Research integrity
Studying the systems, incentives, and evidence practices that shape the reliability of scientific communication.
- Retractions and research misconduct
- Peer review and publication quality
- Miscitation and evidence distortion
Research
Metascience for evidence-building institutions.
Our research examines the integrity, evaluation, governance, discovery, biomedical translation, and equity of science, with growing attention to how AI, information networks, and open data infrastructures are reshaping scientific work.
Research in the age of AI
Examining how AI changes scientific work, evaluation, discovery, and the infrastructures that support trustworthy knowledge production.
- AI for science
- Trustworthy AI in research systems
- LLMs, NLP, and scholarly data
Data-driven science policy
Building evidence and analytic infrastructure that help funders and institutions allocate resources, evaluate portfolios, and anticipate emerging opportunities.
- Scientific portfolio analysis
- Evidence for science funders
- DoD and NIH-style research portfolio analytics
Knowledge discovery and translation
Using large-scale scholarly data, citation networks, natural language processing, and machine learning to understand how ideas move and which discoveries influence future research, medicine, and technology.
- Biomedical research translation
- Causal and substantive citations
- Research frontier prediction
Inequality in science
Measuring structural disparities in credit, recognition, mobility, authorship, and media attention so institutions can build fairer scientific systems.
- Gender and race disparities
- Scientific workforce equity
- Authorship and recognition
Scientometrics and open science
Developing and evaluating metrics, data resources, and open citation practices that make research assessment more transparent and useful.
- iCite, RCR, and article-level metrics
- NIH Open Citation Collection
- Responsible research assessment