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Nima Aghaeepour

Nima Aghaeepour

Professor, AI & Medicine researcher, pilot, former StarCraft Grandmaster

Stanford, California
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Joined May 2025
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Summary

Nima Aghaeepour is a Professor at Stanford University across multiple departments including Anesthesiology, Perioperative and Pain Medicine, Pediatrics, and Biomedical Data Science. He also holds the position of Vice Chair for Research (Data Science) and Professor in Residence at StartX. His primary research focus is on developing machine learning and artificial intelligence methods to study clinical and biological modalities, with a particular emphasis on translational settings, multiomics studies, wearable devices, and electronic health records to address global health challenges. stanford+2
Aghaeepour is a highly cited researcher with a Google Scholar h-index of 50 and 123 i10-index. His publications cover a wide range of topics in machine learning, artificial intelligence, systems immunology, data integration, and wearable devices, with notable contributions to areas like automated flow cytometry data analysis, an immune clock of human pregnancy, and AI-driven precision total parenteral nutrition in neonatal intensive care. google+1
He serves as a Scientific Advisory Board Member for several companies in the health and biotech sectors, including Medeloop, Parallel Bio, MaraBio Systems Inc., WellSIM BioMedical Technologies, and January AI, demonstrating his expertise and influence in applying AI and data science to real-world health solutions. medeloop+3
Beyond his academic and professional pursuits, Nima Aghaeepour has a diverse range of personal interests. He is an airplane and wingsuit pilot, a former StarCraft2 Grandmaster, and enjoys playing the piano, swimming, mountain climbing, chess, target shooting, modern art, and studying Mandarin Chinese. stanford+1

Work

Education

Writing

AI-driven Precision Total Parenteral Nutrition in Neonatal Intensive Care Units with TPN2.0

January 1, 2025

Pioneering work in using AI to guide critical treatment decisions in neonatal care, specifically for total parenteral nutrition. The AI model analyzed ~10 years of data to automate the process, increasing safety and reducing cost.

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A machine learning approach to leveraging electronic health records for enhanced omics analysis

January 1, 2025

Demonstrates how deep learning combined with electronic health record data can significantly improve omics models.

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Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

January 1, 2024

Discusses the use of foundation AI models to generate proteomics data from clinical records, potentially eliminating the need for blood draws and approximating proteomics data for millions of patients.

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Data-Driven Longitudinal Characterization of Neonatal Health and Morbidity

January 1, 2023

Comprehensive analysis of neonatal health, utilizing a data-driven approach.

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An immune clock of human pregnancy

January 1, 2017

Explores the immune system's changes during human pregnancy using a systems immunology approach.

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Critical assessment of automated flow cytometry data analysis techniques

January 1, 2013

A highly cited work contributing to the standardization and improvement of flow cytometry data analysis.

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Hobbies

He is an airplane and wingsuit pilot, a former StarCraft2 Grandmaster, and plays piano. stanford+1

His other interests include swimming, mountain climbing, chess, target shooting, modern art, and Mandarin Chinese. stanford