S
Saima Absar Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
2 h-index
4 pubs
12 cited
Research Areas
Biography and Research Information
OverviewAI-generated summary
Saima Absar's research investigates causal discovery from time series data, focusing on methods to identify time-invariant causal structures. Her work explores the application of neural networks and hybrid approaches to uncover these relationships within temporal datasets. Additionally, Absar has conducted pilot studies utilizing activity trackers to identify critically ill subjects, indicating an interest in the application of technology in health monitoring and emergency care.
Metrics
- h-index: 2
- Publications: 4
- Citations: 12
Selected Publications
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Time Series Causal Discovery Using a Hybrid Method (2024)
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Neural Time-Invariant Causal Discovery from Time Series Data (2023)
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293: UTILIZATION OF ACTIVITY TRACKERS TO IDENTIFY CRITICALLY ILL SUBJECTS: A PILOT STUDY (2022)
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Discovering Time-invariant Causal Structure from Temporal Data (2021)
Collaboration Network
Top Collaborators
Lu Zhang
University of Arkansas at Fayetteville (US)
4 shared publications
- Neural Time-Invariant Causal Discovery from Time Series Data
- Discovering Time-invariant Causal Structure from Temporal Data
- 293: UTILIZATION OF ACTIVITY TRACKERS TO IDENTIFY CRITICALLY ILL SUBJECTS: A PILOT STUDY
- Time Series Causal Discovery Using a Hybrid Method
Donald Adjeroh
West Virginia University (US)
1 shared publication
- 293: UTILIZATION OF ACTIVITY TRACKERS TO IDENTIFY CRITICALLY ILL SUBJECTS: A PILOT STUDY
Gregory Schaefer
West Virginia University (US)
1 shared publication
- 293: UTILIZATION OF ACTIVITY TRACKERS TO IDENTIFY CRITICALLY ILL SUBJECTS: A PILOT STUDY
James M. Bardes
West Virginia University (US)
1 shared publication
- 293: UTILIZATION OF ACTIVITY TRACKERS TO IDENTIFY CRITICALLY ILL SUBJECTS: A PILOT STUDY
Yongkai Wu
Clemson University (US)
1 shared publication
- Neural Time-Invariant Causal Discovery from Time Series Data
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