting these areas are his investigations into values (representing what matters to stakeholders) and norms (representing expectations between stakeholders), which together form sociotechnical systems where humans interact, make decisions, and stay accountable to each other while AI agents augment human intelligence. His recent publications demonstrate a strong focus on value-sensitive AI systems, with work spanning moral frame preservation in news summarization, multi-objective reinforcement learning for climate policy analysis, and mechanisms for responsible autonomy. His research consistently bridges theoretical advances in AI with practical applications in societal decision making, particularly around climate change and democratic processes. Dr. Murukannaiah has successfully mentored numerous graduate students, including current PhD candidates Zuzanna Osika (working on explainable multi-objective decision support), Shubhalaxmi Mukherjee (focusing on fact checking using LLMs), and several recent PhD graduates whose work has contributed to the fields of opinion diversity through hybrid intelligence and context-specific value inference. As co-director of the Hippo Lab and through his leadership roles in the Hybrid Intelligence center, he actively shapes research directions that emphasize fairness, interpretability, and human-centered AI approaches for addressing complex societal challenges.

Lukas HackView profile

Lecturer

• Macroeconomics
• Monetary Economics
• Economic History
• +1 more

Lukas Hack serves as a Lecturer in the Department of Management, Technology, and Economics at ETH Zurich and maintains dual post-doctoral affiliations with ETH Zurich and the University of Mannheim. He completed his doctoral studies at the University of Mannheim in 2024, establishing himself as an emerging scholar in macroeconomic policy research with active engagements across European academic institutions. Hack holds a PhD from the University of Mannheim (2024), with prior educational milestones not specified in available materials. His research program spans macroeconomics, monetary economics, economic history, and public economics, with particular focus on real-time policy interactions. Hack investigates how monetary policy transmits through heterogeneous agents, the fiscal consequences of unindexed progressive taxation ("bracket creep"), and firm-level price-setting responses to macroeconomic news. His methodological innovations include high-frequency event studies, randomized survey designs, and novel identification strategies leveraging institutional features like FOMC voting rotations. Analysis of Hack's publication trajectory reveals three dominant research strands: (1) redefining monetary policy identification by distinguishing systematic from unsystematic components using FOMC composition; (2) documenting how inflation-driven tax bracket creep steepens the Phillips curve through labor supply channels; and (3) analyzing sovereign debt restructuring following credit rating downgrades. His work consistently bridges theoretical modeling with empirical validation using administrative data, daily firm surveys, and historical records. Hack has earned significant recognition: ETH Career Seed Award (CHF 30,000) for early-career research excellence Reinhard Selten Award for contributions to monetary-fiscal interactions As a recent PhD graduate, Hack is developing independent research capacity supported by the ETH Career Seed Award. His collaborative work with Istrefi, Meier, Rostam-Afschar, and Diebold demonstrates strong partnership networks, though formal graduate student advising is not yet documented. He actively participates in major conferences including Sailing the Macro, CEPR Annual Symposium, and SNB Research Conference, reflecting his integration into central banking research communities. Hack operates within ETH Zurich's Department of Management, Technology, and Economics infrastructure without a dedicated named laboratory, but leverages the KOF Swiss Economic Institute resources through his office at LEE F 205.

John KagelView profile

Professor

• Experimental Economics
• Behavioral Economics
• Auction Theory
• +4 more

John Kagel is University Chaired Professor of Applied Economics and Director of the Economics Laboratory at The Ohio State University, with a faculty appointment in the Mershon Center for International Studies. A Fellow of the Econometric Society and former President of the Economic Science Association, he has served on editorial boards of the American Economic Review and Experimental Economics. Education: Ph.D., Economics, Purdue University, 1970 M.S., Economics, Purdue University, 1968 M.P.I.A., Economic Development, University of Pittsburgh, 1966 B.A., Economics, Tufts University, 1964 Professor Kagel pioneered experimental economics as a core methodology. His research spans group decision-making, auction design, industrial organization, and legislative bargaining, with significant contributions to understanding individual decision-making and psychology-economics intersections. Recent work examines team vs. individual strategic behavior, package bidding in auctions, and legislative bargaining dynamics. Analysis of his 15 most recent publications reveals consistent focus on experimental validation of game theory, particularly team behavior in strategic environments, auction mechanisms, and legislative bargaining structures. His work increasingly examines gender differences, cognitive factors, and communication effects in economic decision-making. Scientific Awards: Fellow of the Econometric Society (2003) Distinguished Scholar Award, Ohio State University (2008) Chancellor's Distinguished Research Award (1997) National Fellow, Hoover Institution (1981-82) Best 1973 Western Economic Journal Article Award Distinguished Chair in Economics, University of Siena (2002) As Director of the Economics Laboratory, Kagel has secured significant NSF funding for experimental research. His consulting work includes auction design for the FCC, anti-trust litigation analysis, and patent infringement cases. He has advised numerous doctoral students and co-authored foundational texts including the Handbook of Experimental Economics (2016). His laboratory serves as a hub for experimental economics research, conducting studies on strategic interactions, market mechanisms, and behavioral phenomena with both individual and team participants.

Roderick Joseph LittleView profile

Professor

• Biostatistics
• Missing Data Analysis
• Survey Sampling
• +3 more

Roderick Joseph Little is the Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He is also a Professor in the Department of Statistics and a Research Professor at the Institute for Social Research. Dr. Little previously chaired the Biostatistics Department from January 2007 to December 2009 and from 1993 to 2001. Dr. Little earned his educational credentials from prestigious institutions: PhD in Statistics, London University, 1974 MSc in Statistics and Operational Research, London University, 1972 BA in Mathematics, Cambridge University, 1971 Dr. Little's primary research interests focus on the analysis of data with missing values and complex survey designs. His work has revolutionized methods for handling missing data, moving from ad-hoc approaches like discarding incomplete cases to sophisticated model-based methods using likelihood-based inferential techniques. He is particularly known for his work on pattern-mixture models and penalized spline of propensity prediction methods. His research also extends to model-based approaches for survey analysis that are robust to misspecification. Dr. Little's inferential philosophy is model-based and Bayesian, though he carefully considers the effects of model misspecification. His applied interests span mental health, demography, environmental statistics, biology, economics, and social sciences. Dr. Little has received numerous prestigious awards and recognitions including the Wilks' Memorial Award from the American Statistical Association, being named an ISI highly cited researcher, and election to the National Academy of Medicine. He is also a Fellow of the American Statistical Association, the American Academy of Arts and Sciences, and the Royal Statistical Society. Throughout his career, Dr. Little has chaired or co-chaired 30 doctoral committees, mentoring the next generation of statisticians. He has served in significant editorial and leadership roles, including as Coordinating and Applications Editor of the Journal of the American Statistical Association (1992-1994), co-editor of the Journal of Survey Statistics and Methodology (2016-2018), and Vice President of the American Statistical Association (2010-2012). From September 2010-January 2013, he served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau. Dr. Little is best known for his seminal book "Statistical Analysis with Missing Data" (co-authored with Donald Rubin), now in its third edition (2019). His work has had profound impacts on both theoretical statistics and practical applications across numerous scientific disciplines.

Bingkai WangView profile

Assistant Professor

• Causal Inference
• Clinical Trials
• Machine Learning
• +4 more

Bingkai Wang is an Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health. His research focuses on causal inference, clinical trials, and statistical methods for complex data. PhD in Biostatistics from Johns Hopkins University (2021) BS in Mathematics from Fudan University (2016) His work spans causal inference , machine learning , and test-negative designs in infectious disease research, with methodological contributions to semiparametric efficiency theory and cluster-randomized trials . Recent articles highlight advancements in model-robust inference , stepped-wedge trial designs , and handling incomplete outcomes in clinical trials. He has received multiple awards, including the 2024 IMS New Researcher Travel Award and the Margaret Merrell Award . Contact: bingkai@umich.edu

Evgenia TsompanakiView profile

Lecturer

• Multivariate Analysis
• Latent Variable Models
• Categorical Data
• +4 more

Dr. Evgenia Tsompanaki serves as Laboratorial Teaching Staff in the Department of Statistics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology. Her academic foundation spans advanced statistical theory and practical applications across interdisciplinary domains. Education: BSc in Mathematics (Statistics and Operational Research specialization), University of Patras MSc in Statistics, AUEB PhD in Statistics, AUEB (Thesis: "Using and Expanding Hybrid Latent Variable Models For Modelling Missing Data and For Discovering Influential Points") Her research program focuses on advanced statistical methodologies , particularly hybrid latent variable frameworks for handling incomplete datasets and identifying critical data points. This work bridges theoretical statistics with real-world challenges in public health and societal research, enabling robust analysis of complex categorical systems where missing observations compromise traditional modeling approaches. Her expertise in multivariate techniques provides essential tools for extracting meaningful patterns from high-dimensional social science and medical datasets. Professional Activities: Office: 12 Codrington Str., 1st Floor, Athens Phone: +30 210-8203802

• Database Systems
• Natural Language Processing
• Knowledge Graphs
• +1 more

Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.

Kai WangView profile

Professor

• Bioinformatics
• Statistical Genetics
• Genomic Medicine
• +3 more

Kai Wang is a Professor of Pathology and Laboratory Medicine, specializing in bioinformatics methods to understand the genetic basis of human diseases. His work integrates electronic health records and genomic information to advance large-scale genomic medicine. His research spans statistical genetics, clinical trials, and health informatics, with recent publications focusing on cluster-randomized trials, covariate adjustment, and brain imaging analysis. Notable work includes methodologies for robust inference in stepped-wedge designs and applications in Alzheimer's disease studies. Kai Wang's publications demonstrate expertise in bridging statistical theory with biomedical challenges, particularly in handling incomplete data and optimizing clinical trial precision. While no specific awards or students are listed, his Google Scholar profile highlights contributions to genomic medicine and causal inference.