Jun Deng, PhD, DABR, FAAPM, FASTRO
Cards
About
Titles
Professor of Therapeutic Radiology
Director of Physics Research, Therapeutic Radiology; Associate Director of Medical Physics Residency Program, Therapeutic RadiologyBiography
Dr. Jun Deng is a Professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. Dr. Deng obtained his PhD from University of Virginia in 1998 and finished his postdoctoral fellowship at Stanford University in 2001. Currently with funding from NIBIB, NSF, NCI, DOE and YCC, Dr. Deng’s research has been focused on artificial intelligence, machine learning, big data, and medical imaging for early cancer detection, real-time clinical decision support, digital twins of cancer patients, as well as AI-empowered mobile health and smart medicine. Dr. Deng has been serving on the Editorial Board of numerous peer-reviewed journals, on the study sections of NIH, NSF, DOD, ACS, RSNA and ASTRO since 2005, and as scientific reviewer for various science foundations and institutions since 2015. Dr. Deng is an elected fellow of Institute of Physics, AAPM, and ASTRO.
Appointments
Therapeutic Radiology
ProfessorPrimaryBiomedical Informatics & Data Science
ProfessorSecondary
Other Departments & Organizations
- Biomedical Informatics & Data Science
- Center for Biomedical Data Science
- Deng Lab
- Orange College Affiliates
- Radiobiology and Genome Integrity
- Therapeutic Radiology
- Yale Cancer Center
- Yale Conference of Artificial Intelligence for Precision Medicine
- Yale Ventures
Education & Training
- Postdoctoral Fellowship
- Stanford University (2001)
- PhD
- University of Virginia (1998)
- BS
- Sichuan University (1991)
Research
Overview
Dr. Deng's research has been focused on AI for precision medicine, and AI for precision radiotherapy. Some active projects are listed below:
- Early cancer detection via statistical modeling of personal health data;
- Artificial intelligence for clinical decision support;
- Machine learning with radiation oncology big data;
- A generalizable data framework toward precision radiotherapy;
- Multiscale digital twin modeling of cancer patients;
- AI-empowered mobile health and smart medicine.
Medical Subject Headings (MeSH)
Academic Achievements and Community Involvement
Links & Media
Media
Digital Twins for Health
Dr. Jun Deng, Yale University School of Medicine's Professor of Therapeutic Radiology and Director of Physics Research, talks to us about his new initiative, the Digital Twins for Health Consortium (http://dt4h.org). https://www.youtube.com/watch?v=VyK6-9WwMhg&t=3s.Insights in AI: Medicine and Public Health 2022
This Research Topic is part of the Insights in Artificial Intelligence series (https://www.frontiersin.org/research-topics/33669/insights-in-ai-medicine-and-public-health-2022)Big Data Analytics for Precision Health and Prevention
A research topic dedicated to big data analytics for precision health and prevention (https://www.frontiersin.org/research-topics/10068/big-data-analytics-for-precision-health-and-prevention)Big Data in Radiation Oncology
A book looking into how big data is having an impact on the clinical care of cancer patients (https://www.amazon.com/Radiation-Oncology-Imaging-Medical-Diagnosis/dp/1138633437)Machine Learning with Radiation Oncology Big data
A research topic focused on machine learning with big data in radiation oncology (https://www.frontiersin.org/research-topics/6126/machine-learning-with-radiation-oncology-big-data)Artificial Intelligence for Precision Medicine
A research topic dedicated to artificial intelligence for precision medicine (https://www.frontiersin.org/research-topics/12708/artificial-intelligence-for-precision-medicine)
News
- June 01, 2024
Accolades, Awards & Honors
- March 05, 2024Source: National Institutes of Health
Jun Deng, PhD, DABR, FAAPM, FASTRO, selected as an expert for NIH AIM-AHEAD PAIR Program
- September 15, 2021
Community Research Fellowship Program has Successful Inaugural Year
- March 24, 2021
Teaching Medicine to Machines: Using AI and Machine Learning to Improve Health Care