Alycia N. Carey
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Researcher
Also affiliated: Sandia National Laboratories California (2021)
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Alycia N. Carey's research focuses on machine learning, particularly in areas related to fairness, privacy, and robustness. Her work investigates methods for ensuring equitable outcomes in machine learning models, exploring concepts like statistical and causal fairness. Carey has published on the development of algorithms that address demographic fairness heterogeneity in personalized federated learning and on techniques for in-context learning with differentially private tabular data.
Her research also extends to explainability and utility in machine learning. This includes work on sensitivity analysis to guide explainability in intrusion detection systems and evaluating the impact of local differential privacy on utility loss using influence functions. Additionally, Carey has explored robust training methods for noisy brain MRI data, aiming to improve the reliability of medical imaging analysis. She has collaborated with several researchers at the University of Arkansas at Fayetteville, including Minh-Hao Van and Xintao Wu.
Metrics
- h-index: 5
- Publications: 23
- Citations: 143
Selected Publications
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Achieving Distributive Justice in Federated Learning via Uncertainty Quantification (2026)
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Influence-based approaches for tumor classification in noisy brain MRI with deep learning and vision-language models (2025)
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DP-TabICL: In-Context Learning with Differentially Private Tabular Data (2024)
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Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions (2024)
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Robust Influence-Based Training Methods for Noisy Brain MRI (2024)
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HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks (2023)
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Randomized Response Has No Disparate Impact on Model Accuracy (2023)
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Robust Personalized Federated Learning under Demographic Fairness Heterogeneity (2022)
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The statistical fairness field guide: perspectives from social and formal sciences (2022)
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The Causal Fairness Field Guide: Perspectives From Social and Formal Sciences (2022)
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Adversarial attacks against image-based malware detection using autoencoders (2021)
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A Cancelable Multi-Modal Biometric Based Encryption Scheme for Medical Images (2020)
Collaboration Network
Top Collaborators
- The statistical fairness field guide: perspectives from social and formal sciences
- The Causal Fairness Field Guide: Perspectives From Social and Formal Sciences
- Robust Personalized Federated Learning under Demographic Fairness Heterogeneity
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- Randomized Response Has No Disparate Impact on Model Accuracy
Showing 5 of 9 shared publications
- Robust Influence-Based Training Methods for Noisy Brain MRI
- Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
- HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
- Influence-based approaches for tumor classification in noisy brain MRI with deep learning and vision-language models
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- Randomized Response Has No Disparate Impact on Model Accuracy
- Adversarial attacks against image-based malware detection using autoencoders
- Adversarial attacks against image-based malware detection using autoencoders
- Adversarial attacks against image-based malware detection using autoencoders
- Robust Personalized Federated Learning under Demographic Fairness Heterogeneity
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- Achieving Distributive Justice in Federated Learning via Uncertainty Quantification
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