Member Database

Sardar Ansari

Research Assistant Professor; Director, Data Science at The Weil Institute for Critical Care Research and Innovation

PhD, Computer Science, Virginia Commonwealth University
MS, Statistics, Virginia Commonwealth University
MS, Computer Science, Virginia Commonwealth University
BS, Software Engineering, University of Tehran

Dr. Ansari is a Research Assistant Professor in the Department of Emergency Medicine at the University of Michigan. He is the Director of the Data Science Unit at the Michigan Center for Integrative Research in Critical Care where he uses computational techniques such as signal and image processing, natural language processing, and machine learning to create analytical tools to improve patient care and outcomes. He also uses wearable devices to augment data obtained from electronic health records and bedside monitors to create more accurate diagnostic and prognostic models for various medical conditions. Dr. Ansari’s research interests span critical care, including prediction of adverse events, heart failure, myocardial infarction, cardiac arrhythmia, sepsis, hemodialysis, and non-invasive patient monitoring.

Dr. Ansari earned his PhD in Computer Science and his MS in Statistics at Virginia Commonwealth University (VCU). He received his bachelor’s degree in Software Engineering from the University of Tehran, Electrical and Computer Engineering Department, and his MS in Computer Science from VCU. He joined the Department of Emergency Medicine in 2020 as faculty after being a research fellow in the departments of Computational Medicine and Bioinformatics and Emergency Medicine at the University of Michigan.


COVID-19 detection through scent analysis with a compact GC device, Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome, Correcting Racial Disparities in SpO2 Measurements, Deep Learning and Commercial Wearables, Implementation of an All‐Cause Deterioration Model for Adult Inpatients, Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), Prediction of Heart Failure Onset using Multimodal Data Analysis, Vascular Tone Monitoring using a Novel Wearable Ring

University Affiliation(s)


Community and Professional Affiliation(s)

American Heart Association | IEEE

Research Area(s)

Artificial Intelligence | Biostatistics | Critical Care | Data science / Analytics / AI | Deep neural networks | Health Analytics | Implementation | Other | Sensors and Wearables | cardiovascular diseases | machine learning | mobile health | predictive modeling | statistical signal processing


  • Co-investigator of: Systolic Target Assessment Tool for hemorrhage monitoring
  • Co-investigator of: Validating Non-invasive Sensors for Compensatory Hemodynamics as Predictors of Decompensation
  • Co-investigator of: Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome
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