Shannon Feeley
Class of 2017
Mathematical Sciences
Mentor: Suzanne Weekes, Professor of Mathematical Sciences
Research Project: Search and Rescue Planning: When a search and rescue incident occurs, it is imperative to find survivors as quickly as possible. The uncertainty in the survivors' location usually increases with time, and their likelihood of survival decreases with time. This project will research the methods that are used to identify the most efficient way to maximize the likelihood of locating survivors.
Katie Gandomi
Class of 2017
Robotics Engineering
Mentor: Carolina Ruiz, Associate Professor of Computer Science
Research Project: Autonomous Delivery with Unmanned Aerial Vehicles: As e-commerce companies like Amazon and Ebay grow, there is a demand to have products delivered from factories into the hands of customers faster than ever. With the help of autonomous quadrotor transport, packages could be at your doorstep within hours as small drones are deployed and organized into a complex network of delivery-robots.
In this research project, the mechanical, electrical and software aspects of this problem are explored as well as the artificial intelligence and machine learning behind the master control unit that organizes and deploys the robots.
Amanda Leahy
Class of 2018
Physics
Mentor: Lyubov Titova, Assistant Professor of Physics
Research Project: Use of Gafchromic Film for Brachytherapy Source Characterization: This project will investigate the use of Yb-169 in High Dose Rate brachytherapy using Gafchromic film. The Gafchromic film will be used to measure the radiation output of Yb-169. The results will be compared to Ir-192, currently the most common isotope used in brachytherapy.
Holly Nguyen
Class of 2018
Computer Science
Mentor: Carolina Ruiz, Associate Professor of Computer Science
Research Project: Personalized Computational Tools to Foster Better Sleep Habits in College: This research project involves the design, implementation and use of algorithms and computational tools in a mobile app to improve sleep behavior in college students. The app enables users to track their sleep schedule (as well as caffeine intake and exercise), receive graphical feedback and tailored advice based on personality and chronotype, and adopt healthier sleep behaviors.
The project covers a wide range of computational aspects (including the design and implementation of mobile apps, data mining and predictive analytics), as well as medical and psychology aspects (including healthy behaviors, personality types, behavioral change, feedback and interventions).