L. Zhang Source Confirmed

OverviewAI-generated summary

Lu Zhang's research program focuses on developing and evaluating methods for achieving fairness in sequential decision-making processes, particularly within machine learning contexts. This work aims to ensure that algorithms do not perpetuate or exacerbate existing societal biases over time. Zhang has secured significant funding from the National Science Foundation (NSF) to advance this research.

Two NSF grants underscore the scope of this work. The first, a CAREER award totaling $597,185, supports the project "Towards Long-term Fairness in Sequential Decision Making." The second, an III:Small grant for $484,828, funds research on "Counterfactually Fair Machine Learning through Causal Modeling." These grants highlight Zhang's role as a principal investigator leading federally funded research in this specialized area.

Zhang's scholarly output includes a recent publication in 2026, indicating ongoing activity in the field. The researcher's metrics show an h-index of 2 and a total of 2 publications with 15 citations.

Metrics

• h-index: 1
• Publications: 2
• Citations: 10