
Daniel Knight
· Associate Research Professor • Program Accreditation & Assessment Specialist • DesignUniversity of Colorado Boulder · Paul M. Rady Mechanical Engineering
Active 1976–2025
About
Daniel Knight is an Associate Research Professor and Program Accreditation & Assessment Specialist at the Paul M. Rady Mechanical Engineering department at the University of Colorado Boulder. His research interests include engineering education and K-12 outreach, focusing on improving educational practices and engagement within engineering disciplines. He is involved in various departments and programs, contributing to the advancement of engineering education and outreach initiatives at the university.
Research topics
- Computer Science
- Sociology
- Engineering
- Engineering management
- Psychology
- Social Science
- Mathematics education
- Medical education
- Pedagogy
- Medicine
- Knowledge management
- Systems engineering
- Engineering ethics
- Process management
- Human–computer interaction
- Business
Selected publications
Toward a Resilience-Oriented Understanding of Unit Test Suites and Refactoring in Software Evolution
2025-09-07
article1st authorCorrespondingUnit testing is widely advocated for improving software reliability and maintainability, yet little is known about how developers experience its interaction with refactoring and technical debt. In my early-stage doctoral research, I conducted a preliminary study with 109 professional developers to investigate how unit test suites support—or hinder—refactoring efforts. The findings reveal developer expectations that unit test suites should enable safe, productive change, but also frustrations with fragile tests that break during refactoring. This paper outlines the insights gained, lessons learned about conducting empirical software engineering research, and my evolving proposal: to define and empirically ground the concept of “unit test suite resilience to refactoring.” I present the open questions I find personally meaningful, and describe how I intend to explore frameworks and practices that increase the adaptability of unit test suites.
Automated Deep Learning Pipeline for Pulse Wave Velocity Measurement in UK Biobank MRI Data
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2025-09-16
articleMotivation: MRI-based pulse wave velocity (PWV) is a golden marker of arterial stiffness, but the acquisition and manual segmentation are time-intensive. This study introduces an automated pipeline designed to accelerate PWV measurement process. Goal(s): We aim to present an automated pipeline for measuring aortic arch PWV from MRI localizers and phase contrast MR (PCMR). Approach: We trained two deep learning models: one to generate 3D aorta segmentations from 2D localizers for accurate 3D aortic length measurement, and another to segment PCMR for flow curves and transit time calculation. Results: Our model proved to be able to produce automated PWV measurement on UK Biobank dataset. Impact: Our model enables automatic aortic arch PWV measurement for UK Biobank subjects, which can be used in the investigation of arterial stiffness and prediction of cardiovascular disease for a large population.
2025-07-22
articlenpj Biosensing · 2025-11-07 · 1 citations
articleOpen accessParkinson's Disease (PD) is an age-progressive disorder caused by misfolding of alpha-synuclein (α-Syn) that can begin years before clinical symptoms appear, making early diagnosis crucial for timely intervention. In this study, a novel antibody-functionalized Organic Electrolyte-Gated Field-Effect-Transistor (Ab-OEGFET) biosensor was implemented to detect α-Syn levels in blood serum samples from an A53T Transgenic (TG) mouse line. PD-like pathology was examined in blood serum using Ab-OEGFET devices and in brain tissue samples using Western Blot and immunohistochemistry. Different forms (monomeric, phosphorylated, oligomeric) of α-Syn were identified in low volumes of blood serum samples collected from TG and Wild Type (WT) populations of mice at ages 2, 5 and 8 months, and the biosensor response was correlated to Blot and immunohistochemistry results. The Ab-OEGFETs performance in this study is a promising result towards a minimally invasive blood biomarker-based multianalyte testing strategy for early screening of PD and similar neurodegenerative disease pathologies.
Application of Aptamers for Neurodegenerative Diseases
Carleton undergraduate journal of science. · 2025-10-08
articleOpen accessThe LADDER Group uses DNA aptamers to develop biosensors and therapeutics for various applications. Aptamers are single-stranded DNA/RNA oligonucleotides that bind to specific targets, such as viruses, proteins and small molecules. Aptamers are selected through a process called systematic evolution of ligands by exponential enrichment (SELEX). Throughout this experience, applications of aptamers to neurodegenerative diseases were explored, including the Use of Aptamers for Inhibition of Alpha-Synuclein Aggregation (Daniel Knight) and Aptamers for the Detection of Traumatic Brain Injuries (Sarah Larose).
Macroethics Education in Engineering and Computing Courses
2025-02-27 · 2 citations
articleOpen accessApplyPolygenicScore: An R package for applying polygenic risk score models
Genetics in Medicine Open · 2025-01-01
articleOpen accessPurpose: A polygenic score (PGS) predicts an individual's genetic predisposition to a complex trait. A PGS is created by estimating the relative contributions of multiple common variants to the overall trait, creating a polygenic risk model (PGM). The PGM is then applied by combining its weights with the genotypes of a specific individual to estimate individual-specific genetic predisposition. Genome-wide association studies have served as the basis for thousands of PGMs, leading to many studies associating PGSs with a range of outcomes. Methods: an open-source R package for applying standardized PGMs to new genetic data. We demonstrate its capabilities in a case study, applying a PGM for body mass index (BMI) in 1071 patients diagnosed with bladder, liver, and endometrial cancer. Results: includes functions for input validation, allele matching, and PGS computation and visualization and is extensively documented. The computed PGS for BMI predicted BMI in patients with cancer, but its low accuracy indicates a larger role for nongenetic factors in BMI-influenced cancer outcomes. Conclusion: encourages the wider research community to extend the findings of the statistical genetics niche, facilitating broader use of PGSs and subsequent novel discovery.
An Assessment of Simulation-Based Learning Modules in an Undergraduate Engineering Economy Course
2024-02-06 · 7 citations
articleOpen accessWe propose and assess the effectiveness of novel immersive simulation-based learning (ISBL) modules for teaching and learning engineering economy concepts. The proposed intervention involves technology-enhanced problem-based learning where the problem context is represented via a three-dimensional (3D), animated discrete-event simulation model that resembles a realworld system or situation that students may encounter in future professional settings. Students can navigate the simulated environment in both low-and high-immersion modes (i.e., on a typical personal computer or via a virtual reality headset). The simulation helps contextualize and visualize the problem setting, allowing students to observe and understand the underlying dynamics, collect relevant data/information, evaluate the effect of changes on the system, and learn by doing. The proposed ISBL approach is supported by multiple pedagogical and psychological theories, namely the information processing approach to learning theory, constructivism theory, self-determination theory, and adult learning theory. We design and implement a set of ISBL modules in an introductory undergraduate engineering economy class. The research experiments involve two groups of students: a control group and an intervention group. Students in the control group complete a set of traditional assignments, while the intervention group uses ISBL modules. We use well-established survey instruments to collect data on demographics, prior preparation, motivation, experiential learning, engineering identity, and self-assessment of learning objectives based on Bloom's taxonomy. Statistical analysis of the results suggests that ISBL enhances certain dimensions related to motivation and experiential learning, namely relevance, confidence, and utility. We also provide a qualitative assessment of the proposed intervention based on detailed, one-on-one user testing and evaluation interviews.
2024-02-07 · 2 citations
articleOpen accessSenior authorResearchers have looked into ways to make computer science assignments more engaging, practical, and beneficial to students to improve learning outcomes by increasing student appeal. Offering a pool of assignments and allowing students to choose their preferred assignments is considered as a potential method for improving learning outcomes. In this paper, we investigate the effect of context choice for assignments in an object-oriented programming course that covers various topics such as object-oriented programming concepts, database design and implementation, graphical user interface design, and web application development. Students complete three immersive simulation-based learning (ISBL) modules as course assignments. ISBL modules involve technology-enhanced problem-based learning where the problem context is represented via a threedimensional (3D), animated discrete-event simulation model that resembles a real-world system or context, in this case, we have three simulated systems/contexts around which ISBL assignments are defined: an airport, a manufacturing system, and a hospital emergency department. The research experiments involve four groups: (1) students with no choice who use the same assigned simulated system for all three ISBL assignments; (2) students with no choice who are given a different simulated system for each ISBL assignment; (3) students who can choose their preferred simulated system at the beginning but cannot change their choice for future assignments; and, (4) students who can choose at the beginning and switch between the three simulated systems for subsequent assignments. Data are collected over multiple semesters and statistical analyses are conducted to compare the four groups in terms of motivation, experiential learning, and self-assessment of learning. We also conduct qualitative assessments in the form of interviews to support and explain our statistical results.
Molecular Therapy — Nucleic Acids · 2024-06-15 · 8 citations
articleOpen accessA neuropathological hallmark of Parkinson's disease (PD) is the aggregation and spreading of misfolded α-synuclein (αSyn) protein. In this study, a selection method was developed to identify aptamers that showed affinity for monomeric αSyn and inhibition of αSyn aggregation. Aptamer <b>a-syn-1</b> exhibited strong inhibition of αSyn aggregation <i>in vitro</i> by transmission electron microscopy and Thioflavin T fluorescence. <b>A-syn-1</b>-treated SH-SY5Y cells incubated with pre-formed fibrils (PFFs) showed less intracellular aggregation of αSyn in comparison with a scrambled oligonucleotide control, as observed with fluorescent microscopy. Systemic delivery of <b>a-syn-1</b> to the brain was achieved using a liposome vehicle and confirmed with fluorescence microscopy and qPCR. Transgenic mice overexpressing the human A53T variant of αSyn protein were injected with <b>a-syn-1</b> loaded liposomes at 5 months of age both acutely (single intraperitoneal [i.p.] injection) and repeatedly (5 i.p. injections over 5 days). Western blot protein quantification revealed that both acute and repeated injections of <b>a-syn-1</b> decreased levels of the aggregated form of αSyn in the transgenic mice in the prefrontal cortex, caudate, and substania nigra (SNc). These results provide <i>in vitro</i> and <i>in vivo</i> evidence that <b>a-syn-1</b> can inhibit pathological αSyn aggregation and may have implications in treatment strategies to target dysregulation in PD.
Frequent coauthors
- 206 shared
Angela Bielefeldt
University of Florida
- 199 shared
Madeline Polmear
University of Colorado Boulder
- 138 shared
Chris Swan
Tufts University
- 109 shared
Nathan Canney
Taylor Wimpey (United Kingdom)
- 83 shared
Jacquelyn Sullivan
University of Colorado Boulder
- 69 shared
Daria Kotys-Schwartz
University of Colorado Boulder
- 46 shared
Beverly Louie
University of Colorado System
- 42 shared
Tanya Ennis
University of Colorado Boulder
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