Undergraduate Research Project Marketplace

We are looking for undergraduates who are interested in how students learn and interact with one another in college classrooms. We work in a number of areas:

1. qualitative analysis* of student response to collaborative learning activities
2. qualitative analysis* of student responses on physics assignments to identify areas of confusion
3. statistical analysis of education data (using Python and R)
4. creation of curriculum materials and analysis of their effectiveness, including software development

Previous research experience is not required. We encourage you to apply if you are enthusiastic about studying the learning process in physics!

* In our work, qualitative analysis involves analyzing students' written work to find themes and patterns. For example, this may be analyzing various approaches students take to solving a certain physics problem.

Explore a hypothetical new particle at the Large Hadron Collider.

The student will learn to use tools which simulate collisions of a new hypothetical particle, and analyze the data to determine whether the LHC has the power to discover it.  Examples of previous projects: https://arxiv.org/abs/1609.05251

Our group carries out observational research on galactic nuclei and supermassive black holes. Ongoing projects include direct measurement of masses of black holes in galaxy centers, studies of the physical properties of active galactic nuclei including reverberation mapping (monitoring the time variability of active galactic nuclei to map out their structure on scales of light-days around the black hole), and examination of the relationships between supermassive black holes and their host galaxies. We use data from many facilities including Lick Observatory, Keck Observatory, Las Cumbres Observatory, the Hubble Space Telescope, and the Atacama Large Millimeter/submillimeter array. There are many opportunities for undergraduate participation in various aspects of this work, including measurement of light curves of active galaxies using robotic telescope data.

At present, I'm not able to offer any more summer research positions for summer 2019.

Research Project Description: Huolin Xin’s DeepEM lab focuses their research on the imaging of atoms and their bonding electrons with artificially intelligent transmission electron microscopes. His research group uses state-of-the-art transmission electron microscopy (TEM), electron energy loss spectroscopy, 4D electron diffraction, 3D electron tomography, in-operando liquid cells in conjunction with deep learning and other machine learning algorithms to track and quantify the location, species, crystal phase, and electronic structures of individual atoms inside materials. Potential research projects include:

• Development of deep learning enabled self-driving transmission electron microscopy, and big data mining and analytics.
• Development of in-situ live electron tomography and novel reconstruction techniques for the 3D imaging of dissolution or growth of energy materials in liquids and soft materials/molecules at cryogenic temperatures.
• Development of theories and simulations of fast electron channelling in solid materials and novel imaging techniques based on 4D electron diffraction.
• Identifying cation intermixing and phase transformation in lithium-ion battery cathode materials for making high-energy-density batteries,
• 3D Strain mapping of fuel cell nanocatalysts to understand how their strain profile is connected with their catalytic properties.

Group webpage: https://sites.google.com/view/deepem

Density Functional Theory is one of the most popular and versatile electronic structure methods available in condensed-matter physics, computational physics, and chemistry. However, like any other theory, DFT is not perfect, and our group is devoted to a better understanding of the underlying limitations of the approximations and possible improvements. One of our primary areas of research is semi-classical DFT, which focuses on finding the leading corrections to local density approximations. A newer approach is machine-learning DFT, where we  use machine learning tools to approximate the kinetic energy functional for several simple quantum mechanical systems. Thermal DFT, on the other hand, investigates density functionals at non-zero temperature for use in warm, dense matter. Other directions of research include density-corrected DFT, strong correlation in solids (with Prof. Steve White) and electronic excitations.

Group webpage: http://dft.uci.edu/index.php

My research group studies the global evolution of galaxies over cosmic time, with a particular emphasis on the role of environment in shaping galaxy properties. For more, see past and ongoing project here.