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Dr. Mozziyar Etemadi: Faculty Experts

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Dr. Mozziyar Etemadi

McCormick School of Engineering and Applied Science, Northwestern University Feinberg School of Medicine

Assistant Professor of Anesthesiology
Assistant Professor of Biomedical Engineering


Areas of Focus

  • Anesthesiology
  • Artificial intelligence (AI)
  • Healthcare analytics
  • Healthcare workflow optimization
  • Wearable sensors
  • Physiologic monitoring
  • Low-power embedded systems


  • Artificial intelligence and machine learning algorithm development for healthcare
  • Healthcare middleware for workflow augmentation and machine learning applications
  • Wearable devices for non-invasive, predictive assessments of physiology and pathophysiology
  • Ultra-low power embedded systems for healthcare applications


Mozzi grew up in the Chicago area and fell in love with technology at an early age, first making websites for local businesses, then working for a start-up internet service provider that used wireless radios to provide broadband to areas that were still on dial-up. He then spent 12 years in the San Francisco Bay Area, training in electrical engineering and finishing medical school. During this time he found several opportunities to bring state-of-the-art technology to solve an actionable, clinical need and was named Forbes “30 Under 30” Scientists. He returned to Chicago because a unique opportunity presented itself at Northwestern Medicine – the flexibility to put top notch engineers inside the clinical environment. Mozzi’s research team at Northwestern started inside one of the intensive care units (ICUs), with engineers working literally feet away from patient rooms. Together with the nurses, physicians, and other staff of the ICU, his team continued to use advanced technologies to solve active problems in clinical workflow, predictive analytics, and wearable physiologic monitoring. Most recently, through a large collaboration with Google Research, Mozzi’s team contributed to a novel, AI-based algorithm that screens patients for lung cancer. Promising early results indicate that this tool can outperform radiologists and can spot cancer sometimes years before the current state of the art.