Undergrad USRA Projects 2022

To apply for USRAs, visit the department's Undergraduate Summer Research Awards page.

2022 Summer project descriptions will be updated as projects are submitted. Please see last year's projects for more information about PHAS Faculty projects.

*Students: not all PHAS faculty post research projects on this page. Consider reaching out to faculty directly (listed here or not) if they are engaged in research that interests you.

 

1. Quantum Coherent Control

Contact: V. Milner | Email: vmilner@phas.ubc.ca | Web: http://coherentcontrol.sites.olt.ubc.ca/

Our research group on Quantum Coherent Control uses ultrafast lasers to control and study the behaviour of molecular "super-rotors" and their interaction with quantum media, such as helium nanodroplets or ultracold plasmas. Super-rotors are extremely fast rotating molecules produced in our laboratory (and not available anywhere else!) using a unique laser system known as an "optical centrifuge". Many fascinating properties of molecular super-rotors have been theoretically predicted. A few of them have been already shown by our group in the last five years, but many more await discovery.

In the summer of 2022, the USRA student will help a senior PhD student with an ongoing experiment on the laser centrifugation of molecules captured by the beam of helium nanodroplets. For specific tasks and projects, please contact Dr. Milner at vmilner@phas.ubc.ca.

 

2. Charting the Growth of Galaxies

Contact: Allison Man | Email: aman@phas.ubc.ca | Web: https://phas.ubc.ca/users/allison-man

Galaxies evolve on astronomical timescales of millions or even billions of years. The study of galaxy evolution is therefore based on inferring connections between various galaxy populations across cosmic time. This requires knowledge of galaxy properties, such as distances, sizes, masses, ages, and star formation rates. The student will learn now to extract such information from galaxy images and spectra. Driven by the student's interest, the project will tackle these important scientific questions: What triggers or shuts down star formation in galaxies? How do active supermassive black holes influence star formation of their host galaxies? What happens to galaxies when they collide with each other?

The student will apply their Python computing skills to handle large datasets and images, to visualize and to present findings. These skills are relevant for a variety of projects in astronomy, other research disciplines and beyond academia. Experience with Python programming is required. Knowledge of physics, astronomy, statistics, data analysis, LaTeX and Git will be considered a plus.

 

3. Atomistic Calculations of High Entropy Oxides

Contact: Prof. Joerg Rottler, Prof. Alannah Hallas, Dr. Solveig Aamlid | Email: solveig.aamlid@ubc.ca                                             Web: https://qmi.ubc.ca/research/atomistic-approach-disordered-materials/

High entropy oxides are a novel class of materials that find promising applications in energy conversion and storage, thermoelectric devices, and catalysis. In these crystals, up to five elements share one crystallographic site, which leads to properties that are vastly different from their simpler building blocks. However, predicting the phase stability and crystal structure of such materials is challenging. One approach to solve this problem is to use density functional theory (DFT) to calculate pairwise interaction parameters and further use those parameters in Monte Carlo simulations.

In this USRA project based at the Quantum Matter Institute, the student will help identify relevant elements and crystal structures for high entropy materials, run and optimize calculations of the chosen combinations, and evaluate the results to guide the synthesis of new materials. The student will receive training in computational methods that are commonly used in solid state physics. Good knowledge of computational methods, ideally some experience with Python/Matlab/Unix OS would be very helpful.

 

4. Lithium High Entropy Oxides for Battery Applications

Contact: Prof. Alannah Hallas, Dr. Mohamed Oudah | Email: mohamed.oudah@ubc.ca                                                                        Web: https://qmi.ubc.ca/research/atomistic-approach-disordered-materials/

High entropy oxides (HEOs) are a novel class of materials where atomic disorder leads to promising properties for applications including energy conversion and storage. The class of Li-based HEO's, where Li-ions can move in a disordered oxide lattice containing 5 or more transition metals, have potential for battery applications. Preliminary studies have shown that the Li-ion conductivity in these materials is directly related to the level of disorder and the Li content. Studying the effects of varying the ratios of transition metals and Li content on ionic conductivity can set these materials up for battery applications in the future.

In this USRA project based at the Quantum Matter Institute, the student will help synthesize Li-HEO's, characterize their crystal structure, and study the electrical/ionic conductivity of synthesized samples. The student will receive training in solid-state synthesis techniques, as well as commonly used techniques in solid state physics, such as X-ray diffraction, scanning electron microscopy, and conductivity measurements. Prior knowledge of synthesis techniques and diffraction or completed course work in solid state physics is an asset.

 

5. Magic Angle in Twisted van der Waals Heterostructures

Contact: Ziliang Ye | Email: zlye@phas.ubc.ca                                                                                                                                                  Web: https://qmi.ubc.ca/research/engineering-exotic-phases-in-2d-materials/

Starting from the discovery of graphene, highly anisotropic bonds in van der Waals crystal allows us to prepare a variety of mechanically stable monolayers with only one or a few atoms in thickness. In many cases, these two-dimensional materials exhibit exotic physical properties distinct from bulk counterparts, including but not limited to relativistic particles like electrons following Dirac equation, gate-tunable Ising superconductivity, and exitons with valley degree of freedom, which enables new potentials in novel electronic and optoelectronic device application. More excitingly, these monolayers are robust to be manipulated and stacked together precisely, forming so-called van der Waals heterostructures, which opens a door to almost unlimited opportunities for new physics discovery. Recently, it has been found that unconventional superconductivity can emerge when two layers of graphene are stacked together with a small twist of 'magic' angle. It can be envisioned such a phenomenon should not be unique to the bilayer graphene.

This summer, we welcome undergraduates to join our effort to explore this direction in low-dimensional quantum materials. Students will be exposed to a range of state-of-the-art experimental techniques from 2D materials preparation to van der Waals heterostructure fabrication to nearfield optical characterization.

 

6. Looking for New Physics with ATLAS Precision Measurements

Contact: Alison Lister | Email: alister@phas.ubc.ca | Web: https://atlas.cern/

Q: How do we learn something about new physics beyond the Standard Model (BSM) without measuring it directly? A: We look for its impact on things we can measure! The UBC ATLAS group is working to constrain new physics using precision measurements of Standard Model particles. Different hypothetical BSM particles can cause subtle changes to what we see in the detector. By putting together these measurements we can look for any anomalies that could hint at new physics.

The student will work on translating individual measurements into a combined framework, and optimizing variables to maximize sensitivity. See more information about ATLAS and new physics here.

 

7. Constructing Silicon Inner Tracker (ITk) for ATLAS Detector Upgrade

Contact: Alison Lister | Email: alister@phas.ubc.ca | Web: https://atlas.cern/

The UBC ATLAS group is among several institutions around the world participating in the construction of the new, all silicon, Inner Tracker (ITk) for the upgrade of the ATLAS detector for the High Luminosity Large Hadron Collider (HL-LHC) at CERN. Each module of the silicon strip tracker must undergo a series of thermal and electrical quality control measurements before they are installed in the ATLAS detector. Here at UBC, we are performing these critical tests in our newly commissioned cleanroom.

The student will work on building and improving the test setup, optimizing and automating the testing procedure, and analyzing and presenting the results from electrical tests to the wider ATLAS community. See more information about the ATLAS silicon Inner Tracker here.

 

8. Deep Learning with ATLAS

Contact: Alison Lister | Email: alister@phas.ubc.ca

The ATLAS UBC Group is developing new deep learning techniques for both signal vs. background classification problems as well as inference problems (given what we see in our detector, what are the most likely properties of the particles that produce that signature). The students will work on further improvements of the method as well as develop techniques for mitigation of the impact of the systematic uncertainties on the deep learning model.

Experience and familiarity with Python is required.

 

9. Multi-Task Deep Learning for Segmentation and Outcome Prediction in PET/CT Images of Cancer Patients

Contact: Arman Rahmim | Email: arman.rahmim@ubc.ca | Web: http://qurit.ca

Outline of Research Project:

We aim, in our multidisciplinary lab, to perform advanced image analysis on PET and PET/CT images to improve and simplify diagnosis, cancer staging, therapy response assessment and prediction of outcome for cancer patients, in close collaboration with nuclear medicine physicians. Thorough analyses on medical images, leading to the field of radiomics, often rely on accurate segmentations of tumors; meanwhile, a fully automated segmentation step is a bottleneck for applying radiomics studies on large datasets.

Deep learning especially via Convolutional Neural Networks (CNNs) has enabled improved performance in different medical imaging tasks. In particular, improved outcome prediction for cancer patients can enable better personalization of therapies, though it has been less frequently considered via CNNs since they were not as efficient compared to alternative non-deep-learning frameworks. The deep features learned by (convolutional) layers of CNNs for tumor segmentation (i.e., U-net) have the potential to guide the outcome prediction network by exploiting the scarce and precious radiomics features. We aim to evaluate this potential by suggesting and evaluating the models for simultaneous tumor segmentation and outcome prediction from PET/CT images of patients with different cancers.

Our proposed plan at the Quantitative Radiomolecular Imaging & Therapy (Qurit) lab (Qurit.ca) is to develop multi-task learning approaches for segmentation and outcome prediction in parallel. This innovative idea has only once been applied to PET/CT radiomics and we aim to propose different multi-task frameworks. Studies have shown that multi-task learning, when the tasks are related, could improve the efficiency of the individual tasks. In other words, the deep features that are learned for segmenting tumor regions and the relevant features from tumor regions in outcome prediction can co-facilitate learning. We aim to develop and validate such deep learning frameworks for the analysis of PET/CT images of cancer patients.

The Student's Role:

After joining Qurit lab, the student will gain familiarity with AI-based techniques including deep learning approaches especially for segmentation and outcome prediction by studying reading material and test data provided by Qurit lab, and with the help of the post-doctoral researchers and PhD students in the lab. In the next step, sub-projects that the student is supposed to work on (in collaboration with other lab members) will be refined adaptively based on student capabilities and interests. The main part of the student's project is to provide help to develop and improve the deep models for simultaneous segmentation and outcome prediction. The student will contribute to one or more clusters (research sub-groups within our lab for lymphoid, cervical, prostate and/or lung cancers) to evaluate the capability of the developed multi-task models in different cancers. The student will experience an exciting research program and may have the opportunity to participate in AI competitions and/or conference/journal works by team members.

The trainee will be (i) mentored on a daily/weekly basis, (ii) immersed in a multi-disciplinary environment, (iii) provided with in-depth understanding of recent developments in imaging & AI, and (iv) the opportunity to interact with our clinical and technical collaborators. The trainee will present and discuss research ideas to the team and respond to questions in a safe yet intellectually inspiring environment. The program will also emphasize the trainee's communication skills, a strong area of mentorship in our lab.

 

10. Analyzing Molecular Dynamics with Graph Neural Networks

Contact: Prof. Joerg Rottler (Physics), Prof. Chad Sinclair (Materials Engineering) | Email: jrottler@physics.ubc.ca

This projects explores the application of a novel machine learning technique based on graph neural networks for the analysis of atomic trajectories obtained from molecular dynamics simulations. The network is trained to construct a so-called Markov state model, a low dimensional representation of the dynamics that captures the slowest processes in the material and provides insight into the structural features that govern them. Our groups have previously developed applications for crystals with interfaces, grain boundaries, and simple models of disordered solids (glasses). The goal of this project is to further extend this method by considering nanostructured materials, network glasses, or amorphous metals under deformation.

Good computational skills and experience with programming in Python is required.

 

11. Statistics of CMB Polarization

Contact: Dr. Douglas Scott | Email: dscott@phas.ubc.ca | Web: https://www.astro.ubc.ca/people/scott/basic.html

The cosmic microwave background allows us to probe the Universe on the largest length scales possible. There are several hints or "anomalies" that may suggest modifications to physics on large scales or at very early times in the history of the Cosmos. In order to assess if such anomalies are real or just mild statistical excursions in the data, it is necessary to find new ways to probe the large-scale Universe. One such new probe is provided by sensitive measurements of CMB polarization, which comes from new modes in the early Universe. The latest maps of large-angle polarization have been provided by the Planck satellite. In this project we will study aspects of sky polarization, and investigate statistical techniques that can be used to distinguish the cosmological signals and to test for deviations from statistical anisotropy. Additionally, it will be useful to assess the power of future (more sensitive) polarization measurement using simulations.

 

12. Deep Learning in Astronomy

Contact: Dr. Douglas Scott | Email: dscott@phas.ubc.ca | Web: https://www.astro.ubc.ca/people/scott/basic.html

There are many data analysis problems in astronomy that are best approached using simple likelihood function methods. However, there are other questions (involving non-linear selection tasks, or pattern-matching in huge databases) that are more efficiently performed with "machine-learning" (ML) methods, such as neural networks. One downside to the use of ML approaches is that it is often difficult to determine robust uncertainties on derived parameters. Another unresolved issue is how to combine traditional and ML methods in tasks that use both approaches for different parts. We will investigate these topics by looking at the use of ML in astronomy, combining data at multiple wavelengths to identify and categorize distant galaxies and assess their statistical properties.

 

13. Gravitational Wave Astronomy with LISA

Contacts: Jess McIver & Scott Oser | Email: mciver@phas.ubc.ca and oser@phas.ubc.ca

LISA is a proposed spaced-based gravitational wave detector that will consist of three spacecraft flying in an equilateral triangular configuration with a side length of 2.5 million kilometers. Each spacecraft shines a laser at the other two, and all three measure the interference between incoming and outgoing beams of light. These interferometric measurements will allow LISA to measure the minuscule modulation of the separation distances between spacecraft caused by gravitational waves produced by distant sources such as massive black holes, binary star systems, or even the aftermath of the Big Bang itself. We will examine analysis techniques for how LISA can extract the gravitational wave signal from noise contributions, with a focus on signal and noise modeling. Familiarity with Python and/or C++ is strongly preferred.

 

14. Improving the Performance of the Advanced LIGO Gravitational Wave Detectors

Contacts: Jess McIver & Raymond Ng | Email: mciver@phas.ubc.ca and rng@cs.ubc.ca                                                                    Web: https://gravitational-waves/phas.ubc.ca

Gravitational-wave detector data, including the LIGO detectors, contains a high rate of instrumental artifacts that mask or mimic true astrophysical gravitational wave (GW) signals. This project will characterize noise sources in the Advanced LIGO detectors with the goal of reducing the number of 'false alarm' GW candidates and improving the reach of GW searches for the next observing run. Students will work with a team of physicists and data scientists, and gain transferable skills in data visualization, Python programming, gravitational-wave astrophysics, and large-scale physics experimental instrumentation. Familiarity with Python is preferred.

Learn more about the UBC LIGO Group here and the LIGO Scientific Collaboration here.

 

15. CHIME telescope: options for Pulsar and/or FRB projects

Contact: Ingrid Stairs | Email: stairs@astro.ubc.ca

This summer project will entail Pulsar and/or FRB work with the CHIME telescope: in particular, looking at slow pulsars to measure diffractive scintillation properties as a function of frequency; CHIME's wide fractional bandwidth could allow new probes of the predicted scaling of this phenomenon. Also, this project can include an investigation of wideband profile changes in these pulsars and comparison to narrowband literature measurements and conclusions. FRB projects could involve, for example, interference-excision improvements and testing.

For more information about the CHIME telescope, see here.

 

16. Orbital Infrastructure Resilience after Anti-Satellite Weapon Tests

Contact: Aaron Boley | Email: acboley@phas.ubc.ca | Web: https://www.aaronboley.com/

Debris-generating anti-satellite weapon tests create impulsive injections of trackable and lethal, non-trackable debris into the orbital environment. This debris endangers orbital infrastructure and crewed space exploration through increased risks of high speed impacts. Subsequent on-orbit collisions could lead to further fragmentation events with the potential to cause dramatic changes to the debris environment in time. The industrialization of Earth's orbit through the construction of large satellite constellations (so-called "megaconstellations") is expected to greatly increase the risk of chained fragmentation events following sudden injections of debris.

This project will use the rate equation approximation to explore the evolution and health of orbital infrastructure following a large debris-generating event, envisaged to take place in a megaconstellation environment. The debris generating event will be modelled after recent direct-ascent anti-satellite weapons tests, such as the Indian 2019 and Russian 2021 tests.

 

17. Observations of Satellites in Astronomical Bands

Contact: Aaron Boley | Email: acboley@phas.ubc.ca | Web: https://www.aaronboley.com/

Large constellations of satellites are poised to cause major interference with astronomy. Astronomers are engaged in discussions with governments and industry to identify mitigations for existing interference and to establish paths toward minimizing interference by future satellite constellations. As part of this effort, observations of so-called megaconstellation satellites are needed in astronomical observing bands to establish the brightness distribution of satellites and their variability, as well as to assess the effectiveness of mitigations. Such observations are further needed to emphasize the different ways that satellites will impact astronomy.

This project will observe megaconstellation satellites in different observing bands and assess their brightness range. The project will further compare the results with satellite brightness modelling and assess intrinsic variability.

 

18. Magnetic Resonance with Non-Aqueous and Water Protons in the Brain

Contacts: Alex Mackay & Carl Michal | Email: mackay@phas.ubc.ca & michal@phas.ubc.ca                                                               Web: https://phas.ubc.ca/users/alex-mackay & https://phas.ubc.ca/~michal/

Magnetic resonance imaging (MRI) is heavily used in medicine because it produces images with high contrast between different soft tissue types and between healthy and pathological tissue.

The physical mechanisms which determine this exquisite tissue contrast are still not clearly understood. MR images from the brain are generated from signals coming from hydrogen nuclei in water in the brain; however, the signal from the water protons are influenced by interactions between water and non-aqueous protons attached to lipids and proteins. The proposed project will involve in vitro and ex vivo NMR experiments designed to enable us to better understand the interactions between non-aqueous protons and water protons in the brain. Having a clearer understanding of these interactions may enable us to extract more specific and quantitative information about brain microstructure using MRI.

 

19. Single-Molecule Microscopy of DNA-Enzyme Reactions in Confinement

Contact: Sabrina Leslie | Email: leslielab@msl.ubc.ca | Web: https://leslielab.msl.ubc.ca/

In this research project, the student will use single-molecule Convex Lens-induced Confinement (CLiC) fluorescence microscopy to visualize interactions between DNA and enzyme molecules in confinement. The student will learn and extend image analysis code to quantify interaction kinetics such as binding and unbinding rates from the microscopy data. This research will shed new insight into DNA and enzyme interactions as a function of biophysical variables such as structure, sequence, temperature, and solution conditions.

The student will receive training in single-molecule CLiC fluorescence microscopy and quantitative image analysis and will work closely with a research fellow. Weekly meetings with the supervisor and collaborators, and daily interactions with members of our interdisciplinary research group will support and guide the project. In addition to gaining hands-on experience, anticipated outcomes of this summer research project include virtual presentations with lab members, providing key training in writing and oral communication. The student will also participate in summer research symposia gaining perspective in the fields of biophysics, biotechnology and genetic medicine.

There are two student positions available for this project.

 

20. High Throughput Testing of Coating Mechanical Loss for Advanced LIGO

Contact: Jeff Young | Email: young@phas.ubc.ca | Web: https://qmi.ubc.ca/team-member/jeff-young/

Researchers at UBC's Steward Blusson Quantum Matter Institute (SBQMI) are investigating low-mechanical loss (i.e., low noise), high-refractive index thin-film coatings for use in future generations of gravitational wave (GW) detectors, such as LIGO. These coatings are required to increase the sensitivity of GW detectors, allowing observation of more GW events per run, and of GW events from further away.

In our group, we are using micrometer scale devices to test candidate low-loss thin-film coatings. Specifically, we deposit the thin film of interest onto microresonators fabricated from silicon-on-insulator wafers. With this approach, we can fabricate hundreds of microdisks per chip and coat them with this films whose composition varies across the chip, enabling high throughput testing of many different thin film candidate materials. To measure the mechanical loss, a ringdown technique is utilized where the microdisks are tested in a vacuum to avoid excess air damping. Future studies will also focus on characterizing these films in a cryogenic environment.

Topics that a student could contribute to include: (1) investigating alternative resonator geometries to access lower mechanical frequencies; (2) contributing to the design of a cryogenic vacuum chamber by developing a parts list and creating CAD layouts; (3) investigating optical measurement strategies compatible with our microresonator devices.

 

21. Optical Cavity Stabilization on the nm Scale Using DSP Audio Processors

Contact: David Jones | Email: djjones@phas.ubc.ca                                                                                                                                    Web: https://qmi.ubc.ca/team-member/david-jones/?team-category=investigators

The UBC Ultrafast Spectroscopy Laboratory uses a femtosecond enhancement cavity to generate photons for time and angle resolved photoemission spectroscopy. Enhancement cavities are passive resonators that achieve very high optical power circulating in the resonator using a much weaker laser source as its input. This enhancement is accomplished through the constructive interference of many low power pulses from the incident laser. To achieve a significant enhancement of these laser pulses, the length of the optical cavity must be stabilized to the nanometer scale. A fast servo control loop is employed to guard from disturbances such as acoustic and mechanical vibrations.

We currently use analog servos for these servo loops (normally a proportional-integrator^2 configuration with adjustable corner frequencies). We'd like to develop a digital alternative to these analog electronics for higher performance and more flexibility. In this particular project, we want to leverage the DIY audiophile ecosystem that has built up a tremendous knowledge (and code) of configuring audio-based Digital Signal Processors to perform servo functionality. For hardware, we will use the Sigma DSP audio processor and for software SigmaStudio (a well-documented, graphical development tool using NET based framework with no need to learn VHDL).

Supported by us and online resources, your role would be to develop capability and know-how for programming these DSPs to serve as servos and test their performance using in-house network analyzers and a test-lock apparatus in the lab (likely locking a laser to an optical cavity).

 

22. Particle Physics research with the UBC-DarkLight Experiment at TRIUMF

Contact: Dr. Michael Hasinoff | Email: hasinoff@physics.ubc.ca | Web: https://phas.ubc.ca/users/michael-hasinoff

We are planning a new experiment at the TRIUMF/ARIEL e-linac accelerator to search for a possible X(17) particle that could be the mediator of a new short range 5th force in Particle Physics. The student will participate in measurements of the timing and energy response of a prototype scintillation counter using a solid state PMT. She/He will learn to use the sophisticated CERN data analysis program ROOT as well as the Monte Carlo program GEANT4. All the work will take place at TRIUMF and the student will be able to participate in all the activities planned for the other 30 Co-op/USRA students working at TRIUMF.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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