GLEAM Logo

GLEAM API Project

Overview
Architecture
Methods
Results
Team

Epidemic Engine Cloud-based API

Bridging Epidemic Simulators (GLEAM) with AI Algorithms

Introduction

intro

Architecture

architecture

API Endpoints

Methods

Results

result one
The figure shows the test MAE losses across different batch sizes of 1, 3, and 5. This shows batch active learning with a greedy approach to point selection. The general goal is to train on more data even if the data is generally of lower quality, to gain performance improvements.
result one
The figure shows a comparison of the log-scaled test MAE between our Leam-US model and the offline performance of a baseline model.

Epidemic Progression

Var 1
-15-5
Var 2
5250
Var 3
1020

Team

Researchers

Alaa Fadhlallah
Alaa Fadhlallah

University of California San Diego

LinkedIn

Anirudh Indraganti
Anirudh Indraganti

University of California San Diego

LinkedIn

Ethan Cao
Ethan Cao

University of California San Diego

LinkedIn

Liam Manatt
Liam Manatt

University of California San Diego

LinkedIn

Manav Jairam
Manav Jairam

University of California San Diego

LinkedIn

Kyla (Dawon) Park
Kyla (Dawon) Park

University of California San Diego

LinkedIn

Mentors

Rose Yu
Rose Yu

University of California San Diego

Yian Ma
Yian Ma

University of California San Diego

Matteo Chinazzi
Matteo Chinazzi

Northeastern University

Allen Wu
Allen Wu

University of California San Diego