L. Zhang

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Federal Grant PI

Researcher

University of Arkansas

Graduate Student Researcher

1 h-index 3 pubs 10 cited

Research Areas

OverviewAI-generated summary

L. Zhang's research focuses on advancing fairness in machine learning, particularly within sequential decision-making contexts. Zhang has contributed to developing methodologies for algorithmic recourse, aiming to provide individuals with actionable steps to achieve fairer outcomes from algorithmic systems. This work has been supported by two NSF grants: the CAREER award, "Towards Long-term Fairness in Sequential Decision Making" ($597,185), and "III:Small: Counterfactually Fair Machine Learning through Causal Modeling" ($484,828).

Recent publications by Zhang include "FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents" (2025) and "Algorithmic recourse in sequential decision-making for long-term fairness" (2026). These works highlight an active engagement with contemporary challenges in artificial intelligence and machine learning, exploring the practical implementation and theoretical underpinnings of fairness-aware systems. Zhang has collaborated with Francisco Gumucio at the University of Arkansas at Fayetteville on shared publications.

Metrics

h-index: 1
Publications: 3
Citations: 10

Selected Publications

Algorithmic recourse in sequential decision-making for long-term fairness (2026)

Frontiers in Big Data DOI OpenAlex
FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents (2025)

DOI OpenAlex
Novel Nanomaterials for Developing Bone Scaffolds and Tissue Regeneration (2025)

Nanomaterials 22 citations DOI OpenAlex
Fair Multiple Decision Making Through Soft Interventions (2020)

Neural Information Processing Systems 9 citations OpenAlex
Selective etching of InGaAs∕GaAs(100) multilayers of quantum-dot chains (2005)

Applied Physics Letters 10 citations DOI OpenAlex