I placed 2nd in the RSNA Pediatric Bone Age Challenge, the inaugural machine learning competition sponsored by RSNA.
Our paper Machine Learning for Social Services: A Study of Prenatal Case Management has been published in the American Journal of Public Health.
My name is Ian, and I'm currently a 3rd-year medical student at the Warren Alpert Medical School of Brown University in Providence, Rhode Island.
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.
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.
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.
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.