Alireza Rafiei

Alireza Rafiei

Curious AI Researcher

Alireza Rafiei’s Homepage

Hi there! I’m Alireza, a third-year (2022-now) Ph.D. student in Computer Science and Informatics program at Emory University, advised by Dr. Katebi and Prof. Clifford. My focus is on ML/AI systems in production, and I’m currently exploring creative use cases of AI in healthcare. Before Emory, I completed my M.S. degree at the University of Tehran. My passion lies in crafting innovative AI solutions and engineering techniques to tackle real-world challenges, blending cutting-edge technology with impactful applications. I’m driven by a deep curiosity to push the boundaries of AI research and collaborate on transformative projects.
Outside of work and school, I love playing soccer! I used to play it professionally when I was younger.

Research interests

  • Generative AI
  • AI for Health
  • Multimodal Learning
  • Data-Centric AI
  • Data Engineering
  • MLOps

Updates and news

  • [02/2025] Our paper “Tackling the small imbalanced horizontal dataset regressions by Stability Selection and SMOGN: a case study of ventilation-free days prediction in the pediatric intensive care unit and the importance of PRISM” has been accepted at International Journal of Medical Informatics.
  • [01/2025] Our paper “Auto-FEDUS: Autoregressive generative modeling of Doppler ultrasound signals from fetal electrocardiograms” has been accepted at The Association for the Advancement of Artificial Intelligence (AAAI) and will be presented at the Workshop on Large Language Models and Generative AI for Health (GenAI4H). Looking forward to seeing you at AAAI!
  • [12/2024] I have been honored with the 2024 BMI Award for Best Animation of Research for creating impactful visualizations of my work at Emory University.
  • [11/2024] Our paper “AutoFHR: A neural temporal model for fetal cardiac activity analysis” and App Demonstration “Edge AI for real-time fetal assessment in rural Guatemala” have been accepted at Machine Learning for Healthcare (ML4H). Looking forward to the impactful discussions at ML4H!
  • [09/2024] Our paper “Robust meta-model for predicting the need for blood transfusion in non-traumatic ICU patients” has been accepted for publication in Health Data Science.
  • [08/2024] Our paper “Meta-learning in healthcare: a survey” has been accepted for publication in SN Computer Science, bringing meta-learning and its transformative applications to the AI healthcare community.
  • [06/2024] I’ve received the Society of Critical Care Medicine’s (SCCM) Datathon participation and travel grant. Looking forward to impactful collaboration on improving patient outcomes.
  • [04/2024] “The 2024 Pediatric Sepsis Challenge: Predicting in hospital-mortality in children with suspected sepsis in Uganda” paper has been accepted for publication in Pediatric Critical Care Medicine and the data challenge will be launched soon! Don’t miss this exciting opportunity to collaborate, innovate, and leverage data science and AI to tackle real-world challenges.
  • [04/2024] Our paper “Social media as a sensor: Analyzing Twitter data for breast cancer medication effects using natural language processing” has been accepted for publication and presentation at Artificial Intelligence in Medicine (AIME). See you at AIME!
  • [03/2024] Our paper “Development and validation of a model for endotracheal intubation and mechanical ventilation prediction in PICU patients” paper has been accepted for publication in Pediatric Critical Care Medicine.
  • [01/2024] Our paper “Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit” has been accepted for publication in Computers in Biology and Medicine.
  • [01/2024] I’ve been awarded the Clinical and Single-cell Transcriptomics for Pneumonia codeathon and conference participation grant. Looking forward to tackling crucial real-world challenges!
  • [08/2023] Exiting news! Our paper, “Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model”, was recognized by the American College of Clinical Pharmacy (ACCP) as the 2023 Critical Care paper of the year!
  • [05/2023] Our paper “Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model” has been accepted for publication in the prestigious journal of Critical Care.
  • [04/2023] I’ve received the Bridge2AI CHoRUS workshop travel grant.
  • [07/2022] Our paper “Automated detection of major depressive disorder with EEG signals: a time series classification using deep learning” has been accepted for publication in IEEE Access.
  • [06/2022] Our paper “Pedestrian collision avoidance using deep reinforcement learning” accepted for publication in International Journal of Automotive Technology.
  • [03/2022] Exciting news! I’ve got a fully funded PhD admission to the Computer Science and Informatics program at Emory University! Looking forward to starting my PhD journey!
  • [01/2022] I have begun a remote research internship at Monash University as a machine learning researcher.
  • [01/2021] Our paper “FCOD: Fast COVID-19 Detector based on deep learning techniques” has been for publication in Informatics in Medicine Unlocked.
  • [01/2021] Our paper “SSP: Early prediction of sepsis using fully connected LSTM-CNN model” has been for publication in Computers in Biology and Medicine.





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