جست‌وجوی استاد و پژوهشگر

Jonathan Kolstadمشاهده پروفایل

استاد

• Health Economics
• Industrial Organization
• Public Economics
• +۴ مورد دیگر

Jonathan Kolstad is Professor holding the Henry J. Kaiser Chair at UC Berkeley's Haas School of Business with joint appointments in Economic Analysis and Policy and the Department of Economics. He serves as founding director of the Center for Healthcare Marketplace Innovation and co-director of the Health Initiative at the UC Berkeley Opportunity Lab, while also contributing as core faculty in the Computational Precision Health Graduate Group (UC Berkeley/UCSF). His educational background includes a PhD from Harvard University and BA from Stanford University, establishing foundational expertise in economic theory and empirical methods. Kolstad's research integrates health economics, industrial organization, and public economics to address complex healthcare market dynamics. He pioneers applications of behavioral economics and machine learning to examine information asymmetries, incentive structures, and policy interventions—particularly in insurance markets, quality reporting systems, and medical decision-making. His work consistently leverages novel datasets and causal inference techniques to evaluate real-world policy impacts. Recent publications reveal three dominant research trajectories: (1) malaria treatment optimization through provider/patient incentive alignment in developing nations, (2) socioeconomic determinants of health insurance choice using Dutch longitudinal data, and (3) structural analysis of ACA insurance exchanges and telehealth adoption patterns during the pandemic. These threads demonstrate his signature approach of merging theoretical microeconomics with high-impact policy evaluation. His scientific recognition includes: Arrow Award (2014) for best health economics paper NIHCM Foundation Research Award (2016 and 2018) ASHEcon Medal (2018) for economists under 40 Kolstad mentors through courses like Big Data and Better Decisions and Health Economics, developing problem sets on Massachusetts health reform for graduate instruction. His entrepreneurial engagement spans co-founding Picwell (serving as Chief Data Scientist) and advising governments, corporations, and startups on healthcare innovation. He leads the Center for Healthcare Marketplace Innovation in developing testable models for healthcare market transformation, while contributing to the Computational Precision Health Graduate Group's mission of integrating data science with clinical practice to improve population health outcomes.

Katie Burnhamمشاهده پروفایل

پژوهشگر

• Functional Genomics
• Immunogenetics
• Transcriptomics
• +۳ مورد دیگر

Katie Burnham is a Researcher at the Wellcome Sanger Institute , affiliated with the Human Genetics Programme. She previously held a Postdoctoral Research Fellowship at the University of Oxford's Knight group and earned her PhD in Clinical Medicine from the Wellcome Centre for Human Genetics, Oxford. Her research focuses on functional genomics and host immune response variations in sepsis, integrating genomic , transcriptomic , and clinical data to identify disease subgroups (endotypes) for precision medicine applications. Key contributions include defining endotype-specific treatment responses in clinical trials like VANISH and uncovering genomic drivers of sepsis heterogeneity. Notable trends in her peer-reviewed work (2025–2020) include: Application of multi-omics integration (genomics, transcriptomics, proteomics) to critical care medicine Development of transcriptomic signatures for sepsis stratification and outcome prediction Exploration of genetic determinants Analysis of immunological archetypes and regulatory networks in acute infections Her work has been published in journals such as Nature Genetics , Science Translational Medicine , and The Lancet Respiratory Medicine .

Jennifer Castleمشاهده پروفایل

دانشیار

• Climate Econometrics
• Econometric Modeling
• Model Selection
• +۷ مورد دیگر

Jennifer Castle is the Director of Climate Econometrics at the University of Oxford and a Titular Associate Professor and Tutorial Fellow in Economics at Magdalen College. Her work bridges econometrics and climate-economic modeling, focusing on robust forecasting methodologies and empirical analysis of macroeconomic variables like inflation, unemployment, and climate impacts. Affiliation: University of Oxford, Magdalen College Research Themes: Climate econometrics, structural break analysis, general-to-specific modeling, and non-linear time series Research Interests center on disentangling complex climate-economic relationships and improving forecasting accuracy through rigorous model evaluation. She applies these methods to inflation dynamics, energy policy, and long-term climate transition scenarios. Recent Publications highlight her work on UK inflation forecasting challenges, cryptocurrency market stability, income inequality projections, and climate policy intervention points. Her research emphasizes model robustness in the face of structural shifts and data uncertainty.

Andrew S. Gordonمشاهده پروفایل

عضو هیئت علمی

• Interactive Narrative
• Commonsense Reasoning
• Computational Models of Narrative
• +۶ مورد دیگر

Andrew S. Gordon is a Research Associate Professor of Computer Science at the University of Southern California and Director of Interactive Narrative Research at the Institute for Creative Technologies. His work integrates artificial intelligence, cognitive science, and interactive storytelling to create systems that automatically interpret and generate narrative structures, with a special focus on commonsense reasoning and abductive inference. Education Ph.D. in Computer Science, Northwestern University, 1999 Research Interests Gordon’s research converges on computational narrative intelligence . He develops formal models of commonsense psychology that enable machines to reason about human intentions, beliefs, and emotions. His group designs interactive narrative systems for training and education, builds large-scale story corpora, and pioneers abductive reasoning techniques that combine symbolic logic and statistical learning to interpret temporal data. Recent work explores how large language models can be guided to co-create interactive fiction and how vision–language models can be benchmarked for causal understanding. Publication Trends Across more than two decades, his publications reveal consistent themes: (1) foundational theories of commonsense psychology and strategy representation, (2) narrative technologies that blend AI planning with human creativity, and (3) practical training simulations for defense and education. The 2024-2025 papers highlight a pivot toward evaluating and steering large language models for narrative tasks, while earlier work established abductive reasoning frameworks such as “Etcetera Abduction.” Awards & Honors Best Paper Award, System Lifecycle and Technologies Track, Simulation Interoperability Standards Organization (SIW 2021) Advising & Grants He has successfully mentored three PhD students—Reid Swanson (2010), Christopher Wienberg (2017), and Melissa Roemmele (2018)—whose dissertations span computational narrative, commonsense reasoning, and interactive fiction. His research has been continuously supported by agencies including the U.S. Army, DARPA, and NSF for projects on virtual training environments, narrative-centered learning, and large-scale commonsense knowledge acquisition. Labs & Teams Gordon directs the Interactive Narrative Research Group at USC’s Institute for Creative Technologies, where interdisciplinary teams of computer scientists, cognitive psychologists, and interactive media designers collaborate on systems such as the Rapid Integration & Development Environment (RIDE) for embodied conversational agents and story-driven training simulations.

Paul S. Rosenbloomمشاهده پروفایل

استاد

• Cognitive Architecture
• Artificial General Intelligence
• Hybrid Symbol Systems
• +۴ مورد دیگر

Paul S. Rosenbloom is a Professor in the Department of Computer Science at the University of Southern California , with affiliations at the Institute for Creative Technologies . His work focuses on cognitive architectures , particularly the development of the Sigma architecture and contributions to the Common Model of Cognition . He has pioneered the integration of symbolic, probabilistic, and neural systems in AI research. Research Interests: Dr. Rosenbloom's research spans hybrid symbol systems , neural-symbolic integration , and the evolution of computational models of cognition . His recent work rethinks the Physical Symbol Systems Hypothesis through hybrid systems that bridge symbolic AI and neural networks, addressing challenges in universality , compositional reasoning , and cognitive modeling . Scientific Awards: 2011 Kurzweil Award for Best AGI Idea 2012 Kurzweil Award for Best AGI Paper 2023 Springer Prize for Best Paper Publications and Contributions: He has authored over 150 publications and co-authored foundational works on the Common Model of Cognition with Laird, Lebiere, and Stocco. His research has been supported by grants from the U.S. Army RDECOM and USC's Institute for Creative Technologies. He has mentored numerous collaborators and co-authors, including researchers like V. Ustun , A. Demski , and H. Joshi , in projects spanning virtual humans , reinforcement learning , and distributed vector representations .

Tue Herlauمشاهده پروفایل

دانشیار

• Bayesian Methods
• Complex Networks
• Brain Imaging Data Analysis
• +۳ مورد دیگر

Tue Herlau is an Associate Professor at the Department of Applied Mathematics and Computer Science, Cognitive Systems, within the College of Engineering at Technical University of Denmark. His research focuses on modeling complex networks using Bayesian methods, particularly applied to brain imaging data. He explores multiscale structures in networks (ranging from individual nodes to large-scale temporal dynamics) and causal inference in social and biological systems. Education : Bachelor in Physics and Mathematics (University of Copenhagen), Masters in Informatics (DTU) Recent publications highlight work on generative AI for educational psychology, causal probability trees, Bayesian neural network training, and moral reinforcement learning. Key collaborations span institutions in Denmark and international partners in AI and neuroscience research. He supervises students in probabilistic methods and machine learning applications, including a 2019-2020 Master project on prognostics for carbon/epoxy composites. His network shows strong engagement with computational science, relational databases, and formal concept analysis.

Luis Secoمشاهده پروفایل

استاد

• Harmonic Analysis
• Mathematical Physics
• Mathematical Finance
• +۳ مورد دیگر

Luis Seco is a Professor in the Department of Mathematics at the University of Toronto, with a focus on Mathematical and Computational Sciences. He is based at the Bahen Centre for Information Technology (Room BA-7262) and maintains a presence at the University of Toronto Mississauga (UTM) campus. He serves as Director of the Mathematical Finance Program and is affiliated with RiskLab Toronto, housed at the Bahen Centre. Dr. Seco's research spans multiple disciplines at the intersection of mathematics and finance: Harmonic Analysis Mathematical Physics Mathematical Finance Quantitative Risk Management His scholarly work demonstrates a sophisticated integration of advanced mathematical techniques with practical financial applications. Recent publications (2022-2025) reveal a strong emphasis on machine learning approaches to financial problems, with particular focus on algorithmic trading, risk modeling, and ESG (Environmental, Social, and Governance) investing. His research has evolved from theoretical mathematical foundations to cutting-edge applications in quantitative finance. Dr. Seco's publication record shows significant scholarly productivity with a clear trajectory from pure mathematics toward increasingly applied financial mathematics. His work bridges theoretical rigor with practical financial applications, making substantial contributions to both academic research and industry practices. As Director of the Mathematical Finance Program, Dr. Seco provides leadership in shaping education and research in quantitative finance at the University of Toronto. His RiskLab affiliation indicates active engagement with real-world risk management challenges, connecting academic theory with industry applications.

Benjamin W. Domingueمشاهده پروفایل

دانشیار

• Psychometrics
• Sociogenomics
• Quantitative Methods
• +۳ مورد دیگر

Benjamin W. Domingue is an Associate Professor at the Graduate School of Education , Stanford University, with affiliations including the Stanford Center on Early Childhood and Policy Analysis for California Education (PACE) . His research bridges psychometrics and sociogenomics , focusing on statistical tools for measuring educational outcomes and integrating genetic data into social science research. Education: PhD in Education, University of Colorado Boulder (Advisor: Derek Briggs, 2012) MA in Mathematics, University of Texas at Austin (Advisor: Uri Treisman, 2006) BS in Mathematics, University of Texas at Austin (2001) His work examines test score properties , response time analysis , and genetic influences on educational attainment . Recent projects include developing the Item Response Warehouse (IRW) resource and analyzing oral reading fluency during the pandemic . Key methodological contributions involve the InterModel Vigorish (IMV) for predictive accuracy and speed-accuracy tradeoffs in real-world settings. Scientific Awards Jacobs Foundation Research Fellow (2022–2024) Outstanding Reviewer for AERA Open (2019, 2018) AERA Division D Quantitative Dissertation Award (2013) Faculty Advising Award (2018–2019) His research spans collaborations with institutions like the University of California, Berkeley, University of Colorado Boulder, and The University of Texas at Austin. He has contributed to genetic moderation studies , item response theory , and healthcare provider behavior through psychometric approaches.

• Anomaly Detection
• Topic Modeling
• Human-Computer Interaction
• +۳ مورد دیگر

Tove Helldin is a Senior Lecturer at the School of Informatics, University of Skövde, Sweden. Her research focuses on anomaly detection , topic modeling , and human-computer interaction , particularly in telecommunications networks and decision support systems . She has led projects in AI for climate adaptation and future sepsis diagnostics , emphasizing team effectiveness and trust calibration in automated systems. PhD in Computer Science (2014), University of Skövde Licentiate in Computer Science (2012) MSc in Computer Science (2009) Her scientific work spans interactive machine learning , visualization of causal relationships , and transparency in military threat evaluation . She has contributed to automotive UI design and fighter aircraft automation , with publications in venues like ACM Computing Surveys and IEEE conferences. Notably, her work explores topic modeling applications in network monitoring and interpretable AI frameworks.

• Family dynamics
• Fertility behavior
• Fertility intentions
• +۷ مورد دیگر

Chiara Comolli is an affiliated researcher at the Department of Sociology , Stockholm University , specializing in demographic studies with a focus on family dynamics, fertility behavior, and economic uncertainty. She contributes to research on population dynamics through quantitative methods and longitudinal data analysis. Current affiliation: Stockholm University Demographic Division (SUDA) Research areas: Fertility responses to economic crises, well-being analysis, causal inference Specializes in life course studies and social inequality Her recent publications examine fertility trends in Europe during economic downturns, the impact of employment uncertainty on childbearing, and longitudinal effects of family-work trajectories on well-being. Her work integrates interdisciplinary approaches from demography, sociology, and economics. Comolli actively participates in SUDA's research initiatives, which include master's and doctoral programs in demography. Her methodological expertise in causal inference and quantitative analysis contributes to Stockholm University's leading demographic research profile.

Petri Ylikoskiمشاهده پروفایل

استاد

• Philosophy of Social Sciences
• Sociology
• Social Mechanisms
• +۳ مورد دیگر

Petri Ylikoski is a Professor in Science and Technology Studies (Sociology) at the University of Helsinki's Department of Social Research and a Visiting Professor at Linköping University's Institute for Analytical Sociology. His research integrates philosophy, sociology, and computational methods to address foundational questions in social science, particularly focusing on mechanisms of explanation, micro-macro relations, and methodological individualism. His work spans several interconnected domains: Philosophy of Social Sciences : Theoretical frameworks for social explanation, causality, and model-based reasoning. Social Mechanisms : Analysis of causal processes in social phenomena, from norms to institutions. Interdisciplinary Methodologies : Agent-based simulation, machine learning applications, and epistemology of modeling. Ylikoski's publications consistently explore the interplay between individual agency and social structures, with emphases on mechanistic explanations, theoretical models, and critiques of reductionism. Recent trends include collaborations bridging philosophy with computational social science, such as studies on AI interpretation and agent-based simulations of social dynamics. No awards, grants, or student advising details are documented in available sources. Similarly, specific labs or teams are not mentioned, though his collaborative projects (e.g., with Juho Pääkkönen and Jaakko Kuorikoski) highlight a network-oriented research approach.

Nicola Meschedeمشاهده پروفایل

استاد

• Science Education
• Teacher Education
• Professional Development
• +۴ مورد دیگر

Nicola Meschede is a Professor of Natural Science Elementary and Primary Education at the Institute of General Studies Didactics, Faculty of Educational Sciences, University of Münster, a position she has held since January 2018. Previously, she served as a substitute professor for General Studies Didactics at the same institution from October 2017 to January 2018. Her academic journey includes significant research positions at Goethe-Universität Frankfurt and the Leibniz Institute for Science and Mathematics Education at Kiel University. Her educational background comprises a Doctorate in Natural Sciences (Dr. rer. nat.) from the University of Münster and the Leibniz Institute for Science and Mathematics Education (2009-2013), a Second State Examination for Primary School Teaching from Siegburg Teacher Training College (2013-2015), and a First State Examination for Primary, Secondary, and Comprehensive School Teaching with a focus on primary education from Siegen University and the University of Münster (2004). Professor Meschede's research centers on professional vision development in teachers, particularly how educators perceive and interpret learning-relevant classroom situations in science education. Her work examines the cognitive processes involved in recognizing significant teaching moments, with special attention to classroom management, learning support, and addressing student heterogeneity. She investigates how video-based learning environments can enhance teachers' professional perception skills during both initial and continuing teacher education. Analysis of her recent publications reveals a strong thematic focus on video-based teacher training methodologies, with particular emphasis on developing digital learning modules that foster multi-perspective professional vision. Her work consistently addresses how teachers process classroom situations, with increasing attention to language-sensitive approaches in science education and the relationship between teacher beliefs and professional vision development. The interdisciplinary nature of her research bridges educational psychology, science education, and teacher training methodologies. Professor Meschede leads several significant research projects including ViU:P (Video-based Instructional Analysis, Focus on Performance), which examines the causal chain of professional knowledge, teaching quality, and student achievement. She also directs the ProdiviS project developing digital, video-based self-learning modules for professional classroom perception training, the ComeMINT project focusing on professionalizing primary teachers in digital media use, and the ViFoNet project creating video-based training modules for digitally supported teaching. Her research methodology emphasizes video-based analysis of classroom situations, examining how teachers recognize and interpret learning-relevant events. She has developed specialized video portals like ViU: Early Science and ProVision that serve as both research tools and teacher training resources. Her work demonstrates how structured video analysis, particularly with modeling and prompting techniques, can enhance pre-service and in-service teachers' professional vision capabilities.

• Action Theory
• Social Philosophy
• Philosophy of History
• +۷ مورد دیگر

Doris Gerber is a Senior Lecturer (Privatdozentin) at the Philosophical Seminar of the University of Tübingen , affiliated with the Faculty of Philosophy. She has held visiting professorships (Vertretungen) at the Universities of Bayreuth, Stuttgart, Jena, and Bielefeld, and served as a lecturer at the University of Heidelberg. Education : 2001: PhD in Philosophy, University of Tübingen 1995: Magister Artium in Philosophy and History, University of Tübingen 1987: Diplom in Pedagogy, University of Tübingen Her research spans Action Theory , Social Philosophy , Philosophy of History , and Philosophy of Language , with a focus on collective responsibility, historical causality, and intentional agency. Recent publications explore counterfactual reasoning in historiography, the interplay of social structures and collective actions, and temporal dimensions in historical narratives. Key scientific awards include the Landesgraduiertenstipendium (1996–1998) from Baden-Württemberg. She has contributed to edited volumes like Kollektive Verantwortung und internationale Beziehungen (2010) and advanced theories on historical explanation in journals such as Zeitschrift für philosophische Forschung and Erwägen – Wissen – Ethik .

Yongmiao Hongمشاهده پروفایل

استاد

• Model Specification Testing
• Nonlinear Time Series Analysis
• Financial Econometrics
• +۴ مورد دیگر

Yongmiao Hong serves as the Ernest S. Liu Professor of Economics and International Studies in the Department of Economics at Cornell University. He holds dual appointments as Professor of Statistics and field member in both the Department of Statistical Sciences and Center for Applied Mathematics. Professor Hong's research spans model specification testing, nonlinear time series analysis, financial econometrics, and empirical studies of Chinese economic systems. His methodological innovations include generalized spectral analysis for capturing nonlinear dependencies, semiparametric specification tests using orthogonal series/kernel methods, and autoregressive conditional interval (ACI) models for interval-valued time series data. His work demonstrates how interval data (e.g., daily stock price ranges) provides superior econometric estimation compared to point-valued observations. His publication record reveals consistent contributions to top-tier journals including Econometrica , Annals of Statistics , and Review of Financial Studies . Key trends show evolutionary progression from foundational specification testing (1990s) to sophisticated nonlinear time series tools (2000s), with recent focus on interval-valued modeling and multivariate conditional distribution validation. His research consistently bridges theoretical econometrics with financial market applications. Professor Hong advises doctoral students in economics and statistics, though specific advisee names aren't publicly listed. His research has been supported by grants enabling extensive empirical work on Chinese financial markets and continuous-time model validation. He previously served as President of the Chinese Economists Society in North America (2009-2010). His laboratory work centers on time series methodology development, particularly spectral analysis extensions and interval data modeling. Current projects involve refining ACI models for crude oil price forecasting and developing wavelet-based covariance matrix estimators robust to heteroskedasticity.

• Income Distribution
• Labor Economics
• Education Economics
• +۴ مورد دیگر

Prof. Dr. Martin Biewen is a Full Professor of Statistics, Econometrics and Quantitative Methods at the University of Tübingen 's Faculty of Economics and Social Sciences. Since 2023, he serves as Scientific Director of the Institute for Applied Economic Research (IAW Tübingen) and is a member of the Cluster of Excellence - Machine Learning for Science. His research spans income distribution , labor economics , education economics , and microeconometrics , with recent work applying machine learning to inequality analysis. PhD, University of Heidelberg (2000) Habilitation, University of Mannheim (2005) His methodological innovations include bootstrap inference for inequality measurement and Stata implementations of decomposition techniques. He has served on multiple advisory boards including the German Federal Ministry of Labour and Social Affairs and the German Economic Association 's standing committees. Current grants include DFG Priority Programme 1764 and leadership roles in the LEAD Graduate School. Articles demonstrate expertise in minimum wage impacts , wealth inequality , and gender gaps in economic literacy . He has developed statistical software packages for Stata and R used in inequality analysis across 20+ journals.