STEM Program

Transforming Healthcare with Data Science and Machine Learning

Faculty Advisor: Adjunct Associate Professor of Computer Science, UCLA

Anticipated Start Time: TBD (meetings will take place for around one hour per week)

Research Program Introduction

The advent of smart infrastructures, wearable devices, and the Internet of Things (IoT) has enabled platforms through which large volumes of data can be easily captured and transferred over networks with no or minimum interaction from humans. Every individual nowadays can produce data in ways that were hard to imagine only a decade ago. Harnessing this data can help scientists and engineers achieve astonishing breakthroughs—from building autonomous cars to developing personalized healthcare. 

However, capturing and collating data alone cannot be helpful unless we learn how to understand, interpret, and extract patterns, often implicit, from it. As data scientists and engineers, we need to tweak data to be amenable to various techniques and sciences, such as artificial intelligence and machine learning. This program will teach the essential ideas behind data science, machine learning, and statistics. We will also work on a few essential methods and techniques in most data-driven projects.

Students will also learn general and subject-specific research and academic writing methods used in universities and scholarly publications. Upon completing the program, they will generate their own work products.

Project Topics

  • Analyzing healthcare data 

  • Predicting medical conditions based on patients’ electronic health records

  • Activity tracking using wearable devices such as smartwatches or smartphones 

  • How to use activity tracking to detect frailty 

  • Machine Learning Techniques

  • Prediction, classification, supervised, and unsupervised learning

Program Details

  • Cohort size: 3 to 5 students

  • Duration: 12 weeks

  • Workload: Around 4 hours per week (including class and homework time)

  • Target students: 9-12th graders interested in Data Science, the Internet of Things (IoT), Machine Learning, Artificial Intelligence, Statistics, or Health Care topics.