Upsilon Lab works with two types of projects: sponsored projects which are supervised by professors, and in-house projects which are entirely internal.
All of our projects are categorized by branch. The sponsorship status is listed in the subheader of each project. You can visit the pages for those categories, learn more about the projects and find the team managers on these pages:
Tools & Experimental Research (TER) Projects
We are currently looking for enthusiastic managers to lead new TER projects! Please apply!
Laser Box Software
Manager: Keqin Yan – In-House
Learning Objectives: Programming (Python), CAD
APPLICATIONS: OPEN
A great number of experimental Physics research involves understanding of optics and their arrangements. This project is designed to target a very specific problem: how to put optics into a box nicely? Specifically, we plan to use a combination of programming and CAD to build a software tool that facilitates the process of implementing and aligning optics in a box or on a baseplate, which could potentially be very helpful in the fields such as quantum computing (imagining having a quantum computer that looks like a traditional one).
Theory & Simulation (TS) Projects
MODELING QUANTUM SYSTEMS WITH MACHINE LEARNING
Manager: Joshua Wong – In-House
Learning Objectives: Programming (Python), Neural Networks
APPLICATIONS: OPEN
Electrons are all over the place, and they’re complicated! If only we could somehow model their wavefunctions… but with so many in a given molecule, that’s practically impossible! I wonder, is there an easier way to model a complicated wavefunction…? Yes! We can use machine learning to do this!
Learning Physics with Neural Networks
Manager: William Zhu – In-house
Contact: williamzhu@g.ucla.edu
Learning Objectives: Programming (Python, Keras, and Tensorflow), Neural Networks, Deep Learning
APPLICATIONS: OPEN
The goal of this project is to train a deep learning model to predict future states of a closed system based on a known sequence of past states, in a physics-consistent fashion. We will build and run simple physics simulations, then assemble obtained data into an appropriate dataset, on which we will train our network.
