The application deadline was March 14th for the 2025 summer program. Check back again in winter 2026 for next year's program!
The Physics & Astronomy Department has created a ten-week Undergraduate Summer Research program, open only to UCLA students in the Physics & Astronomy Department, to be held June 17 - August 22, 205. Please fill out the online application here. The application deadline is Friday, March 14th.
In addition to the online application, you are asked to provide:
Place all these documents including the application form in a folder and compress them in a single zip file and send to shoko@astro.ucla.edu with a subject line “2025 summer program”.
The following is the list of projects for 2025 summer:
Accelerator and Beam Physics
Faculty: Pietro Musumeci
Project: This project will focus on developing compact permanent magnet based electron optics to be used in high brightness electron beamlines. Permanent magnet technology is particularly attractive as it allows to build compact, cost-effective, and strong lenses charged particle beams. One of the drawbacks is the lack of tunability. Students will be involved in 3d magnetostatic solvers and particle tracking simulations to design novel electron lenses. This will inform construction, assembly and magnetic field measurements and if successful, electron beam testing will follow.
Condensed matter physics/quantum information science
Faculty: Jason Petta
Project: Effects of ionizing radiation on qubit relaxation and coherence times.
Experimental Condensed Matter:
Faculty: Stuart Brown
Project: TBD
Faculty: Chris Regan
Project: We are studying ferroelectricity in hafnium zirconium oxide with scanning transmission electron microscopy. Students have the opportunity to participate in many aspects of the project, ranging from sample fabrication to experimental design to data acquisition and analysis to simulation and interpretation.
Faculty: Qianhui Shi
Project: The project is to build multi-layer moire structures from 2D materials to explore emergent quantum states, with a focus on the role of layer population. The student will make the samples and participate in the cryogenic measurements.
Experimental Particle Physics:
Faculty: David Saltzberg
Project: We are analyzing rare data with two Higgs Bosons from the CMS experiment at the Large Hadron Collider at CERN. Measuring this process is one of the major goals of the upcoming datataking at CERN as it measures the underlying symmetry breaking model of Standard Model of particle physics. At UCLA we are using a decay mode for which neural networks/machine learning should be particularly helpful in separating the signal from background (top quark production), one with two W bosons and two b quarks. This student will use our infrastructure with neural networks to try to optimize separating signal from background by inventing new kinematic variables as well as modifying the fundamental neural network parameters, such as how it implements non-linearities. The student we have in mind, has already worked for a year doing hardware in our lab and would greatly benefit from a data analysis project.
Faculty: Jay Hauser
Project: Simulate and visualize interesting particle physics interactions such as proton decay, hyperon production and decay, dark matter interactions, and high-energy neutrino interactions. The research will use software tools such as Pythia, GEANT4, and our own virtual reality visualization package G4VR. The students do not need to have taken quantum mechanics or particle physics classes, but good programming skills are needed.
Nuclear Physics/QCD Collider:
Faculty: Zhongbo Kang
Project: Quantum Chromodynamics (QCD) describes the strong interaction that binds quarks and gluons into protons, neutrons, and other hadrons. While its fundamental laws are elegantly formulated, understanding how they determine hadron structure remains a major challenge in nuclear and particle physics. This challenge drives modern high-energy collider experiments, which generate vast datasets to probe the substructure of matter. In this project, students will learn key QCD concepts and develop computational tools to analyze experimental data, extracting fundamental properties of quarks and gluons.
Quantum Computing/QCD:
Faculty: Zhongbo Kang
Project: Quantum Chromodynamics (QCD), which governs the strong force, has both perturbative and non-perturbative regimes. While perturbative QCD applies at high energies, the non-perturbative regime—relevant for confinement and phase transitions—remains challenging. Quantum computing offers a novel approach to simulating strongly interacting systems. In this project, students will explore quantum simulations of QCD-like models, such as the Schwinger model in 1+1 dimensions, gaining hands-on experience with quantum many-body dynamics and computational tools for high-energy physics.
Plasma Physics and Fusion Physics
Faculty: Alfred Wong
Project: To relate Plasma Physics course 180E to Prof Wong's current innovative fusion research efforts. Will send you two recently published papers in this area.
Astroparticle Physics, Dark Matter Direct Search
Faculty: Alvine Kamaha
Project: Prof. Kamaha's main research work is on an international project, called the LUX-ZEPLIN (LZ) dark matter experiment which has been selected by the Department of Energy as the flagship U.S. dark matter experiment. Prof. Kamaha is currently looking for students who are interested in analyzing real data from the LZ dark matter detector to search for signatures coming from dark matter interactions inside the detector. Selected students will work on detector calibrations and background mitigations and well as working on different detector Geant4 simulations and some hardware projects.
She is also looking for students to work in-situ within the test facility she is developing in her research laboratory in Knudsen Hall. The first in-situ project is to optimize a novel technique for particle detection based on supercooled water. The second one is the development of a low-background counting facility as well as the development of cleanliness techniques for future generations of dark matter detectors. The third one is a R&D project for some calibration techniques in noble liquid dark matter detectors.
Laboratory astrophysics, laser-plasma experiments
Faculty: Derek Schaeffer
Project 1: Application of Machine Learning to High-Repetition-Rate Laser-Plasma Experiments. This project would focus on the application of machine learning and neural networks to the analysis of large datasets from high-repetition-rate laser-plasma experiments. In the experiments, a high-powered laser generates a plasma blast wave, which is diagnosed with a Thomson scattering diagnostic over thousands of laser shots. A ML algorithm is then used to process this large dataset to generate 2D images of plasma parameters. The goal of these proof-of-principle experiments is to demonstrate an ability to process experimental data in real-time in order to guide the operation of the experiment, for example by optimizing laser parameters to achieve desired plasma properties. 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. In these systems, a supersonic plasma expands into a pre-magnetized ambient plasma, which can be reproduced using high-powered lasers. Key to understanding the resulting dynamics is measuring the plasma properties (density, temperature, flow) using advanced light-based diagnostics likeThomson scattering. The student would have the opportunity to analyze data from these experiments (using MATLAB or python) to study how plasma properties evolve over space and time.
Faculty: Anna Ciurlo
Project: Studying stars orbiting Sgr A*, the supermassive black hole at the Milky Way's center, provides critical insights into extreme gravitational environments and stellar dynamics. Extracting their spectra reveals properties such as composition, mass, and age, which inform models of star formation and evolution in the vicinity of black holes. Spectroscopic data, combined with astrometric measurements, also enable precise orbital reconstruction, offering stringent tests of general relativity in the strong-field regime and constraints on Sgr A*'s mass and spin. A key challenge in spectral extraction is the accurate subtraction of foreground and background interstellar medium emission. This project employs an advanced gas emission model to enhance stellar spectral extraction. The student will evaluate the impact of modeled gas subtraction on stellar spectra extraction, to showcase the potential of this technique and expand its application.
Faculty: Tuan Do
Project: Machine learning and AI in Astrophysics - our group 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 : Pradip Gatkine
Project: Maturing astrophotonic spectrograph technology for space-based implementation. Astrophotonic spectrographs offer the most compact way of implementing spectrographs on a chip. This allows us to collapse the 3D bulk-optical spectrgraphs in astronomy to 2D chips, which miniaturizes spectrographs for both ground- and space-based astronomy. Our group is working on maturing this technology for deployment in space. In that direction, we need to do several ground-based tests to test and prove all the building blocks. This will form the foundation of this summer research opportunity. Students with an interest and background in optics, python programming, and hands-on instrumentation are welcome to apply.
Faculty: Andrea Ghez
Project: The selected student will work on the Galactic Center Orbits Initiative, which is a long-term project being carried out at Keck Observatory to measure the orbits of stars to learn about the physics and astrophysics of supermassive black holes. We explore methods for extending the time baseline of the existing observations to improve our ability to perform new tests of General Relativity in the relatively unexplored regime near a supermassive black hole.
Faculty: Mark Morris
Project: work a theory to explain the multiplicity of the nonthermal radio filaments found in parallel bundles in the Galactic Center region.
Faculty: Smadar Naoz
TBD
Faculty: Michael Rich
TBD
Faculty: Jean Turner
Project: Project: The Massive Embedded Star Cluster Atlas: Project description: The Massive Embedded Star Cluster Atlas (MESCAL) survey is searching for the largest observed star clusters in the Universe, super star clusters, in local galaxies. Super star clusters provide the closest present-day analogs to study how star formation occurred in the early universe. The student will learn how to cross-reference all-sky survey catalogs and assist with a multiwavelength expansion of MESCAL, supplementing radio fluxes with mid-infrared, ultraviolet, and x-ray fluxes. The student will assist with the construction of the resulting polychromatic catalog. Previous summer students of the group have also completed independent followup projects related to the catalog leading to first-author publications.
Questions? Contact the Undergraduate office: Mary Tran, Student Affairs Officer, 1-707A PAB, 310-206-1447.
Previous REU programs: