Where/When WUSTL Kelleher Lab. Summer 2017.
GoalTo individualize learning experiences through modifying difficulty of code puzzles using machine learning algorithm
MotivationTechnology is advancing quickly and we need more computer scientists in the workforce. To do so, early teaching of coding skills is important, but around the greater St. Louis area the number of computer science educators is dropping quickly because of so much opportunities with a CS degree. To provide more accessible coding education, the Looking Glass team made a program that teaches fundamental coding concepts, yet still cannot emulate human educators. What make human educators so special is their ability to provide individualized learning experiences, such as providing easier problem sets if a student is struggling.
Where/When ArchHacks, hackathon held at WUSTL. October 2017.
Award Runner-up for best overall hack, Biggest world impact, Best domain name
Goal To make diagnose of pelvic rotation and scoliosis more affordable and accessible for people
Motivation Many are suffering from scoliosis and most incidences can be easily cured through stretching, if diagnosed earlier in the stage. However, diagnose of such is expensive and time consuming for most people because currently, the only way to diagnose is through X-rays, CT scans, and MRI. And if not treated at an early stage, it will develop into a severe pelvic rotation that might be even more costly.
Next Steps Reliability research and connect with sponsors
Where/WhenAdvanced Machine Learning. Spring '18
GoalUsing various machine learning models learned in class, our team proposed to find a model that can reliably predict wine’s quality solely from its chemical components. With the best performing algorithm, we can further develop an app that many wine consumers including chefs, restaurant owners, and hotel managers can use to select their choice of wine and evaluate through just taking a picture of a label.
MotivationComing from a country with the highest alcohol consumption rate, our team decided as a whole to experiment and analyze the wine dataset imported from UCI Machine Learning Repository.