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Zihang LuView profile

Assistant Professor

• Biostatistics
• Epidemiological Research
• Bayesian Analysis
• +4 more

Dr. Zihang Lu is an Assistant Professor in the Department of Public Health Sciences at Queen’s University, affiliated with the School of Medicine and Faculty of Health Sciences. He holds a PhD and MSc in Biostatistics from the University of Toronto (2020 and 2013) and a BSc in Biostatistics from Southern Medical University (2012). His research focuses on developing novel statistical and machine learning methods for complex data analysis, with applications in clinical and epidemiological studies, particularly in asthma, obesity, sleep, pain, and cancer research. He collaborates with clinicians to address statistical challenges in longitudinal and functional data analysis, Bayesian modeling, and integrative clustering. Education : PhD in Biostatistics, University of Toronto (2020) MSc in Biostatistics, University of Toronto (2013) BSc in Biostatistics, Southern Medical University (2012) Research Interests : Bayesian data analysis and variable selection Longitudinal and functional data clustering High-dimensional data integration Statistical methods for disease subtyping Design and analysis of observational studies Advising & Grants : Current advisees: Caroline Lee (MSc 2022), Veronique Rowley (2021), Zhiwen Tan (PhD 2022), Mojtaba Ahmadiankalati (MSc 2021) Recruiting CANSSI Distinguished Post-Doctoral Fellow for Bayesian methods in longitudinal health data analysis Accepting MSc/PhD students for 2023 Labs & Teams : His team focuses on statistical methodology development and clinical collaborations, with active projects in pediatric lung function (e.g., the CHILD Cohort Study) and chronic disease management.

Rickard SandbergView profile

Associate Professor

• Machine Learning
• Deep Learning
• Data Analytics
• +9 more

Rickard Sandberg is an **Associate Professor** and **Center Director** at the **Department of Entrepreneurship, Innovation and Technology** at the **Stockholm School of Economics (SSE)**. His work bridges econometrics, statistics, and business analytics with a focus on time series analysis, machine learning applications, and sustainability measurement. **Research Interests**: Machine Learning, Deep Learning, Data Analytics, Predictive Analytics, Forecasting, Nonlinear Time Series Modelling, Structural Economic Modelling, Econometrics, and Measuring Sustainability. His research emphasizes theoretical advancements in statistical methods and their practical application in economic and business contexts. **Key Contributions**: His publications explore unit root testing in nonlinear models, ESG rating challenges, and the impact of energy policies. Notable works include analyzing Scandinavian unemployment trends, cartel damage calculations, and Nordic companies' data-driven transformations. His 2023 paper on ESG ratings proposes solutions for consistency in ambiguous evaluation systems. **Teaching & Outreach**: Teaches advanced econometric time series courses (e.g., MSc 5314) and actively engages in international academic collaborations through presentations in Japan and Brazil. His work on AI for sustainability highlights interdisciplinary outreach efforts. **Labs/Teams**: Leads research initiatives within SSE’s Department, focusing on entrepreneurship and innovation through data and economic modeling frameworks.

• Environmental Health
• Epidemiology
• Climate Change Health Impacts
• +4 more

Erika Garcia, PhD, MPH is an Assistant Professor in the Division of Environmental Health at the Department of Population and Public Health Sciences, Keck School of Medicine of USC. Her work focuses on understanding how ambient environmental exposures influence human disease development, particularly through advanced causal inference methods. Key research areas include early-life air pollution effects on pediatric health (asthma, lung function, metabolic outcomes) and climate-driven exposures like extreme heat. Education: PhD in Environmental Health Sciences, UC Berkeley MPH in Environmental Health Sciences, UC Berkeley Research Interests: Climate change health impacts: heat waves, air pollution, and associated mortality/morbidity Environmental justice: disparities in pollution exposure and health outcomes Exposure mixtures: synergistic effects of chemical pollutants Epigenetic biomarkers of environmental exposures Recent Article Trends: Focus on methodological innovations in exposure assessment, policy-driven intervention modeling, and translational research linking environmental exposures to chronic disease pathways. Recent work emphasizes population-level vulnerability analysis and actionable solutions for disadvantaged communities. Advising/Grants: Active in mentoring interdisciplinary teams but specific grant details not highlighted in current text.

Matthew PecencoView profile

Assistant Professor

• Labor Economics
• Criminal Justice System
• Education Policy
• +3 more

Matthew Pecenco is an Orlando Bravo Assistant Professor of Economics at Brown University, specializing in labor economics with a focus on criminal justice system impacts, education policy, and development economics. His research employs experimental and quasi-experimental methods to analyze topics such as incarceration effects, peer preferences in education, and entrepreneurship drivers. He holds a PhD from UC Berkeley (2020). Research Interests: Criminal justice system policy evaluation Educational market dynamics and peer effects Climate change's labor market implications Entrepreneurship through labor experience His recent work explores incarceration's socioeconomic consequences, workplace discrimination in digital markets, and climate adaptation strategies for labor productivity. Pecenco's methodologies emphasize causal inference and policy relevance.

Thomas Le BarbanchonView profile

Professor

• Labor Economics
• Public Finance
• Econometrics
• +5 more

Thomas Le Barbanchon is a Full Professor of Economics at Bocconi University since 2025 and holds the Rodolfo Debenedetti Chair in Labor Economics. He received his PhD from Ecole Polytechnique (CREST-ENSAE) in 2012 and has been affiliated with institutions like CEPR, CREST, J-PAL, LEAP, BIDSA, IGIER, IZA, and IFS. His research focuses on labor economics, particularly job search mechanisms, unemployment insurance effects, gender disparities, and labor market policy evaluation. Educational background: PhD in Economics (Ecole Polytechnique), MSc in Economics (Universitat Pompeu Fabra), Diplôme d'ingénieur statisticien-économiste (ENSAE), and Diplôme d'ingénieur (Ecole Polytechnique) Research highlights include: Quantifying the impact of unemployment insurance reforms in France Analyzing gender differences in job search behavior Studying the effectiveness of hiring credits during economic crises Investigating how traditional AI improves job matching Examining migrant-native job search segregation patterns Scientific contributions appear in top journals like American Economic Journal: Applied Economics , Quarterly Journal of Economics , and Review of Economic Studies . Awards include two ERC grants (2017 & 2024) and the Bocconi Impact Award (2022). He mentors PhD students in economics and labor policy, currently serves as Director of IGIER research center, and maintains active editorial roles at Review of Economic Studies and Journal of the European Economic Association .

• Econometrics
• Applied Econometrics
• Nonparametric Identification
• +3 more

Andrei Zeleneev is a Lecturer in Economics at University College London (UCL), Department of Economics, where he joined in 2020. He holds a PhD from Princeton University and is affiliated with CeMMAP (Centre for Microdata Methods and Practice). His research focuses on econometric methodologies, particularly addressing challenges in panel models, network models, and errors-in-variables. His work emphasizes nonparametric identification, robust estimation, and applied econometric techniques to handle unobserved heterogeneity and measurement errors. Dr. Zeleneev’s academic background includes advanced training in econometrics, and his research spans both theoretical and applied domains. He has published extensively on topics such as treatment effects in large panels, latent variable modeling, and structural econometric approaches to network analysis. His articles highlight innovations in handling complex data structures and improving the reliability of econometric inferences in settings with non-classical errors or latent factors. He maintains an active research agenda, with recent work addressing methodological advancements in semiparametric models and interactive fixed effects. Dr. Zeleneev’s professional activities include teaching at UCL and contributing to the econometrics community through publications and affiliations with leading institutions.

Marco CastilloView profile

Professor

• Behavioral Economics
• Public Economics
• Economics of Education
• +1 more

Marco Castillo is a Professor of Economics at Texas A&M University, specializing in Behavioral/Experimental Economics, Public Economics, and Economics of Education. He holds a PhD from the University of Wisconsin-Madison and previously served as a Professorial Fellow in the Education and Child Development research group at the University of Melbourne’s Melbourne Institute. His work employs experimental methods to design optimal policies in areas like child development, education, and criminal justice. He is affiliated with the IZA Institute of Labor Economics as a Research Fellow and the CESIfo Research Network as a Fellow. Education: PhD in Agricultural and Applied Economics, University of Wisconsin-Madison Bachelor’s Degree in Economics, Catholic University of Lima, Peru Research Interests: Experimental methods in policy design Child development interventions Education economics Charitable giving mechanisms Risk preferences and decision-making His research bridges theory and practice, often involving field experiments to assess policy impacts on real-world behaviors. Awards & Fellowships: Fellow of the CESIfo Research Network Research Fellow at IZA Institute of Labor Economics Grants & Advising: While no specific grants are listed, his work frequently involves policy-relevant experimental research. He advises students through his teaching and research collaborations at Texas A&M. Labs/Teams: Active in the Economics Science Association’s initiatives and collaborates with institutions like the Melbourne Institute on child development projects.

Fiery CushmanView profile

Professor

• Moral Psychology
• Social Neuroscience
• Reinforcement Learning
• +3 more

Fiery Cushman is Professor of Psychology at Harvard University, directing the Moral Psychology Research Laboratory. His work examines the cognitive architecture of moral judgment through computational and experimental approaches. Education includes: PhD from Harvard University Postdoctoral fellowship at Harvard Research investigates: Learning mechanisms in moral development Punishment as social communication Counterfactual reasoning in decision-making Theory of mind in social evaluation Publications present computational models unifying moral learning, judgment and behavior across 50+ articles. Awards and honors: Stanton Prize (Society for Philosophy and Psychology) Daniel M. Wegner Theoretical Innovation Prize (SPSP) Henry Merrit Wriston Fellowship Laboratory employs diverse methodologies including behavioral experiments, fMRI, developmental studies and economic games. Current projects examine contractualist principles in moral judgment and the cognitive foundations of blame.

Leitgeb HannesView profile

Professor

• Logic
• Philosophy of language
• Philosophy of science
• +4 more

Hannes Leitgeb is the Chair of Logic and Philosophy of Language and an Alexander von Humboldt Professor at Ludwig-Maximilians University Munich, where he also founded and directs the Munich Center for Mathematical Philosophy . His research spans logic, epistemology, philosophy of science, and philosophy of mathematics, with a focus on formal models of belief, Bayesianism, and Carnapian foundations. Education: Dual PhDs in Philosophy and Mathematics from the University of Salzburg. Research Trends: Integrates mathematical rigor into philosophical analysis, particularly in belief stability, counterfactuals, and identity criteria. His work bridges classical epistemology with computational approaches. Editorial Leadership: Served as Editor-in-Chief of Erkenntnis , Coordinating Editor of Review of Symbolic Logic , and Subject Editor for the Stanford Encyclopedia of Philosophy . Scientific Awards: Elected member of L'Académie Internationale de Philosophie des Sciences (2011) Alexander von Humboldt Professorship (2010) Friedrich Wilhelm Bessel Research Award (2007) Philip Leverhulme Prize (2007) Leitgeb has delivered over 168 invited talks since 2005 and published 74 articles, including in top journals like Mind , The Philosophical Review , and Nous . His declined offers from prestigious institutions (Stanford, UC Irvine) underscore his academic prominence.

Shahin JabbariView profile

Assistant Professor

• Machine Learning
• Algorithmic Fairness
• Game Theory
• +7 more

Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +3 more

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

David RichView profile

Professor

• Environmental Epidemiology
• Air Pollution Health Effects
• Public Health
• +4 more

David Rich is a Professor of Public Health Sciences, Medicine, and Environmental Medicine at the University of Rochester's School of Medicine and Dentistry. He holds a Sc.D. in Epidemiology and Environmental Health from Harvard and an M.P.H. from the University of Medicine and Dentistry of New Jersey. His research focuses on environmental epidemiology, particularly the health effects of air pollution, including maternal exposure on fetal development, cardiorespiratory outcomes, and the efficacy of air quality policies. Dr. Rich leads the Division of Epidemiology and directs the PhD/MS programs in Epidemiology. Education: Sc.D., Harvard School of Public Health (2004) M.P.H., University of Medicine and Dentistry of New Jersey (1999) B.S., Rutgers University (1994) Research Interests: Dr. Rich's work examines maternal air pollution exposure's impact on placental development, metabolic dysfunction, and fetal health. He also evaluates air quality policies' effects on mortality, particularly in the U.S. and China. Current studies include cohort analyses of pregnant women in New York and Pennsylvania, particle size distribution monitoring, and accountability studies on mobile source emissions. Awards: George H. Cook Distinguished Alumni Award (2015) Best Environmental Epidemiology Paper of 2013 (2014) Excellence in Teaching Award (2009) Grants & Labs: Funding includes support from NIH, Health Effects Institute, and New York State Energy Research and Development Authority. He mentors epidemiology students and teaches graduate courses in environmental and occupational epidemiology.

Boris ShorView profile

Associate Professor

• Political Science
• State Legislatures
• Political Polarization
• +7 more

Boris Shor is an Associate Professor of Political Science at the Hobby School of Public Affairs, University of Houston, where he conducts research on state legislatures, political polarization, representation, and health policy. He is associated with multiple interdisciplinary initiatives and contributes to major data infrastructure in political science. Education: Ph.D. and M.A. in Political Science, Columbia University B.A., Princeton University Shor specializes in American politics with a focus on legislative behavior, ideological measurement, and health policy. His work combines quantitative methods, roll call analysis, and survey data to understand polarization and representation across U.S. states. He has developed one of the most comprehensive datasets on state legislative ideology, influencing both academic research and policy analysis. His recent publications span top journals such as American Political Science Review , American Journal of Political Science , and Political Analysis , with a thematic focus on state-level political dynamics, methodological innovation, and health policy politics. The research consistently explores how partisanship, ideology, and institutional structures shape policy outcomes. Scientific Awards and Honors: Robert Wood Johnson Scholar in Health Policy, UC Berkeley Fellow, Center for the Study of Democratic Politics, Princeton University Shor has secured major external funding from the National Science Foundation and the Russell Sage Foundation . He has advised undergraduate, master’s, and doctoral students throughout his career. His ongoing book project examines the politics of health policy in the American states, building on his interdisciplinary background in political science and public health. He is affiliated with research centers focused on democratic governance and public policy, contributing to collaborative academic networks across institutions.

Dong GongView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Marios GeorgakisView profile

Researcher

• Cardiovascular Disease
• Stroke
• Atherosclerosis
• +8 more

Marios Georgakis is a clinician-scientist and Junior Group Leader at the Institute for Stroke and Dementia Research (ISD) at LMU Munich. He also holds a Visiting Scientist position at the Broad Institute of MIT and Harvard. His research focuses on leveraging multi-omics data and causal inference methods (e.g., Mendelian randomization) to discover drug targets for atherosclerosis, develop personalized risk stratification tools for cerebrovascular disease, and identify in vivo biomarkers of disease activity. His work bridges human genetics, molecular biology, and clinical translation. Education : MD and PhD (Epidemiology) from the National and Kapodistrian University of Athens; doctoral studies in Systemic Neurosciences at LMU Munich. Honors : Emmy Noether Award (DFG), CHARGE Consortium Early Career Achievement Award, Hertie Network Fellowship, and multiple scholarships/fellowships. Research Themes : Drug target discovery for cardiovascular disease via multiomics integration Molecular phenotyping of atherosclerosis using single-cell RNA-seq and spatial transcriptomics Development of AI-driven tools for vascular imaging and aging Genetic studies of inflammation, cytokines, and stroke subtypes Causal inference in vascular risk prediction and post-stroke outcomes Recent Article Trends : His team's publications (2024-2020) emphasize: Proteogenomic and genetic studies of atherosclerosis Cytokine signaling pathways (e.g., IL-6, CCL2/CCR2) Polygenic and genomic risk scores for stroke Multi-omics biomarkers in cerebrovascular disease Clinical translation of Mendelian randomization findings Meta-analyses of population-based data Scientific Awards : Emmy Noether Group Leader Award (DFG, 2023) CHARGE Consortium Early Career Achievement (2023) Hertie Network Fellowship (2023) Walter-Benjamin Postdoctoral Fellowship (2021-2022) Team Leadership : Georgakis mentors multiple PhD students and postdocs in his lab. His group collaborates with vascular surgeons, neurologists, and computational biologists. Current projects include the AtherOMICS biobank and AI-driven vascular phenotyping tools.