Predicting ACL injuries and recovery time in athletes, accepted at an IEEE conference

ACL tears are among the most common serious injuries in sport, and most research looks at anatomy while ignoring what athletes actually do in training. Ben built interpretable machine learning models, including Random Forest and Gradient Boosting, on basketball athlete performance data such as training hours and intensity. His work predicts not just the risk of a first-time ACL injury but also how long recovery will take, a question most existing studies treat as a fixed input rather than something to forecast.

FIELDSports Medicine & AI

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

BEFORE THE FELLOWSHIP

A high school student and basketball fan who wanted to understand why ACL tears end so many athletic careers.

AFTER

First author of machine learning models that predict both ACL injury risk and recovery time from athlete performance data, accepted at an IEEE conference.

THE LEDGER

01First-author paper accepted at an IEEE conference

02Predicts first-time ACL injury risk, not just re-injury

03Forecasts recovery time, which most prior studies do not model

04Interpretable Random Forest and Gradient Boosting models on real training data

NEXT CASE STUDYManomay Jain, First-author paper accepted at an IEEE conference

Every case study starts with one application.

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