Undergraduate Research Showcase

Welcome to Columbia University’s 9th Annual
Undergraduate Research Showcase organized by the Fu
Foundation School of Engineering and Applied Science.
Faculty in the Engineering School and throughout the University
recognize the importance of research in enriching undergraduate
education and strive to make opportunities available for our ambitious
undergraduates. Similarly, our undergraduates recognize the value of
the unique experiences they gain from conducting research and
exploring the cutting-edge of science and engineering disciplines in
world-class facilities. The Undergraduate Research Showcase provides
a venue for undergraduate Columbia University students from
Engineering to share their experiences, discoveries, and enthusiasm with
their fellow peers, faculty, and administrators.
This year, we have seven students participating in the Undergraduate
Research Showcase wanting to share their experiences with
you. Projects cover a wide range of subjects that are as varied as our
student-body.
Although we cannot interact in person this year, through the tenacity of
our students, faculty, and the Engineering School, we still have the
opportunity to hear about our students’ cutting-edge research. I
encourage you to partake in the online presentations this year to learn
more about their exciting research - our students continue to do amazing
things in these difficult times!
Barclay Morrison
Vice Dean of Undergraduate Programs
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Table of Contents
Quantifying Myosin Networks and Their Roles in Morphogenesis 4
Cole James Allan, Mechanical Engineering, SEAS ‘21
Alternating Bubbles Emerging from Two Interacting Vertical Gas Jets in a Liquid 5
Boyuan Chen, Chemical Engineering, SEAS ‘23
Electricity Price Models in the Context of Increasing Renewable Energy Generation 6
Felipe Aleixo dos Santos Couto, Mechanical Engineering, SEAS ‘22
A Parametric, Ultrasound-Based Model of the Uterus in Late Gestation 7
Arielle Feder & Divya Rajasekharan, Mechanical Engineering, SEAS ’22 & ‘21
Automated Artery and Vein Classification of Pulmonary Vessels 8
Anthony Luo, Computer Science, SEAS ‘23
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Quantifying Myosin Networks and Their Roles in Morphogenesis
Cole Allan, cja2160@columbia.edu
SEAS ‘21, Mechanical Engineering, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Dr. Karen Kasza, Bonomi Summer Scholar, Kasza Living Materials Laboratory,
Columbia University
Abstract
Morphogenesis is a process during embryonic development in which cells and/or tissues
develop their shape. These morphogenetic events utilize a network of motor proteins,
non-muscle myosin II, to generate forces. Thus, being able to identify and characterize
this network makes it possible to synthetically control tissue folding, and, potentially in
the future, build robust tissue architectures out of 2-dimensional tissue sheets. The goal of
this study, completed virtually in the Kasza Living Materials Laboratory, was to develop
a software tool that would identify the myosin networks within Drosophila embryos at
various stages of development and to quantitatively assess how the myosin networks
influence the propensity of tissues to remodel. Using confocal microscopy, supracellular
myosin networks were fluorescently tagged in high resolution movies. Some of the
properties analyzed in the developed software include measuring the network
connectivity, measuring the flexibility of each network edge, and identifying regions of
rapidly changing myosin. These properties are essential to characterize the structure of
myosin networks. Specifically, we found that myosin segments became less tortuous
when there was tissue elongation in the same orientation. Additionally, the cell velocity
was tracked to identify distinct regions of tissue that behaved more fluid-like. In the
future, with this understanding of myosin networks and cellular movement, we are
hopeful that we will be able to coordinate cell behavior and manipulate the mechanical
properties within tissues.
Keywords
Morphogenesis, Drosophila melanogaster, Myosin Networks
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Alternating Bubbles Emerging from Two Interacting Vertical Gas Jets in a Liquid
Boyuan Chen, bc2878@columbia.edu
SEAS ’23, Chemical Engineering, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Dr. Christopher Boyce, Bonomi Summer Scholarship, Columbia University
Abstract
Optical imaging experiments of two vertical gas jets injected into a liquid demonstrate
that bubbles pinch off from these jets in an alternating (180 degrees out of phase) pattern.
Image analysis demonstrates that this alternating pattern occurs only at sufficiently high
Froude numbers and sufficiently low ratios of the separation distance between orifices to
the orifice diameter. Otherwise, the bubbles from the two jets breakoff at uncoordinated
times relative to one another. A physical mechanism is proposed in which the alternating
pattern occurs due to a growing jet pushing liquid between the two jets towards the
second jet, such that the second jet pinches off, forming a bubble. This mechanism is
used to formulate a simplified coupled harmonic oscillator model which predicts
qualitatively the transition from uncoordinated to alternating bubble breakoff.
Keywords
Gas jets, asynchronous break-off, coupled oscillator
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Electricity Price Models in the Context of Increasing Renewable Energy Generation
Felipe dos Santos Couto, f.couto@columbia.edu
SEAS ’22, Mechanical Engineering, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Dr. Bolun Xu, Undergraduate Research Involvement Program, The Earth Institute,
Columbia University
Abstract
Renewable energy is no longer an interesting possibility for the future but rather an
urgent demand in the present to fight the climate crisis. Nevertheless, academia, industry,
and public entities still face many challenges to increase penetration of renewable sources
on the world energy mix. In this context, storing energy has risen as a key alternative and
numerous storage solutions have been developed. The purpose of this study is to better
understand how large-scale energy storage systems (ESS) impact electricity prices. We
developed interpretable machine learning models and analyzed how supply and demand
attributes correlate with price fluctuations – especially, price spikes, which are a major
sign of market inefficiency. Optimal Regression Trees and Multiple Linear Regressions
were applied to the Southwest Power Pool market data, shedding some light upon the
attributes’ sensitivities. We then utilized the sensitivities to estimate price reductions due
to energy storage. Results showed a potential reduction, on average, of 19.0% on price
spikes. Further studies shall continue to investigate the effects grid-scale ESS on prices.
By doing so, we hope to corroborate with the expansion of renewable energy generation
and ESS on the grid.
Keywords
renewable energy, energy storage, interpretable machine learning, electricity price
models
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A Parametric, Ultrasound-Based Model of the Uterus in Late Gestation
Divya Rajasekharan & Arielle Feder, dr2940@columbia.edu & adf2153@columbia.edu
SEAS ’21 & ’22, Mechanical Engineering, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Dr. Kristin Myers, Summer@SEAS, Myers Soft Tissue Lab, Columbia University
Abstract
Pregnancy poses an interesting mechanical problem, as the female body must evolve to
accommodate a growing fetus. Since direct research into the mechanical environment of
pregnancy is precluded for clear ethical reasons, 3D models provide a unique opportunity
to study the mechanical properties of the uterus via simulation. In late pregnancy (LP),
the geometry of the uterus changes distinctly: the elliptical shape of early pregnancy
grows more tapered towards the cervix end, terminating in a V-like profile in the coronal
plane. This shift raises the question of whether geometric changes are necessary to
mediate important developments in the load-bearing properties of the uterus. To
investigate this possibility, we built two uterus models to accommodate the late-gestation
coronal shape with varying degrees of accuracy. The first is based on a limited number of
ultrasound measurements, with overall shape informed by patient-averaged
characteristics derived from MRI. The second is a highly parameterized model, driven by
MRI measurements. Two additional models—a ground truth model segmented directly
from MRI and the lab’s current, elliptical parametric model—were used as points of
reference. By comparing these models’ behavior in a simple static load analysis for 5 LP
patients, we evaluated the mechanical significance of the change in LP coronal shape. We
found that the stress distribution was highly dependent on local fluctuations in uterine
wall thickness. Models based on limited ultrasound measurements were not always
sensitive enough to capture this variation. In the LP models, we observed that the
tapering of the uterus had the effect of drawing pressure loads away from the cervical os
and into the lower side walls, while the blunter coronal profile of early-gestation allowed
stress to concentrate at the os. Finally, the first and second principal strain directions—
oriented circumferentially and longitudinally, respectively—were consistent across all
four models. In conclusion, the late pregnancy uterus exhibits load bearing properties
distinct from earlier pregnancy, mediated by a change in coronal shape. Furthermore, a
parametric modeling framework, which accounts for the coronal shape on a patientaveraged
basis, may be a viable option to efficiently represent the load-bearing
characteristics of the LP uterus when compared to higher resolution, but computationally
intensive, MRI-driven models.
Keywords
pregnancy, biomechanics, 3D modeling, simulation, finite element analysis (FEA),
ultrasound, magnetic resonance imaging (MRI)
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Automated Artery and Vein Classification of Pulmonary Vessels
Anthony Luo, anthony.luo@columbia.edu
SEAS ‘23, Computer Science, Columbia University
Supervising Faculty, Sponsor, and Location of Research
Professor Andrew F. Laine, Summer@SEAS
Heffner Biomedical Imaging Lab, Columbia University
Abstract
An accurate and automatic system for artery and vein classification of pulmonary vessels
has the potential to drastically increase efficiency of medical diagnoses and analyses that
require artery and vein labeling of pulmonary vessels. In particular, labeled arteries and
vessels facilitate site isolation for vascular pathology, and assist inferences of causes of
pulmonary vascular dysfunction. We present a software pipeline to perform automatic
artery and vein classification up to a user specified diameter without the need for contrast
enhanced CT or manual seed point initialization. The performance of this software
pipeline is significantly faster than manual labeling by an expert analyst and provides
additional flexibility through vessel diameter and branch length filtering options. Our
pipeline uses an expert system based approach to classification by leveraging the
closeness and co-orientation of pulmonary arteries and bronchi. Compared to prior art,
our system introduces a novel combination of straight-line and per-voxel co-orientation
and co-distance calculations. We plan to compare our pipeline against a semi-automated
region growing approach and manually annotated ground truth.
Keywords
pulmonary vessels, artery vein classification, airways
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