Warren Alpert Medical School
Doctor of Medicine
May 2020
Ian Pan
DATA | MEDICINE | AI
Featured
September 2019
Felipe and I placed 8th out of 1475 teams in the SIIM-ACR Pneumothorax Segmentation competition on Kaggle. [GitHub & Solution Overview]
November 2018
Alexandre and I placed 1st out of over 1400 teams in the RSNA Pneumonia Detection Challenge hosted on Kaggle. [GitHub & Solution Overview]
Phillip, Alex, and I also authored a publication in AJR about the challenge.
October 2017
I placed 2nd in the RSNA Pediatric Bone Age Challenge, the inaugural machine learning competition sponsored by RSNA.
May 2017
Our paper Machine Learning for Social Services: A Study of Prenatal Case Management has been published in the American Journal of Public Health.
About Me
My name is Ian, and I'm currently a 4th-year medical student at the Warren Alpert Medical School of Brown University in Providence, Rhode Island. I'm applying to radiology residencies.
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.
I like watching TV, videos about food, taking naps, and eating ice cream.
I was born in Urbana, Illinois and grew up in Los Angeles, California, but now I consider myself a proud Rhode Islander.
Skills
Python
Expert
R
Expert
SQL
Proficient
Machine Learning
Expert
Data Science
Expert
Statistics
Expert
Deep Learning
Expert
Education
Brown University
Master of Arts, Biostatistics
May 2016
Brown University
Bachelor of Science, Applied Mathematics-Biology
May 2016
Experience
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.