JHU Engineering

Design Day

Johns Hopkins Engineering Design Day is the Whiting School’s premier event that showcases the innovative works of Hopkins engineering students. Come see how students implement their classroom knowledge, creativity, and problem-solving skills to develop inventions and processes that solve real-world problems and create a better future.​​

Congratulations to all on a fantastic 2025 event!

Information on JHU Engineering Design Day 2026 coming soon.

Schedule At-a-Glance

8:30 to 11:30 a.m. | Student Presentations
12 to 1:30 p.m. | Keynote Session and Lunch
1:30 to 3:30 p.m. | Poster Session
3:30 to 4 p.m. | Awards Presentation and Closing Remarks

OOTD – Virtual Closet Android App

For EN.601.290 (User Interfaces & Mobile Applications), our group developed a mobile app that allows users to log their clothing into a virtual closet, customize and save outfits, and view their closet at a glance on their phones. We hope this project will foster creativity and experimentation, encourage users to buy less and wear more of what they have, and have fun with fashion without the need for any special purchases beyond what they already own.

Predicting Hospital Readmission Following Acute Kidney Injury

Acute Kidney Injury (AKI) is a sudden loss of renal filtration capacity that leads to toxin and fluid accumulation. Patients who survive AKI-related hospitalizations are frequently readmitted, contributing to poor outcomes and healthcare strain. However, existing readmission risk tools are often based on small, single-center cohorts and lack generalizability.

To address this, we linked the Johns Hopkins Precision Medicine Analytics Platform with Kaiser Permanente Mid-Atlantic EHR data to construct a large, multi-center post-AKI cohort of over 60,000 admissions, integrating demographics, comorbidities, lab trends, vital signs, and other physiologic data. We trained a stacked gradient boosting ensemble that achieved an AUROC of ~0.65 for predicting 90-day all-cause readmission—outperforming logistic regression and single-model baselines.

Systematic feature analysis identified dynamic vital signs and bedside physiologic measurements as key predictors, supporting the development of a concise, clinically actionable risk score to identify high-risk patients and guide targeted post-discharge interventions.