Ian Pan


About Me


My name is Ian, and I'm currently a medicine intern at University Hospitals Cleveland Medical Center in Cleveland, Ohio. I will be a radiology resident at Brigham & Women's Hospital in Boston, Massachusetts starting July 2021. I studied applied math and statistics at Brown University and also received my MD degree from the Warren Alpert Medical School of Brown University.

My passion lies at the intersection of machine learning, data science, and healthcare. I believe we can integrate AI into medicine to make life easier for doctors and better for patients. My present focus is on computer vision and deep learning. I love PyTorch and hate TensorFlow.

I like watching K-dramas, videos about food, taking naps, and eating ice cream. I also enjoy competing on Kaggle - I am the world's first physician & Kaggle Grandmaster.

I was born in Urbana, Illinois and grew up in the suburbs of Los Angeles, California. I spent 8 years in Rhode Island for school and now consider myself a proud New Englander.

Twitter | LinkedIn | GitHub | CV | Email Me








Machine Learning


Data Science




Deep Learning



Warren Alpert Medical School

Doctor of Medicine

May 2020

Brown University

Master of Arts, Biostatistics

May 2016

Brown University

Bachelor of Science, Applied Mathematics-Biology

May 2016


Machine Learning Engineer

3D Laboratory

Rhode Island Hospital | Providence, RI

Oct 2016 - Present

Apply machine learning to various medical image analysis problems.

Automatic detection and segmentation of intracranial hemorrhage in head CT scans using 3D convolutional neural networks (CNNs).

Screening for pathological findings in chest radiographs using CNNs.

Bone age estimation from pediatric hand radiographs using CNNs.

Malignancy scoring of thyroid nodules viewed on ultrasound using CNNs.

Data Extractor Lead

Center for Evidence Synthesis in Health

Brown University | Providence, RI

Oct 2015 - Oct 2016

Extracted and reviewed quantitative data from over 300 clinical trials and observational studies.

Proofread and edited data extractions performed by other data extractors.

Assembled all data into a central dataset for client use.

Contributed to systematic review examining efficacy of tympanostomy tubes in children with otitis media.

Data Science Fellow

Data Science for Social Good Fellowship

University of Chicago | Chicago, IL

May 2015 - Aug 2015

Used data science to identify women at high risk of experiencing an adverse birth outcome.

Developed a machine learning pipeline to extract, transform, and load data from administrative databases that were used to train machine learning models.

Developed a web application to facilitate client and provider usage in the field.

Published methodology and findings in the American Journal of Public Health: Pan et al. (2017) Machine Learning for Social Services: A Study of Prenatal Case Management.

GitHub open source repository available here.