Undergraduate Research Showcase

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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


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


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


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


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.


Morphogenesis, Drosophila melanogaster, Myosin Networks


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


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.


Gas jets, asynchronous break-off, coupled oscillator


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


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.


renewable energy, energy storage, interpretable machine learning, electricity price



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


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.


pregnancy, biomechanics, 3D modeling, simulation, finite element analysis (FEA),

ultrasound, magnetic resonance imaging (MRI)


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


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.


pulmonary vessels, artery vein classification, airways


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