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Dr. Parisa Rashidi received her PhD in computer science with an emphasis on machine learning. She is currently an associate professor at the J. Crayton Pruitt Family Department of Biomedical Engineering (BME) at University of Florida (UF). She is also affiliated with the Electrical & Computer Engineering (ECE), as well as Computer & Information Science & Engineering (CISE) departments. She is the director of the “Intelligent Health Lab” (i-Heal).  Her research aims to bridge the gap between machine learning and patient care.

Dr. Rashidi is a National Science Foundation (NSF) CAREER awardee, the National Institute of Health (NIH) Trailblazer Awardee, Herbert Wertheim College of Engineering (HWCOE) Assistant Professor Excellence Awardee, and a recipient of the UF term professorship. She is also a recipient of UF’s Provost excellence award for assistant professors.

To date, she has authored 80+ peer-reviewed publications. She has chaired numerous workshops and symposiums on intelligent health systems and has served on the program committee of 20+ conferences. Over the past few years, she has received $10.6M in extramural research funds with collaborators and $3.05M as an individual investigator. Dr. Rashidi’s research has been supported by local, state, and federal grants, including awards from the National Institutes of Health (NIBIB, NCI, and NIGMS) and the National Science Foundation (NSF).

Featured Publications

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning

Journal Article
Davoudi, Anis, Kumar Rohit Malhotra, Benjamin Shickel, Scott Siegel, Seth Williams, Matthew Ruppert, Emel Bihorac, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Azra Bihorac, and Parisa Rashidi
Nature, Scientific Reports, 9, Article number: 8020 (2019).
Publication year: 2019

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

Journal Article
Benjamin Shickel, Tyler J. Loftus, Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Azra Bihorac & Parisa Rashidi
Nature Scientific Reportsvolume 9, Article number: 1879 (2019)
Publication year: 2019

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Journal Article
Benjamin Shickel, Patrick James Tighe, Azra Bihorac, Parisa Rashidi
IEEE Journal of Biomedical and Health Informatics
Publication year: 2018

Featured Research Projects

  • Intelligent ICU

    Our long-term goal is to sense, quantify, and communicate patient condition in an autonomous, precise, and personalized manner. We plan to build the foundation of an intelligent ICU by developing and validating pervasive context sensing, precise context inference, and situation-aware context communication using novel sensing and artificial intelligence technologies. This project is carried out at Intensive Care Units (ICU) at UF Health hospitals (UFH).


  • Trajectory Prediction in Hospitalized Patients

    Predicting the status of patient throughout their hospital stay

    We expect to facilitate early interventions by precisely predicting a patient’s clinical trajectory using high-resolution data and using accurate deep learning models.

  • In-Community Health Monitoring

    Our long term goal is to develop an ecologically momentary assessment tool for capturing patient status in real time in the context of momentary physiologic and psychometric data.

Honors and Awards

  • 2019
    Mitchell Max Award Finalist
    Mitchell Max Award Finalist, National Institute of Health (NIH)
  • 2014
    Provost’s Excellence Award for Assistant Professors
    Provost’s Excellence Award for Assistant Professors, University of Florida (UF Excellence Award)
  • 2019
    HWCOE Excellence Award for Assistant Professors
    Excellence Award for Assistant Professors, Herbert Wertheim College of Engineering (HWCOE Excellence Award)
  • 2019
    NIH Trailblazer Award
    The National Institute of Biomedical Imaging and Bioengineering (NIBIB) Trailblazer R21 Award was awarded for assessing pain management in ICU patients.

    The under-assessment of pain response is one of the primary barriers to the adequate treatment of pain in critically ill patients and is associated with many negative outcomes such as chronic pain after discharge, prolonged mechanical ventilation, longer ICU stay and increased mortality risk.

    The objective is to build the foundation of an autonomous, clinically-available pain assessment system by developing and validating pain recognition algorithms in a fully uncontrolled ICU setting. The proposed research is relevant to public health because it can result in enhanced critical care workflow, ultimately improving patient outcomes and decreasing hospitalization costs.

  • 2018
    Term Professorship
    UF Term Professorship for excellence in Research, Teaching, Service
  • 2018
    National Science Foundation Faculty Early Career Development Program (NSF CAREER)
    This award will allow to advance exploration of machine learning algorithms and critical care medicine. Precise assessment and prediction of patient status in the ICU can enable early interventions and can result in improved patient outcomes. However, today’s ICUs still face many barriers for assessing and predicting patient status. Essential information such as pain and functional status are not captured automatically, but rather are repetitively measured by ICU nurses and existing methods have limited accuracy and infrequently used. This leads to missing opportunities for early interventions. This will represent the first attempt to autonomously assess pain and functional status in the ICU, to predict precise patient trajectory from high-resolution data, and to improve predictive clinical models through user feedback. In addition, the research will contribute to a broader understanding of future design considerations for the next generation of lifelong learning systems and intelligent hospitals.
  • 2017
    National Academy of Engineering (NAE), Frontiers of Engineering
    The Frontiers of Engineering program brings together a select group of emerging engineering leaders from industry, academe, and government labs to discuss pioneering technical work and leading edge research in various engineering fields and industry sectors.
  • 2015
    Biomedical Engineering Society (BMES) Career Development Award
  • 2015
    Microsoft Faculty
    Invited Summit Participant
  • 2014
    National Science Foundation Travel Award
    Computing Challenges in Future Mobile Health Systems and Applications Workshop
  • 2011
    The Outstanding Dissertation Award
    Washington State University, WA


  • PhD January 2008 - May 2011

    PhD in Computer Science - Applied Machine Learning

    Washington State University

  • M.Sc. September 2006 - December 2007

    M.Sc. in Computer Science - Applied Machine Learning

    Washington State University

  • B.Sc. September 2000 - September 2005

    B.Sc. in Computer Engineering

    University of Tehran


  • Present2020

    Associate Professor

    University of Florida

  • 2020August 2013

    Assistant Professor

    University of Florida, Biomedical Engineering

  • present2013

    Affiliated Assistant Professor

    University of Florida, Electrical & Computer Engineering

  • present2016

    Affiliated Assistant Professor

    University of Florida, Computer & Information Science & Engineering

  • June 2013September 2012

    Assistant Professor

    Feinberg School of Medicine, Northwestern University