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 15 - August 21, 2026. You will receive a stipend of $6000. Please fill out the online application. The application deadline is Wednesday, March 25th.
Please note that this program is for the UCLA undergraduate students majoring in Physics or Astronomy only. Students graduating in Spring/Summer 2026 are not eligible to apply. Also, this will be a full-time program; you will not be allowed to take summer classes concurrently.
In the online application, you are asked to provide:
In addition, you will need to provide:
The following is the list of projects for 2026 summer:
Theoretical Elementary Particle Physics
Faculty: Per Kraus
Project: Computations in quantum chaotic systems, such as the SYK model, as models for black holes. Using a combination of analytical and numerical methods, relatively simple quantum mechanical models may be used to simulate certain aspects of Hawking radiation from black holes, including into regimes where the semiclassical spacetime description breaks down.
Particle Physics, Astrophysics, General Physics
Faculty: Jay Hauser
Project: The student will create their own virtual reality (VR) tool to explore interesting physics and astrophysics, including gravitational motion (solar systems, globular clusters, motion of stars near the black hole at the galactic center, etc.), the relativistic appearance of star fields, dark matter detection or proton decay experiments, and visualization of high-dimensional data. The students do not need to have taken quantum mechanics or particle physics classes, but programming experience in C++, C#, or Unity is needed.
Experimental Plasma Physics, Laboratory Astrophysics:
Faculty: Derek Schaeffer
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.
Condensed Matter:
Faculty: Yaroslav Tserkovnyak
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
Condensed Matter Physics/ Quantum Information Science:
Faculty: Jason Petta
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.
Experimental Condensed Matter
Faculty: Stuart Brown
Project: Stress-tuning quantum materials
A notable feature of many classes of quantum materials is our ability to change the nature of a system’s ground state by means of non-thermal control. An example is the application of uniaxial or biaxial stress, which can affect the relative importance of correlations or because a symmetry change triggers a topological transition.
The proposed project involves the design, testing and implementation of a mechanical stress device that can be incorporated into cryogenic experiments of quantum materials.
Nuclear and Particle Physics
Faculty: Zhongbo Kang
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.
Astroparticle
Faculty: Alvine Kamaha
Project: TBD
Condensed Matter
Faculty: Qianhui Shi
Project: Two-dimensional materials provide a versatile platform to design, build and investigate many-body systems with merging quantum phenomena. This project will build multilayer WSe2 systems - from monolayers or bilayers - at controllable twist angles to realize superlattices of certain symmetry. The emergent phases will be studied, with a focus on the role played by the valley degree of freedom in the band structure. Students will build the structure, design the device and participate in the low-temperature transport and capacitance measurements. In addition, students may pursue focused mini-projects, such as developing fabrication recipes for making reliable electrical contacts or performing calculations of electronic band structures.
Experimental Condensed Matter
Faculty: Anshul Kogar
Project: TBD
Experimental Plasma Physics
Faculty: James Rosenzweig
Project: TBD
Experimental Atomic-Molecular-Optical Physics
Faculty: David Leibrandt
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
Hard Condensed Matter
Faculty: Christopher Gutiérrez
Project: TBD
Condensed Matter
Faculty: HongWen Jiang
Project: Semiconductor qubits are among the leading platforms for quantum computing. To enable error correction, real‑time, high‑fidelity readout of qubit states is essential. In this project, the REU student will help construct electronics to optimize the fast readout of a semiconductor quantum‑dot device coupled to a superconducting microwave resonator.
Faculty: Chris Regan
Project: In ancient times people stored data by positioning atoms. Think of the abacus, or the ink on the pages of a book. Then we used magnetic dipoles (magnetic tape, hard drives). Now we move electrons (flash memory, solid state drives). However, electrons have proved to be too squirrelly to efficiently corral in very small memory elements. As a result, those AI data centers popping up all over the world are unsustainably power-hungry. To address this problem we must go back to the beginning. In a few years we will again be storing data with the positioning of atoms, likely in a ferroelectric material like hafnium zirconium oxide.
Students on this project will work on developing this exciting, next-generation memory technology. They will design and build data acquisition systems that can detect the minute electrical currents that result when a bit is flipped from "0" to "1" and back. With these DAQ systems they will explore and characterize the interplay between materials properties, electromagnetic fields, and thermodynamics that governs the function and failure of these devices. A successful project will help realize practical ferroelectric memory.
Faculty: Steve Furlanetto
Project: Professor Furlanetto’s research group focuses on the Cosmic Dawn, the era in which the first galaxies formed just a few hundred million years after the Big Bang. Their work uses simple, flexible models of early galaxies to study the essential physics driving galaxy formation and evolution and how these systems influence their large-scale environment - especially through “reionization,” when photons from these first galaxies transformed the intergalactic medium. In this summer project, we will use these models to explore the observable implications of the fundamental physics of galaxies.
Faculty: Andrea Ghez
Project: TBD
Faculty: Smadar Naoz
Project: TBD
Faculty: Rene Ong
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: Michael Rich
Research project 1: Remotely operate 3 telescopes to take data on low surface brightness galaxies and "ghost" or extremely late stage planetary nebulae, in collaboration with Dr. Patrick Ogle at the Space Telescope Science Institute. Research Project 2: Use the Hoffman2 cluster to reduce imaging data of the Galactic bulge taken with the Dark Energy Camera at the Blanco 4m telescope. These data will be used to derive the metallicity distribution of the bulge, via machine learning.
Faculty: Tuan Do
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.
Previous REU programs: