CLINIC PROGRAM
Industry Solutions
1
3
More than 250 students tackled 55 projects during the 56th year of the Clinic Program. Thanks to companies like Lawrence Livermore National Laboratory and Sandia National Laboratories, who received Milestone Awards for the 30 projects each have sponsored, students had a diverse range of problems to solve. Here we share the details of several of the projects, many of them interdisciplinary, presented during Projects Day, May 7. Find more project descriptions at hmc.edu/clinic
22
2
1.
2.
3.
CO MPUTER SCIE N CE C L I N I C
C O MPUT E R S C I E N C E / PH YS I C S C L IN I C
EN GIN E E R IN G/ M AT H E M AT IC S C L IN IC
RACECAR for Education
Machine Learning and Quantum Dots
Stretch Wrapper
MIT Lincoln Laboratory liaison: Andrew Fishberg ’16 Advisor: Zachary Dodds Students: Parth Desai PZ ’19, Chloe Elliott SCR ’19, Alasdair Johnson PZ ’19, Anthony Seto ’19, Reagan Smith ’19
HRL Laboratories LLC liaisons: Seán Meenehan ’08, Emily Pritchett Advisor: Peter Saeta Students: Corbin Bethurem CMC ’19, Evan Hubinger ’19, John Jeang ’19, Vivian Phun ’19
Niagara Bottling LLC liaison: Parker LaMascus Advisor: Timothy Tsai Students: Stephanie Blankley ’20, Bohan Gao ’19, Tai Le POM ’19, Adrian Sanchez Arias ’19, Dana ShangGuan ’20, Elijah Whitsett ’19
In collaboration with Beaver Works outreach program, MIT Lincoln Laboratory developed an autonomous RC car with powerful sensors and processing. Using two RACECAR robots, the Clinic team researched and developed a curriculum in which students implement a variety of robot navigation algorithms. HMC first-year students used these cars to test and refine the materials, resulting in a curriculum that will make the RACECAR platform more accessible. MIT hired the students to continue work on the curriculum during the summer.
In order to use electrostatically defined quantum dots to build qubits for quantum computers, HRL Laboratories seeks a three-dot system with a configuration of one electron per dot. Since this process of tuning up the dots manually is laborintensive, the team sought to automate the process via a machine learning technique, namely deep reinforcement learning.
The team developed an algorithm that autonomously optimizes stretch wrapper material usage under quality constraints. The algorithm, compatible with Niagara’s main data and control hub, does this by safely experimenting with different machine parameters and finds recipes that either meet or surpass the goal of 20 percent material usage reduction.
HARVEY MUDD COLLEGE
