Anahita Khojandi
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor - Engineering
Also affiliated: Christiana Care Health System (2017); Georgia Institute of Technology (2017); Harvard University (2017); University of Pittsburgh (2014); University of Michigan (2017–2020); University of Tennessee System (2020–2025); Massachusetts General Hospital (2017); University of Tennessee at Knoxville (2017–2026)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Anahita Khojandi's research focuses on the application of machine learning and data-driven modeling to address complex problems in healthcare and engineering. Her work includes developing algorithms for real-time sensor anomaly detection and recovery, particularly in the context of automated vehicles and connected automated vehicle sensors. She has investigated methods for predicting the onset of sepsis earlier by analyzing continuous high-frequency physiological data streams, and has explored the use of wearable sensors and data modeling to optimize clinical assessments for Parkinson's Disease.
Khojandi has also applied artificial intelligence techniques to the review of human factor errors in nuclear power plants. Her research publications demonstrate a commitment to improving prediction performance in real-time data analysis, with a case study focusing on sepsis prediction. She has also explored dynamic deep reinforcement learning frameworks for anomaly detection. Her work has led to a publication record of 128 papers, with an h-index of 25 and over 2,000 citations.
Metrics
- h-index: 25
- Publications: 130
- Citations: 2,035
Positions
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University of Arkansas at Fayetteville publications 2026ORCID
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Professor - Engineering publications 2026University of Arkansas at Fayetteville Institution web page
Selected Publications
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X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations (2026)
Collaboration Network
Top Collaborators
- X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations
- X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations
- X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations
- X-Admix: An Interpretable Multimodal Cross-Attention Framework for Integrating Genotype, Local Ancestry, and Social Drivers of Health in Admixed African American Populations
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