2021 Projects

UW Physics REU 2021 Project List


Projects are offered from the following physics subfields:

  • Cosmology and astrophysics

  • Elementary particle physics

  • Nuclear physics and astrophysics

  • Atomic physics

  • Physics education

  • Condensed matter and nanostructure physics

  • Numerical modeling and simulations

  • Biophysics

Additional projects may be added to this list. If you have a special interest not represented in the list below, feel free to contact either Subhadeep Gupta or Gray Rybka for help. They may be able to design new projects that align with your interests.

Experimental Projects

New Radiofrequency Detectors for Particle Physics
Gray Rybka

Advances in ultra low noise microwave electronics have opened the door to a new generation of detectors with extreme sensitivity to very low energy signals. This project will be related to developing detectors that have applications to astroparticle physics: axion dark matter and neutrino mass measurements.

Ultracold Atoms and Quantum Gases
Subhadeep Gupta

Through the orchestrated use of lasers and electronics, neutral atomic gases can be cooled and trapped at nano-Kelvin temperatures in a high-vacuum environment, where their properties are completely dominated by quantum mechanics. Here atoms interfere like laser beams and flow without friction. In our laboratory, we prepare and study such ultracold gases with a focus on understanding their behavior, testing fundamental quantum theories, and for future applications in quantum information science. In one project we work on resonantly interacting ultracold atoms to study superfluids and ultracold molecules. In another, we perform atom interferometry experiments with Bose-Einstein condensates (BEC). Researchers in our group acquire a broad range of experimental skills while exploring frontier topics in low-temperature quantum physics. The REU student can engage in multiple aspects of the experiments - past REU students have made significant contributions by (for instance) designing and building electromagnetic atom trapping coils, diode laser assemblies, performing laser spectroscopies, and data analysis. Please see our website http://faculty.washington.edu/deepg/ for further details.

Optoelectronics of 2D Semiconductor Heterostructures
Xiaodong Xu

Heterostructures of 3D semiconductors are a central component of condensed matter physics and modern solid state technologies (such as diode lasers and high-speed transistors). The recent discovery of monolayer semiconductors offers exciting opportunities to engineer analogous 2D heterostructures for exploring a wide range of new properties and functionalities. In this project, the REU student will be involved in the investigation optoelectronic properties of the 2D heterostructures. The student will learn how to obtain monolayer semiconductors, heterostructure device fabrication and characterization, and be involved in optical measurements (such as polarization and time-resolved photoluminescence, Raman spectroscopy, second harmonic generation etc.).

Search for Dark Matter
Leslie Rosenberg

Our group is operating the Axion Dark Matter eXperiment (ADMX), a detector to search for the axion, a hypothetical particle that may form the dark matter in our galaxy. We recently commissioned a new data channel that looks for axions that have recently fallen into our galactic dark-matter halo. Also, we're in the process of rebuilding the detector for its high-sensitiity scan. We welcome someone with computing and mechanical skills who can join our group and who has an interest in experimental cosmology.

Quantum Computing with Trapped Ions
Boris Blinov

In the trapped ion quantum computing lab at the University of Washington we experimentally investigate the techniques for building a conceptually new type of computational device. A quantum computer will be extremely fast at solving some important computational problems, such as the factoring and the database search. While days of practical quantum computing may be quite far in the future, we are developing the main building blocks of such a device – the quantum bits ("qubits"), the basic logic operations, the qubit readout... The physical implementation of the qubit in our lab is the hyperfine spin of a single, trapped barium ion. A student in this REU project will participate in experiments with laser-cooled, RF-trapped single ions, will help develop techniques for single- and multi-qubit manipulation via microwave-induced hyperfine transitions and ultrafast laser-driven excitations. They will gain valuable hands-on experience with lasers and optics, RF and digital electronics, and ultrahigh vacuum technology.


Simulations of phase transitions in trapped ion crystals

Boris Blinov

Develop numerical simulations of trapped ions in various trap geometries. Simulate various types of Coulomb crystals under different trapping and laser cooling conditions. Perform simulations of crystalline order change in non-equilibrium systems, observe crystal defect formation and dynamics. As a bonus, if the lab is running in parallel, compare simulation results to the real data.

Quantum jumps

Boris Blinov

Develop numerical tools to analyze quantum jumps in single and multiple trapped ions. Work with large experimental data sets to identify the timing of the quantum jumps and look for correlations between the quantum jumps in multi-ion crystals. These would be hints of superradiance (or subradiance) effects.

Advanced two-dimensional devices
David Cobden

In our group we investigate new physics in devices made from combinations of two-dimensional van der Waals materials, including graphene and many others. Phenomena under study include 2D topological phases, 2D superconductivity, 2D ferroelectricity, 2D magnetism and 2D phase transitions. In this project the student will learn to make their own 2D devices with a particular physics goal in mind, and then carry out a range of measurements, which are likely to include low temperature transport in high magnetic fields and suitable kinds of photoexcitation and spectroscopy.

Large single-electron response CCDs to search for dark matter - (in person only)
Alvaro Chavarria

The next step in the DAMIC dark matter search is the development of an array of large area, thick charge-coupled devices (CCDs) capable of detecting extremely small energy depositions that ionize as little as two electron-hole pairs in the silicon target. These devices will provide unprecedented sensitivity to low-mass dark matter particle candidates in the galactic halo that may interact with ordinary atoms in the target. One of the main challenges for DAMIC is the instrumental noise that arises from the leakage current across the CCDs. The goal of this project is to mitigate the leakage current of the first prototype CCDs by optimizing the mechanical, thermal and electrical performance of the CCD module.

Quantum electronics in two dimensions - (in person only)
Matthew Yankowitz

A wide family of van der Waals (vdW) materials can be mechanically isolated down to atomic monolayer thickness. These crystals can further be mixed-and-matched and stacked on top of one another to create heterostructures with designer electronic properties. When two neighboring crystals are rotated, a geometric superlattice moiré pattern emerges which further modifies the overall device properties. The REU student will learn how to fabricate devices consisting of 2D vdW materials. Using electrical transport measurements, the student will be involved in the characterization of devices exhibiting novel tunable electronic phenomena including superconductivity, magnetism, and other effects of electronic correlations.

DNA optomechanics for studying quantum and thermal physics

Arthur Barnard


In their native physiological environment, DNA molecules fluctuate and contort due to entropic forces. The Classical and Quantum Nano-Systems Lab is interested in taking these molecules, suspending them like guitar strings in vacuum and measuring how they vibrate—the goal of this work is to better understand thermalization and decoherence in nanostructured systems. In this REU project, a student will simulate the time dynamics of underdamped nanomechanical strings as well as model how DNA molecules couple with photonic cavities. Simulations will use homebuilt code and focus on understanding how thermalization in DNA is impacted by sequence dependent mechanical structure. Modeling will use commercially sourced photonics software and aid in designing optimized photonic detectors. The student will gain experience in computational physics on a project that will be directly connected with forthcoming experiments.

Pion Energy Regression with Probabilistic Learning
Shih-Chieh Hsu

Separating the energy response of charged and neutral pions as well as calibrating them is a core component of reconstruction in the calorimeter of the ATLAS detector. It is shown that deep learning approaches outperform the classification applied in the baseline local hadronic calibration used in ATLAS and improve the energy resolution for a wide range in particle momenta, especially for low energy pions. This project will explore different probabilistic learning methods based on the Mixture Density Network and Bayesian Network for estimating the distribution of pion energy. Knowing the energy values along with the associated uncertainties could improve Pion reconstruction in ATLAS.

Detecting Dark Photons with Deep Learning
Shih-Chieh Hsu

An intriguing hypothesis of Dark Sector is the Dark Matter constituents could be neutral under Standard Model interactions, but they could interact through a new, still unknown, force under a “hidden” charge. This new hidden symmetry would be mediated by a massive gauge boson, the dark photon, which is expected to couple to the Standard Model via a kinetic mixing. Highly collimated photon-jets can arise from the decay of highly boosted dark photons that can decay to multiple photons collimated enough to be identified in the electromagnetic calorimeter as a single, photonlike energy cluster. In this project, we will use a simplified calorimeter geometry to study photon-jet properties and investigate classification techniques from from the Standard Model backgrounds. We will adopt computer vision techniques that take advantage of lower level detector information and compare performances respect to conventional shower shape-based methods. The DenseNet-based architecture can potentially outperform other methods and accelerate discovery of dark photons at the Large Hadron Collider.


Theory/Numerical Modeling Projects

Light Front Quantum Mechanics
Jerry Miller

In 1947 Dirac introduced a new form of relativistic quantum mechanics in which the variable ct +z acts as a “time” coordinate and ct-z acts as a “space” coordinate. This so-called light front formalism was largely forgotten until the 1970's, when it turned out to be useful in analyzing a variety of high energy experiments. Despite the phenomenological success of this formalism, it has enjoyed only limited use in computing wave functions of particles and atomic nuclei. The present project is devoted to using the light front formalism to solve quantum mechanics problems involving bound and scattering states. A mathematically strong REU student would learn about relativistic quantum mechanics through the process of solving the relevant relativistic equations. This project would involve working on interesting and timely topics and could provide great preparation for graduate school quantum mechanics, field theory or even string theory. A full year of quantum mechanics is a necessary prerequisite.

Quantum Simulation with Interacting Photons
Arka Majumdar

Understanding correlated many-body effects, including high-temperature superconductivity and fractional quantum Hall physics is not only of fundamental scientific interest but also has the potential for transformational societal impact, with applications in faster, more efficient electronics and topological quantum computers. However, such systems are extremely difficult to study theoretically due to the massive computational resources required. An innovative solution is to build an alternative, well-controlled experimental platform to simulate the performance of these correlated electronic systems. Several physical systems, including cold atoms, ion traps, defect centers, and superconducting circuits, have been considered for quantum simulations. In particular, strongly interacting optical photons provide a unique approach to non-equilibrium many-body quantum simulation due to the ease of adding and destroying photons via external driving and photon loss, as well as measuring multi-photon correlations using readily available single photon detectors. Such interactions, however, require the realization and control of nonlinear optics (NLO) at the few photon level, a daunting task in practice. We have recently developed a platform using solution processed quantum dots coupled to an array of cavities. Unfortunately, in this platform, we cannot reach single photon nonlinearity easily. In the REU project, we plan to model this quantum system (using master equation and quantum trajectory method) and identify the steady-state observables, which exhibit quantum mechanical behavior, even in the absence of single photon nonlinearity. The project also aims to understand the use of this nonlinear coupled cavity array to problems such as quantum machine learning.

Learning the shape of protein universe
Armita Nourmohammad

Proteins play a central role in all parts of biology from immune recognition to brain activity. A key challenge is to predict how protein sequence and structure determines function, such as the protein’s binding affinity to ligands or its enzymatic activity. With the growth of molecular data, machine learning has become a powerful tool in protein science. However, these techniques often generate black-box models, which are powerful but hard to interpret. In this project, students work to develop AI-guided approaches to protein structure-function maps, which respect the physical symmetries in the representation of proteins, and hence, result in more interpretable models of protein micro-environments that could reflect the underlying biophysics.

This project will allow students to work with protein sequence and structural data as well as implementing basic statistical methods for machine learning in Python. This project is computationally intensive and students will learn to work with high performance computing (HPC) to analyze and interpret large data. Background in biology is not necessary but students should have a strong background in statistical mechanics and should be familiar with programming languages such as Python, Julia, or others.


Information processing and control of evolving populations
Armita Nourmohammad

Adaptive Darwinian evolution is an act of information processing: populations sense and measure the state of their environment and adapt by changing their configurations accordingly. Changes of the environment result in an irreversible out-of-equilibrium adaptive evolution, with a constant flow of information. In this project students will explore the fundamental limits in evolution and ecology by combining theoretical approaches grounded in statistical physics and information theory with molecular data. In particular, we aim to develop adaptive optimal control approaches for artificial selection to drive evolving populations towards desired phenotypic targets. The efficacy of such control approaches is limited by our ability to predict evolution — a theoretical limit that we will closely study in this project.

To tackle this problem, students will develop stochastic models for the evolutionary process and apply numerical and analytical techniques to study the underlying dynamics. Background in biology is not necessary but students should have a strong background in statistical mechanics and should be familiar with programming languages such as Python, Julia, or others.

Functional organization of the adaptive immune system
Armita Nourmohammad

The adaptive immune system consists of highly diverse B- and T-cell receptors, which can recognize and specifically react to a multitude of diverse pathogens. Over the past decade, there has been a growth of sequence data on immune receptors, along with more limited information on their interactions with pathogens. Statistical inference and AI-guided approaches to interpret these high-throughput immune repertoire sequences has shed light on the generation of immune receptor diversity and how biophysical features of immune receptors are selected for different function. The goal of this project is to construct data-driven approaches to infer sequence determinants of function for B-cell receptors, as they differentiate to perform distinct functions within an individual.

This project will allow students to work with high-throughput immune receptor repertoire data and will familiarize them with statistical modeling and inverse-inference techniques. This project is computationally intensive and students will learn to work with high performance computing (HPC) to analyze and interpret large data. Background in biology is not necessary but students should have a strong background in statistical mechanics and should be familiar with programming languages such as Python, Julia, or others.