Available Projects 

​​

​

Faculty: David Leibrandt

Atomic, Molecular, and Optical Physics

 

The Leibrandt group performs AMO physics experiments using the toolboxes of trapped-ion quantum information processing and precision measurement to explore fundamental physics.  Undergraduate students working in the group participate in the experiments at all levels, from building electronics and optical systems to controlling and measuring the quantum states of individual atoms and molecules.  For more information, see our website at https://leibrandtgroup.physics.ucla.edu  

​

​

​

Faculty: Jason Petta

Condensed Matter/Quantum Information Science

 

Project 1 - Machine learning protocols for the efficient tune-up and operation of semiconductor quantum dots.  Work closely with an experimental team to develop automated calibration and quantum control routines that enable scaling to larger quantum system sizes.

 

Project 2 - Three dimensional quantum dot arrays.  Use device modeling packages incorporating Poisson-Schrodinger equation solvers to simulate new quantum dot device designs that allow the long range transport of spins and extensions to three dimensional quantum dot arrays.
 

​​

​

Faculty: Zhongbo Kang

Nuclear

 

Project 1: Probing Saturated Gluon Matter with Machine Learning
At very high energies, protons and nuclei are dominated by dense gluon fields that may enter a new state of matter known as the Color Glass Condensate (CGC). Understanding this saturated gluon regime is one of the central goals of the future Electron–Ion Collider (EIC), a next-generation facility that will explore the internal structure of matter with unprecedented precision. In this project, students will learn the basic ideas of high-energy Quantum Chromodynamics (QCD) and investigate how modern machine learning techniques — including transformer-based models — can be used to identify and characterize signatures of gluon saturation. The project combines fundamental physics with cutting-edge computational tools and offers hands-on experience at the interface of theory, data, and AI.

​

Project 2: Quantum Machine Learning for Jet Classification
High-energy particle collisions produce collimated sprays of particles known as jets, which reflect the properties of the underlying quarks and gluons. Classifying jets — for example, distinguishing quark-initiated from gluon-initiated jets — is an important problem in collider physics and relies heavily on machine learning techniques. In this project, students will perform an exploratory study comparing quantum machine learning approaches with established classical machine learning methods for jet classification. The goal is to investigate how different algorithmic frameworks perform on realistic jet datasets and to understand the strengths and limitations of emerging quantum-inspired techniques. Students will gain hands-on experience with particle physics, data analysis, and modern machine learning tools, while contributing to an interdisciplinary research direction at the interface of quantum information and high-energy physics.

 

​

​

Faculty: Tuan Do

Astrophysics

 

Our lab seeks to use machine learning methods to enable discoveries in astronomical data. The scale and complexity of astronomical data are growing exponentially, so it is important that our tools and methods grow as well to enable new discoveries. Our group studies both how machine learning is being used in astronomy and applies machine learning methods to challenging astronomical problems such as the nature of dark matter and dark energy. Potential research projects include machine learning in extragalactic astronomy, cosmology, and the study of stars around the supermassive black hole at the center of our galaxy.

​

​

​

Faculty: Derek Schaeffer

Experimental Plasma

​

Project 1: Application of Machine Learning to High-Repetition-Rate Plasma Experiments: This project would focus on the application of machine learning to the analysis of large datasets from high-repetition-rate experiments on the Large Plasma Device at UCLA. By utilizing physics-informed neural networks trained on the data, we can potentially extract additional information not directly measured in the experiments. The student would have the opportunity to develop ML algorithms and analyze data from these experiments using python. Depending on progress, the student may have an opportunity to help design and participate in follow-on experiments.

 

Project 2: Analyzing Data from Collisionless Shock Experiments on Large Laser Facilities: This project would focus on the analysis of data from laboratory astrophysics experiments on large laser facilities. The experiments studied the physics of collisionless shocks, a process that is found in many astrophysical systems from the Earth’s magnetosphere to supernova remnants. Key to understanding the resulting dynamics is measuring the plasma properties (density, temperature, flow) using advanced light-based diagnostics like Thomson scattering and refractive imaging. The student would have the opportunity to analyze data from these experiments using python to study how plasma properties evolve over space and time.

 

Project 3: Analyzing Data from Mini-Magnetosphere Experiments on the Large Plasma Device. This project would focus on the analysis of data from mini-magnetosphere experiments on the Large Plasma Device at UCLA. The experiments studied the physics of magnetic reconnection, in which oppositely directed magnetic fields lines merge and annihilate to convert magnetic energy to heat and kinetic energy. The student would have the opportunity to analyze data from these experiments using python. Depending on progress, the student may have an opportunity to help design and participate in follow-on experiments.
 

​

​

Faculty: Rene Ong

High Energy Particle/Astrophysics

​

Project 1 relates to the GAPS balloon experiment.  GAPS is a balloon-borne particle physics experiment to search for novel sources of antimatter.  GAPS had a successful 25-day flight in Antarctica in Dec 2025-Jan 2026.  Project will involve testing critical hardware that was returned, particularly electronic boards associated with the power and trigger systems.  Student will also study calibration data and carry out analysis of flight data, including energy depositions, trigger patterns, and event reconstruction.  Interest in hands-on experimental work and analysis work using C++/ROOT would be helpful.

​

Project 2 relates to the VERITAS gamma-ray observatory.  VERITAS is an operating telescope that detects very high-energy (VHE, E>100 GeV) photons from powerful astrophysical accelerators.  Project involves the analysis of VERITAS data for various sources in the Galactic plane, possibly including a binary system or the Galactic center region.   Interest in analysis work using C++/ROOT would be helpful.

 

 

 

Faculty: Yaroslav Tserkovnyak

Condensed Matter Theory

 

In this project we investigate the propagation and decay of spin waves in nanoscale antiferromagnetic systems controlled by applied electrical currents. Antiferromagnets are a class of magnetic materials in which neighboring atoms have magnetic moments aligned in opposite directions, resulting in no net magnetization.  These materials are especially important for ultrafast information processing and data storage because their intrinsic spin dynamics occur at terahertz (THz) frequencies. This enables the possibility of extremely fast, energy-efficient devices that can operate at high speeds while remaining compact, stable, and robust, making antiferromagnets a promising platform for next-generation spintronic and magnonic technologies. Spin waves are collective oscillations of magnetic moments that carry angular momentum through a magnet and form the basis of magnonic information transport. In addition to exploring the underlying physics, students will be trained in advanced computational methods and data-processing techniques. This includes hands-on experience with large-scale numerical simulations and practical use of UCLA’s High-Performance Computing (HPC) resources, such as the Hoffman2 Cluster.

 

Prerequisite:  Statistical physics and quantum mechanics, introductory programming skills preferably with Python or Matlab
 

​

​

Faculty: Alvine Kamaha

Astroparticle

​

TBD
 

​

​

Faculty: James Rosenzweig

Experimental Accelerator and Plasma Physics

​

TBD

​

​

​

Faculty: Smadar Naoz

Astrophysics

​

TBD

​

​

​

Faculty: Qianhui Shi

Condensed Matter Experiment

​

TBD

​

​

​

Faculty: Stuart Brown

Condensed Matter Experiment

​

TBD

​

​

​

Faculty: Andrea Ghez

Astrophysics

​

TBD

​

​

​

Faculty: Michael Rich

Astrophysics

​

TBD