Hung P. Do, PhD, MSEE

Manager Medical Affairs - Senior Clinical Scientist (MRI Physicist)
Email   Homepage   Google Scholar   LinkedIn   Github   Work   Tustin, CA

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Expertise and Skills

  • Magnetic Resonance Imaging (MRI) Physics, MRI Pulse Sequence Design, and Advanced Image Reconstruction

  • Signal and Image Processing

  • Design and execute scientifically sound and FDA 510k-cleared hypothesis-driven and clinical-evaluation research studies

  • Applications of Statistics, Data Science, Machine Learning, and Deep Learning to MRI, Medical Imaging, and Healthcare

  • Clinical evaluations and translations of innovative imaging solutions into clinical practice

  • End-to-end project management and multidisciplinary collaborations

  • Fundamental and translational MRI research related to quantitative imaging, novel pulse sequence design, and advanced image reconstruction

  • Programmable animation, data visualization, data analysis, and statistics using Python and R

  • Bayesian methods and probabilistic programming for quantitative imaging with uncertainty quantification

  • Mathematical modeling, numerical simulations, and optimization

  • Automatic and reproducible pipelines for data curation, cleaning, and visualization, statistical analysis, and report/presentation generation using Git version control, Bash, Python, R, and related packages

  • Operating Systems: Linux, macOS, and Microsoft Windows

  • Programming Languages: Python, R, Bash, EPIC (General Electric (GE) Healthcare’s C-based MRI pulse sequence programming language), RSDE (Canon Medical Systems’ Cpp-based MRI pulse sequence programming language)

  • Packages and Libraries: Pandas, NumPy, PyDicom, SciPy, Matplotlib, Tidyverse, ggplot2, Seaborn, fastai, TensorFlow-Keras, PyTorch, etc.

  • Version Control: Git and GitHub

  • Tools: Microsoft Office, Command Line (Terminal), Markdown, \(\LaTeX\), OsiriX, Visual Studio Code, Anaconda, etc.



Honors and Awards


  • Data Science: Statistics and Machine Learning, a 5-Course Specialization, Profs. Jeff Leek, Roger Peng, Brian Caffo, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University

    • Course 1: Statistical Inference
    • Course 2: Regression Model
    • Course 3: Practical Machine Learning
    • Course 4: Developing Data Products
    • Course 5: Data Science Capstone Project
  • Data Science: Foundations using R, a 5-Course Specialization, Profs. Jeff Leek, Roger Peng, Brian Caffo, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University

    • Course 1: The Data Scientist’s Toolbox
    • Course 2: R Programming
    • Course 3: Getting and Cleaning Data
    • Course 4: Exploratory Data Analysis
    • Course 5: Reproducible Research
  • AI for Healthcare, a 5-Course Specialization, Profs. Nigam Shah, Laurence Baker, David Magnus, Serena Yeung, Mildred Cho, Steven Bagley, Matthew Lungren, Tina Hernandez-Boussard, Stanford University

    • Course 1: Introduction to Healthcare
    • Course 2: Introduction to Clinical Data
    • Course 3: Fundamentals of Machine Learning for Healthcare
    • Course 4: Evaluations of AI applications in Healthcare
    • Course 5: AI in Healthcare Capstone Projects
  • Statistics with Python, a 3-Course Specialization, Brenda Gunderson PhD, Brady T. West PhD, Kerby Shedden PhD, University of Michigan

    • Course 1: Understanding and Visualizing Data with Python
    • Course 2: Inferential Statistical Analysis with Python
    • Course 3: Fitting Statistical Models to Data with Python
  • AI for Medicine, a 3-Course Specialization, Pranav Rajpurkar PhD candidate, Stanford University and

    • Course 1: AI for Medical Diagnosis
    • Course 2: AI for Medical Prognosis
    • Course 3: AI for Medical Treatment
  • Improving your statistical inferences, Daniel Lakens PhD, Eindhoven University of Technology

  • Improving Your Statistical Questions, by Daniel Lakens PhD, Eindhoven University of Technology

  • Neural Networks for Machine Learning, Prof. Geoffrey E. Hinton, University of Toronto

  • Deep Learning, a 5-Course Specialization, Prof. Andrew Ng, and Stanford University

    • Course 1: Neural Networks and Deep Learning
    • Course 2: Improving Deep Neural Networks: Hyper-parameter tuning, Regularization and Optimization
    • Course 3: Structuring Machine Learning Projects
    • Course 4: Convolutional Neural Networks
    • Course 5: Sequence Models
  • Machine Learning, Prof. Andrew Ng, Stanford University

  • Introduction to Julia DataFrames, Prof. Bogumił Kamiński, the author of the DataFrames.jl package, Warsaw School of Economics

  • Julia for Data Science, Dr. Huda Nassar, Stanford University

  • Introduction to Julia (for programmer), Dr. Jane Herriman, Director of Diversity and Outreach at Julia Computing, California Institute of Technology (Caltech)

Selected Publications

(out of 6 journal papers, 6 whitepapers, and >35 conference and workshop abstracts)

  • HP Do, Y Guo, AJ Yoon, and KS Nayak. “Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN.” Magnetic Resonance in Medicine 2020; 83:1863–1874. PDF JRNL

  • HP Do, V Ramanan, X Qui, J Barry, GA Wright, NR Ghugre, KS Nayak. “Non-Contrast Assessment of Microvascular Integrity using Arterial Spin Labeled CMR in a Porcine Model of Acute Myocardial Infarction.” Journal of Cardiovascular Magnetic Resonance 20:45, July 2018. PDF JRNL

  • HP Do, AJ Yoon, MW Fong, F Saremi, ML Barr, KS Nayak. “Double-gated Myocardial Arterial Spin Labeled Perfusion Imaging is Robust to Heart Rate Variation.” Magnetic Resonance in Medicine 77(5):1975-1980, 2017. PDF JRNL

Teaching and Training Experience

Peer Reviewer



Jogging, Running, Hiking, Nature, Science and Technology

(updated Dec 31, 2021)