PORTFOLIO
NSF REU-funded research project under Professor Calden Wloka.
Investigated biases towards static and dynamic features in machine-learning models trained for video classification.
As an undergraduate researcher, I implemented key functionality in the analysis pipeline to visualize the behavior of arbitrary neural network video classifiers, ran experiments to measure feature biases, and probed a key mathematical error that I uncovered across many published code implementations of a popular model visualization tool.
Additionally, I strategized research goals and coordinated fellow team members to achieve priorities in parallel.
Research project under Professor Sarah Kavassalis.
Investigating the impact of explicitly encoding spatial relationships in graph neural networks to forecast the evolution of nonlinear atmospheric dynamics from the Lorenz-96 model.
Digital flashcards tailored for practicing American Sign
Language (ASL).
Developed using Flask and hosted on AWS.
Software SULI internship at the National Renewable Energy
Laboratory under K. Shankari.
As a summer intern, I implemented and analyzed candidate
machine learning pipelines, using clustering and random
forests, to predict characteristics of human travel such as
mode of transportation and purpose of travel. To this end, I
also developed a two-step supervised clustering pipeline which
uses DBSCAN and SVMs to cluster trips geospatially.
These predictions can make unlabeled research data usable for
aggregate analysis; they can also be used as label suggestions
in the NREL OpenPATH data-collection tool to streamline the
labeling process and reduce user burden.
Research project under Professor Susan Martonosi.
Developed simulations to analyze the propagation of
information in a social network; examined how the spread of
news was affected by the truthfulness and political bias of
news articles, as well as readership bias and social network
structure.
Image classification of animal species using convolutional neural networks, data augmentation, and transfer learning.
Exploration of classical machine learning algorithms for HMC's
CS189 Programming Practicum elective.
Project goal: understand how classical machine learning
algorithms work, and apply them to predict air pollution
levels on the ground, based on weather data and satellite
observations of pollutants. Dataset from
Zindi.
Data analysis and design at Pomona's Sustainability Office.
This is the annual evaluation of Pomona's performance on
sustainability metrics, newly presented as an interactive
report on Tableau. The 2021 report provides a unique look into
the College's 'baseline' measurements during a year of fully
remote learning, and lends insight on the effects of human
behavior versus infrastructure.
Exploration of the capabilities of reservoir computing. Applications to forecasting the growth of digital communities.