جست‌وجوی استاد و پژوهشگر

Chien-Jer Charles Linمشاهده پروفایل

دانشیار

• Chinese Linguistics
• Psycholinguistics
• Cognitive Science
• +۷ مورد دیگر

Chien-Jer Charles Lin is an Associate Professor at the Department of East Asian Languages and Cultures and Adjunct Associate Professor in the Department of Linguistics and Cognitive Science Program at Indiana University Bloomington . As director of the Language and Cognition Lab , he investigates the interface of grammar, cognition, and society, with a focus on Mandarin Chinese and cross-linguistic comparisons. Education: PhD in Linguistics from the University of Arizona (2006) His research integrates psycholinguistic experiments (eye-tracking, EEG), corpus analysis , and sociolinguistic fieldwork to study: Processing of head-final structures (e.g., Chinese relative clauses) Cognitive effects of tone sandhi and phonetic orthography Discourse relations (causals, concessives, counterfactuals) Linguistic bias and sociopolitical identity in language variation His 15 most recent articles (2015–2024) reflect interdisciplinary focus on: Mandarin phonology (tone sandhi, neutral tones) Syntactic typology and parsing universals Translation syntax and corpus authenticity metrics Lexical ambiguity and metaphorical polysemy Discourse coherence and cognitive load Scientific Awards : Trustees’ Teaching Award (IU, 2016) Young Scholar Award (IACL, 2010) PACLIC-19 International Young Scholar Award (2005) Fulbright Scholarship (2001–2003) He advises graduate students across IU’s Cognitive Science Program , Psychological and Brain Sciences , and Second Language Studies , and has secured grants from IU’s Research Equipment Fund, Chiang Ching-Kuo Foundation, and Mellon Innovating International Research Fellowship.

Wei Panمشاهده پروفایل

استاد

• Statistical Genetics
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Wei Pan is a Professor in the Division of Biostatistics & Health Data Science at the School of Public Health, University of Minnesota. He is also affiliated with the Graduate Program in Bioinformatics, Biomedical Genomics Center, and Masonic Cancer Center. PhD in Statistics (UW-Madison, 1997) MS in Computer Science (UW-Madison, 1996) BEng & BS in Computer Engineering & Applied Math (Tsinghua University, 1989) Research Interests Dr. Pan specializes in statistical genetics , focusing on genetic association testing for GWAS and sequencing data. His work in bioinformatics includes integrative analysis of multi-omics data and microarray gene expression analysis. He develops methods in statistical learning (supervised, unsupervised, semi-supervised), penalized/regularized models, and survival analysis for clinical trials. Publication Trends His recent publications (2021-2023) emphasize causal machine learning (Mendelian randomization, IV regression, causal networks) and deep learning (CNNs for genomic data, interpretable neural networks). Earlier work (2008-2014) focused on graphical models and clustering algorithms for biomedical data. Scientific Recognition Fellow, American Statistical Association (2010-- ) Member, Delta Omega Honorary Society in Public Health Editorial Board, Statistics in Medicine (2002-2006) Associate Editor, JASA (2003-2006) Advising & Grants Dr. Pan advises students in statistical genetics and machine learning, with trainees participating in the NIGMS T32 Biostatistics Training in Genetics and Genomics program (2020-2025). His research involves collaborations with biomedical scientists and clinicians, supported by NIH grants including the T32 training grant. Labs & Teams He leads a research group meeting weekly for collaborative projects, with subgroups focusing on network analysis , deep learning , and semi-supervised learning . The group participates in the BD2K Guide to the Fundamentals of Data Science Series and hosts post-doctoral researchers in statistical genetics and neuroimaging.

George Maldonadoمشاهده پروفایل

دانشیار

• Epidemiology
• Causal Inference
• Environmental Health
• +۲ مورد دیگر

George Maldonado, PhD, MSPH, is an Associate Professor in the Division of Environmental Health Sciences at the University of Minnesota School of Public Health. With a strong academic foundation from UCLA, where he earned his PhD and MSPH in Epidemiology and Environmental Health, Dr. Maldonado has established himself as a leading researcher in causal inference methods within epidemiology. His research focuses on methodological approaches to causal inference from epidemiologic data, with particular expertise in uncertainty analysis, quantitative bias assessment, and the application of counterfactual theory. Dr. Maldonado's work bridges environmental health, infectious disease epidemiology, and methodological innovation, contributing significantly to the theoretical foundations of epidemiological science. His publication record demonstrates a consistent contribution to epidemiological methodology, with recent work exploring the intersection of artificial intelligence and epidemiological methods, including investigations into how AI and chatbots might assist in epidemiological research and manuscript preparation. His scholarly output spans from foundational theoretical work to practical applications in risk analysis and environmental health. Member, Delta Omega Honorary Society in Public Health Dr. Maldonado's research program addresses critical methodological challenges in epidemiology, with particular focus on improving how researchers assess causality, quantify uncertainty, and address various forms of bias in observational studies. His work has implications for environmental health research, infectious disease modeling, and evidence-based public health decision-making.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Robin Vandendriesscheمشاهده پروفایل

پژوهشگر

• Competition Law
• Data Protection Regulation
• Digital Market Competition
• +۴ مورد دیگر

Robin Vandendriessche is a Doctoral Researcher at the Brussels Center for Competition Policy within the Faculty of Economic and Social Sciences and Solvay Business School at Vrije Universiteit Brussel. His work focuses on the intersection of EU Competition Policy and Data Protection, examining how digital economy developments challenge traditional competition frameworks. His research interests include: Digital market competition dynamics Data protection considerations in antitrust cases Foreign subsidies regulation and market distortion EU State Aid policy in environmental contexts Merge control in data-intensive industries Vandendriessche's recent publications (2023-2025) reveal a strong focus on emerging competition challenges in digital ecosystems, with particular attention to data-induced market power, Android Auto antitrust doctrine, and the effectiveness of the EU Foreign Subsidies Regulation. His work bridges legal and economic perspectives through mixed-methods approaches including document analysis, empirical studies, and policy toolkits. His scientific recognition includes: Certificate for outstanding master thesis (2023) Nomination for best competition law theses at Belgian universities (2023) Nomination for Concurrences Antitrust Writing Award 2025 As an active supervisor, he has guided four master's theses on topics ranging from semiconductor acquisitions to tax reforms and sports business models. His research activities include conference chairing (ASCOLA 2025), workshop presentations, and extensive peer-review work. The OZRIFTM17 project (2023-2027) serves as the foundation for his ongoing investigation into competition policy adaptation for the digital age.

Qingyuan Zhaoمشاهده پروفایل

استادیار

• Causal Inference
• Biostatistics
• Social Sciences
• +۲ مورد دیگر

Professor Qingyuan Zhao is a University Assistant Professor in Statistics at the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. He previously held a postdoctoral fellowship at the Wharton School, University of Pennsylvania, and is currently affiliated with the Statistical Laboratory at Cambridge. Born in Wuhan, China, Zhao earned his BSc in Mathematics from the University of Science and Technology of China and a PhD in Statistics from Stanford University. Research Focus: His work centers on causal inference, particularly using Mendelian randomization and graphical models to analyze complex relationships in biomedical and social sciences. He develops statistical methodologies for observational studies, adaptive experiments, and high-dimensional data analysis. Publications: Zhao's recent research explores causal mediation analysis, off-policy evaluation, confounder selection, and sensitivity analysis in Mendelian randomization. His methodological contributions include matrix algebra for graphical models and iterative graph expansion techniques. Academic Roles: He serves as a Fellow and Director of Studies in Mathematics at his college, contributing to education and academic governance in mathematics and statistics.

Nicolas Alan Menziesمشاهده پروفایل

دانشیار

• Tuberculosis
• HIV/AIDS
• Infectious Disease Modeling
• +۵ مورد دیگر

Nicolas Alan Menzies serves as Associate Professor of Global Health in the Department of Global Health and Population at Harvard T.H. Chan School of Public Health, where he is also core faculty at the Harvard Center for Health Decision Science. His work bridges academia and policy through collaborations with the CDC, Gates Foundation, WHO, and Global Fund. Professor Menzies specializes in tuberculosis and HIV/AIDS modeling , employing Bayesian statistics and decision science to optimize disease control programs. His research focuses on cost-effectiveness analysis , health equity , and resource allocation in infectious disease control, with particular attention to racial disparities in TB outcomes and pandemic response strategies. The lab develops mathematical models to evaluate long-term population impacts of health interventions when empirical data is limited. Recent publications reveal strong thematic trends: 70% address tuberculosis epidemiology and control, 20% focus on HIV/pandemic modeling, and 10% examine health disparities. Key methodological approaches include Bayesian evidence synthesis (45% of papers), transmission-dynamic modeling (30%), and cost-effectiveness analysis (25%). Current work emphasizes granular risk prediction for targeted TB prevention and quantifying disparities in US-born populations. As principal investigator of multiple NIH-funded projects including the Optimal Targeting for Individual and Population-Level TB Prevention initiative, Menzies directs a multidisciplinary team developing web-based decision tools for clinicians and policymakers. The lab maintains active partnerships with CDC's TB division and contributes to global consortia like TB MAC (Modelling and Analysis Consortium). The Menzies Lab operates from Building 1, Room 1105 at 665 Huntington Avenue, Boston, leading projects that combine mechanistic modeling with empirical data to address infectious disease challenges in both US and global contexts.

Philip E. Tetlockمشاهده پروفایل

استاد

• Social Psychology
• Political Psychology
• Decision Science
• +۵ مورد دیگر

Philip E. Tetlock is the Leonore Annenberg University Professor at the University of Pennsylvania, with appointments in the Psychology Department and the Wharton School of Business. A leading scholar in social psychology , political psychology , and decision science , he is renowned for his work on forecasting tournaments and expert judgment . His research bridges organizational behavior , cognitive science , and geopolitical risk assessment . Ph.D., Yale University (Psychology, 1979) M.A. and B.A., University of British Columbia (1976-1975) Tetlock’s research focuses on: Expert judgment and its limitations in political and geopolitical forecasting Counterfactual reasoning in world politics and historical analysis Forecast aggregation via prediction markets and polls Cognitive biases in decision-making and belief perseverance His recent publications explore: 2024 – Comparing prediction markets (LMSR vs. CDA) and their efficacy in identifying elite forecasters 2023 – Analyzing long-range geopolitical forecasts and the role of specialization in nuclear proliferation prediction 2022 – Challenging the false dichotomy between individual forecasting and systemic risk awareness Scientific awards include: Fellowships in the American Philosophical Society , MacArthur Foundation , and American Academy of Arts and Sciences Thomas Schelling Award , Woodrow Wilson Award , and Grawemeyer World Order Prize Multiple MacArthur Fellowships and Distinguished Scientific Awards As co-leader of the Good Judgment Project , Tetlock has pioneered superforecasting and probability judgment training . He advocates for Bayesian reputational bets and adversarial collaborations to advance scientific progress in political science and behavioral economics .

• Cognitive Development
• Hypothetical Reasoning
• Counterfactual Reasoning
• +۳ مورد دیگر

Dr. Angela Nyhout is a Senior Lecturer in Psychology at the University of Kent's School of Psychology, where she serves as Programme Director for the MSc in Developmental and Educational Psychology and Student Experience Lead. She joined the university in 2020 after completing her PhD at the University of Waterloo (Canada) and a postdoctoral fellowship at the University of Toronto. Her research focuses on cognitive development, particularly the development of hypothetical reasoning and children's learning through engagement with fictional worlds. Core research areas include: Counterfactual reasoning and mental simulation abilities Scientific hypothesis testing in children Influence of fictional narratives on beliefs and attitudes Relationships between different forms of possibility thinking Educational interventions to foster reasoning skills Her publications demonstrate consistent focus on cognitive development, with recent work expanding into moral understanding through media and experimental studies on open-mindedness. Research consistently explores how children mentally represent alternative possibilities and transfer learning across contexts. Dr. Nyhout has secured substantial research funding including: SSHRC Insight Grant (2022-2026): Counterfactual reasoning in science learning ($246,065 CAD) Templeton Religious Trust Grant (2022-2024): Art Opening Minds project (£169,435) BA/Leverhulme Small Grant (2021-2023): Mental simulation development (£9,822) SSHRC Canada Grant (2019-2021): Spontaneous counterfactual thinking ($46,965 CAD) She currently supervises PhD students Zeynep Genc (primary) and Sarah Lily Resanovich (tertiary), and leads modules in Child Development, Advanced Cognitive Development, and Science of the Imagination. She is an active member of the Imagination Research Group.

David Gotzمشاهده پروفایل

استاد

• Visualization
• Visual Analytics
• Data Analysis
• +۵ مورد دیگر

David Gotz is a Professor at the University of North Carolina at Chapel Hill School of Information and Library Science (SILS) , where he serves as Director of the Carolina Health Informatics Program and UNC's Certificate in Applied Data Science . He is also an Adjunct Professor of Computer Science at UNC. Dr. Gotz leads the Visual Analytics and Communication Lab (VACLab) , focusing on interactive visual methods for data analysis and communication in healthcare and life sciences. Education: PhD in Computer Science (UNC-Chapel Hill, 2005), MS in Computer Science (UNC-Chapel Hill, 2001), BS in Computer Science (Georgia Tech, 1999). His research integrates data visualization, human-computer interaction, statistics, and machine learning to address complex data challenges, particularly in healthcare. Recent work explores counterfactual reasoning in visual analytics to improve causal inferences from clinical data. He has received grants from the National Science Foundation (NSF) and National Institutes of Health (NIH) to support this work. Dr. Gotz has been recognized with awards such as the 2017 ACM Computing Reviews Best of Computing Honoree , 2016 ACM IUI Best Paper Award , and multiple IBM Research Accomplishment Awards for his contributions to visual analytics and health informatics. Scientific Awards: 2017 ACM Computing Reviews Best of Computing Honoree 2016 ACM IUI Best Paper Award 2015 Data Fellow, National Consortium for Data Science 2013 IBM Research Accomplishment Awards 2012 IBM Research Accomplishment Awards At UNC, Dr. Gotz teaches courses including INLS 560: Programming for Information Professionals , INLS 582: Systems Analysis , and INLS 641: Visual Analytics . He is open to advising new doctoral students through the VACLab , which emphasizes interdisciplinary collaboration with healthcare professionals and cancer researchers.

Jonathan Luke Austinمشاهده پروفایل

دانشیار

• International Relations
• Political Violence
• Science and Technology Studies
• +۶ مورد دیگر

Jonathan Luke Austin is Associate Professor of International Relations at the University of Copenhagen , affiliated with the Department of Political Science and the Faculty of Social Sciences . As Principal Investigator for the 3.2 million EUR 'Future of Humanitarian Design' project and Director of the Centre for Advanced Security Theory (CAST) , he merges political science with technology, design, and aesthetics to address global security challenges. His research spans Global political violence and torture Transdisciplinary 'International Political Design' Post-critique methodologies Security theory and socio-technical networks with extensive fieldwork in Syria, Lebanon, Iraq, and Turkey. Awarded Nominated for 'Best Teacher of the Year' (2023) 2018 'Most Influential Article' recognition , Austin supervises PhD students and collaborates with institutions like the Geneva Graduate Institute , EPFL Lausanne , and organizations including MSF and ICRC . His publications explore violence prevention, digital visibility, and design interventions in world politics.

Max Argusمشاهده پروفایل

پژوهشگر

• Computer Vision for Robotics
• Reinforcement Learning
• Vision and Language
• +۳ مورد دیگر

Max Argus is a researcher at the Computer Vision Lab , University of Freiburg , focusing on computer vision and AI for robotics. He holds a PhD in Computer Science (2022) and a Master’s in Physics (University of Heidelberg), where his thesis was supervised by Björn Ommer . Research Interests : His work bridges Computer Vision for Robotics , Reinforcement Learning , and Vision-Language Integration . He explores Learning from Demonstration to enable compositional task planning, Visual Servoing for precise control, and XAI to enhance model interpretability. Publications Trends : Recent publications highlight advancements in Vision-Language Models (ICLR 2025), Explainable AI (TMLR 2025), and Robot Manipulation (ICRA 2024). His work integrates Latent Diffusion , Flow Matching , and Contrastive Learning for robotics and vision tasks. Labs & Collaborations : He collaborates with the Computer Vision Group under Prof. Thomas Brox and Wolfram Burgard , contributing to projects like cVLA (camera-space vision-language actions) and RobotIO (robot manipulation library.

Kosuke Imaiمشاهده پروفایل

استاد

• Causal Inference
• Computational Social Science
• Survey Methodology
• +۱۳ مورد دیگر

Kosuke Imai is a Professor in the Department of Government and Department of Statistics at Harvard University . He is also an affiliate of the Institute for Quantitative Social Science . Previously, he was a faculty member at Princeton University for 15 years, where he founded the Program in Statistics and Machine Learning. During the 2024-2025 academic year, he is on sabbatical at Nuffield College, University of Oxford. Education: PhD in Political Science from Harvard University (2005) Appointment: Professor (2018-present) at Harvard Imai specializes in statistical and machine learning methods for social science research. His work spans causal inference , computational social science , survey methodology , and policy learning . Recent focuses include heterogeneous treatment effects with high-dimensional data, algorithmic redistricting , and fairness analysis using generative AI. His research has produced 15+ recent articles in top journals like the Proceedings of the National Academy of Sciences , Journal of the American Statistical Association , and Political Analysis . Topics include AI-assisted decision-making , racial disparity estimation , and spatiotemporal causal modeling . He also develops open-source software tools such as FindIt and redist for causal analysis. Scientific Awards: Clarivate Highly Cited Researcher (2024) Political Analysis Editors' Choice Award (2011) Tom Ten Have Memorial Award (2013) Contact: imai@harvard.edu | Office: CGIS Knafel Building, Room 306, 1737 Cambridge Street, Cambridge, MA 02138

• Machine Learning
• High-Dimensional Statistics
• Reinforcement Learning
• +۴ مورد دیگر

Martin J. Wainwright is the Cecil H. Green Professor at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and the Department of Mathematics . He is also associated with the Statistics and Data Science Center , the Laboratory for Information and Decision Systems , and the Institute for Data, Systems and Society . His research bridges machine learning , high-dimensional statistics , and information theory , with a focus on theoretical guarantees for algorithms in reinforcement learning, optimization, and graphical models. Books : High-Dimensional Statistics: A Non-Asymptotic Viewpoint (2019, Cambridge University Press), Statistical Learning with Sparsity: The Lasso and Generalizations (2015, CRC Press). Research Themes : Statistical and computational trade-offs, robustness in adaptive learning, posterior contraction rates, and decentralized estimation. His recent work explores non-asymptotic analysis , stochastic approximation , and instance-dependent guarantees in reinforcement learning and optimization. Key contributions include minimax optimality in value estimation, variance-reduced Q-learning , and adaptive inference under elliptical constraints. Awards : IMS Medallion Lecturer , COPSS Presidents' Award , Loève Prize in Probability , Fellow of the Institute of Mathematical Statistics , NIPS Outstanding Paper Award .

• Machine Learning
• Health Informatics
• Causal Modeling
• +۳ مورد دیگر

Caglar Hizli is a Doctoral Researcher at the Department of Computer Science , School of Science, Aalto University. His work focuses on causal modeling in healthcare and temporal data analysis. Aalto SCI award: Teaching Assistants of the Year 2023 Research Interests : Causal modeling for healthcare interventions Point process modeling of treatment-outcome trajectories Nonparametric methods for blood glucose dynamics Temporal mediation analysis in clinical data Scientific Awards : Aalto SCI award: Teaching Assistants of the Year 2023