Publications
Using networks to relate student interactions to their recognition of strong peers
Gaining recognition from peers has been shown to improve student persistence and career intentions in physics. It is important, therefore, to understand how students develop perceptions of their peers. Prior research suggests that interactions are one possible mechanism for peer recognition: interacting with others allows students to demonstrate their physics skills and knowledge and acquire recognition as a physicist. To probe this explanation directly, we use methods from social network analysis to compare students' self-reported interactions to their recognition of strong peers.
Validating a Weekly Survey to Understand Student Division of Roles in Physics Labs
Why the machine (dis)agrees: understanding uncertainty in natural language processing classifications
Introductory physics students' recognition of strong peers: Gender and racial or ethnic bias differ by course level and context
Researchers have pinpointed recognition from others as one of the most important dimensions of students' science and engineering identity. Studies, however, have found gender biases in students' recognition of their peers, with inconsistent patterns across introductory science and engineering courses. Toward finding the source of this variation, we examine whether a gender bias exists in students' nominations of strong peers across three different remote, introductory physics courses with varying student populations (varying demographics, majors, and course levels).
Neuromuscular embodiment of feedback control elements in Drosophila flight
While insects such as Drosophila are flying, aerodynamic instabilities require that they make millisecond time scale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons.
Role of conservation laws in the density matrix renormalization group
We explore matrix product state approximations to wave functions which have spontaneously broken symmetries or are critical. We are motivated by the fact that symmetries, and their associated conservation laws, lead to block-sparse matrix product states. Numerical calculations which take advantage of these symmetries run faster and require less memory. However, in symmetry-broken and critical phases the block-sparse ansatz yields less accurate energies. We characterize the role of conservation laws in matrix product states and determine when it is beneficial to make use of them.
Understanding interaction network formation across instructional contexts in remote physics courses
Engaging in interactions with peers is important for student learning. Many studies have quantified patterns of student interactions in in-person physics courses using social network analysis, finding different network structures between instructional contexts (lecture and laboratory) and styles (active and traditional). Such studies also find inconsistent results as to whether and how student-level variables (e.g., grades and demographics) relate to the formation of interaction networks.
Polarity of the CRISPR roadblock to transcription
CRISPR (clustered regularly interspaced short palindromic repeats) utility relies on a stable Cas effector complex binding to its target site. However, a Cas complex bound to DNA may be removed by motor proteins carrying out host processes and the mechanism governing this removal remains unclear. Intriguingly, during CRISPR interference, RNA polymerase (RNAP) progression is only fully blocked by a bound endonuclease-deficient Cas (dCas) from the protospacer adjacent motif (PAM)-proximal side.
STRAINS: A big data method for classifying cellular response to stimuli at the tissue scale
Cellular response to stimulation governs tissue scale processes ranging from growth and development to maintaining tissue health and initiating disease. To determine how cells coordinate their response to such stimuli, it is necessary to simultaneously track and measure the spatiotemporal distribution of their behaviors throughout the tissue. Here, we report on a novel SpatioTemporal Response Analysis IN Situ (STRAINS) tool that uses fluorescent micrographs, cell tracking, and machine learning to measure such behavioral distributions.
Reentrant rigidity percolation in structurally correlated filamentous networks
Many biological tissues feature a heterogeneous network of fibers whose tensile and bending rigidity contribute substantially to these tissues' elastic properties. Rigidity percolation has emerged as an important paradigm for relating these filamentous tissues' mechanics to the concentrations of their constituents. Past studies have generally considered tuning of networks by spatially homogeneous variation in concentration, while ignoring structural correlation.