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Biography and Research Information
OverviewAI-generated summary
Maryam Kheirandish's research centers on the application of machine learning and deep learning algorithms for risk-based decision-making, particularly in health-related contexts. Her work includes developing frameworks for dynamic prediction of treatment outcomes, such as in tuberculosis, and quantifying uncertainty in deep learning models when dealing with noisy or discrete input data. Kheirandish has collaborated with researchers including Shengfan Zhang and Donald G. Catanzaro at the University of Arkansas at Fayetteville, co-authoring multiple publications with them. Her scholarship metrics include an h-index of 2, with 3 total publications and 20 total citations.
Metrics
- h-index: 2
- Publications: 3
- Citations: 20
Selected Publications
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Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making (2025)
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Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes (2022)
Collaboration Network
Top Collaborators
Donald G. Catanzaro
University of Arkansas at Fayetteville
3 shared publications
In database
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making
Valeriu Crudu
Phthisiopneumology Institute "Chiril Draganiuc" (MD)
3 shared publications
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making
Shengfan Zhang
University of Arkansas at Fayetteville
3 shared publications
In database
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes
- Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making
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