Available research projects


2024 projects

The KU particle astrophysics group specializes in radiowave detection of neutrinos, specifically ultra-high energy neutrinos interacting in cold (Antarctic or Greenlandic) polar ice. Depending on their software background, students would work on either data analysis from existing experiments (ARA, ANITA, PUEO in Antarctica and/or RNO in Greenland) or perform design, simulation, construction and measurement of the radio-wave antennas and hardware used to perform measurements.

 

I. Project Title: Improving Equity in Physics Instruction



I. Project Description: The goal of this project is to quantitatively characterize the interplay between physics self-efficacy, calculus transfer ability, identity (gender, ethnicity, first generation status, etc.), and learning/performance in introductory physics. Students will pursue this goal by developing computer code necessary to jointly analyze data collected from separate assessments of self-efficacy and mathematics transfer. Based on their analysis, students will then have the opportunity to develop revised assessments as well as curriculum revisions, including supplemental instruction tools, specifically targeting improvements in self-efficacy, calculus transfer, or both, with the goal of improving equity in student performance and retention.

 

II. Project Title: How certain are students in their understanding of uncertainty?

II. Project Description: The goal of this project is to understand how students in introductory physics laboratory courses employ uncertainty calculations in the analysis of their data and apply uncertainty-based reasoning in subsequent experimental design. Data has been previously been gathered using an assessment of proficiency with uncertainty and error propagation. Students will develop the computer code necessary to determine correlations between within these data and between these data and student identity, with the purpose of refining the assessment. An additional goal would be to compare assessment results from semester to semester to determine if any changes to the laboratory curriculum have had a measurable effect on student understanding of uncertainty analysis.

 

III. Project Title: Are introductory laboratories helping or hurting?

III. Project Description: An individual's self-efficacy is a measure of their belief to succeed in a particular discipline or at a particular task, and lower self-efficacy has been linked with poorer performance and lower retention in physics courses. The goal of this project is to determine how laboratory courses affect physics self-efficacy, thereby providing data necessary for making these courses more beneficial. Students will develop the computer code necessary to compare data from self-efficacy assessments administered in several laboratory courses to determine what course, instructor, and/or student-centered variables most strongly influence self-efficacy scores and gains. An additional goal is to compare self-efficacy survey results from semester to semester to determine if any changes the curriculum of the laboratory courses have had a measurable effect on self-efficacy.

 

IV. Project Title: How do students affect the behavior of their instructors?

IV. Project Description: This project involves working with faculty members to improve a machine learning algorithm for analyzing sentiment from free response comments in student evaluations of teaching by the graduate teaching assistants in their introductory physics laboratory courses. The goal of this project is to understand the feedback mechanism between evaluations of teaching and subsequent changes in teaching style or approach. This will enable us to better understand the needs and expectations of students enrolled in these courses as well as better prepare our graduate teaching assistants to be effective instructors.

 

V. Project Title: Learning from failure: evaluating the interplay between standards-based grading and student mindset.

V. Project Description: The goal of this project is to determine how students navigate an introductory physics course implementing standards-based grading and how student responses to that grading system are mediated by their mindset, self-efficacy, and other dimensions of their identity.  Students will pursue this goal by jointly analyzing data collected from separate assessments of self-efficacy, mindset, course performance, and attitude.  Of particular interest is whether students take advantage of the opportunities afforded to them by this grading system to learn from their mistakes, the resources they employ if they do so, and how aspects of their identity correlate with these decisions and outcomes.  

 

Biography:

Chris Fischer is the director of the Engineering Physics Program and the department chair. He has been extensively involved in curriculum development, including the redesign of the department’s calculus-based introductory physics sequence. His research interests include how mathematics transfer affects student learning in physics and how those effects are modulated by student identity.

 

Introduction to quantum information theory 

We extend what we learn from quantum mechanics to understand quantum information theory. We will begin by reviewing the postulates of quantum mechanics and Shannon’s information theory. We will compare classical Shannon’s theory and its quantum version, entanglement, Bell’s inequality, quantum communication etc. For quantum machine learning study, we will learn how quantum algorithms are utilized to solve problems which are difficult to approach using classical methods.

Good python skills are required to study quantum machine learning.

Strong background on quantum mechanics and linear algebra recommended.

Outcome: understand how quantum algorithms work and advantage of quantum machine learning.

Faculty advisor:  KC Kong

KC Kong is a theoretical physicist whose interest ranges from a very small scale (particle physics) to a very large scale (particle astrophysics and cosmology). He works on various topics including models with extra dimensions, supersymmetry, dark matter, collider phenomenology, application of machine learning and quantum algorithms.

Lithium-ion batteries, with their high-energy density, high-discharge voltage, and relatively low cost, have been the battery of choice for a wide variety of applications, including portable consumer electronics, hybrid- and all-electric cars, and grid-scale energy storage. However, these batteries also come with drawbacks: potential safety issues and growing concerns regarding the availability of lithium and of the cathode materials. Replacing lithium ions with earth-abundant, non-toxic, and non-flammable ions would alleviate these concerns. We will use computational methods that start from only the atomic structure (so called first-principles methods), based on density functional theory, to find and investigate new cathode materials without having to rely on time-consuming experiments.

Outcomes: During this project students will learn the basics of solid state physics and first-principles methods. Programming skills and basic linux shell, including experience with high-performance computing platforms, will be acquired or further extended.

Bio: Dr. Peelaers is a computational condensed matter physicist who uses and develops first-principles methods, based on density functional theory to improve our understanding of the physics of nanostructured, electronic, and energy materials, with a focus on wide-bandgap oxides. His work aims to design and improve the next generation of power electronics, sensors, memristors, and batteries.

We are looking for a student to work on antenna modeling for the upcoming Radar Echo Telescope project. This will require prior experience in programming (python and/or c++) and the desire to learn new methods for optimizing designs. Prior experience with E&M is not required, but will be very helpful to making a quick start on the project.

Faculty advisor: Steven Prohira

The student will design, construct, and test a laser heating apparatus that will be integrated with an inkjet printer for printing high-performance semiconductor quantum dots/graphene photodetectors. The goal is to demonstrate on-chip programmable printing of high-performance electronic and optoelectronic devices with minimum thermal budget using focused laser heating coordinated with printing. The result is directly related to future electronics integrating nanostructured devices with CMOS. The student(s) will determine what the best approach is and implement it accordingly. The student(s) is expected to design and integrate a variety of mechanical, optical and electronic to make the apparatus functional and user friendly.

Two-dimensional (2D) materials are atomically thin crystals, highly confined in the out-of-plane dimension. 2D materials host outstanding electronic and optical properties making them promising for beyond-Moore and quantum technologies. One example of the properties of 2D materials distinguishing them from three-dimensional materials is their electrostatic properties. In this proposed research, the undergraduate student will learn (1) electrostatic properties of 2D materials. (2) first-principles calculations based on Density Functional Theory (DFT), (3) performing DFT calculations in a high-throughput way on remote supercomputers. (4) extracting and analyzing information from a large dataset using python scripts.

A strong background in Python is required.

Faculty advisor: Qunfei Zhou