Ian Pan

DATA | MEDICINE | AI

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Featured

November 2018

Alexandre and I placed 1st in the RSNA Pneumonia Detection Challenge hosted on Kaggle. Take a look at our Solution Overview and GitHub repository for more details.

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

profile

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.

Skills

Python

Expert

R

Expert

SQL

Proficient

Machine Learning

Expert

Data Science

Expert

Statistics

Expert

Deep Learning

Expert

Education

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

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.