Projects

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 ObjectivesProgramming (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

Contactwilliamzhu@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.