Hi! šŸ‘‹

Iā€™m Julia, a senior at the Massachusetts Institute of Technology studying computer science and urban science & planning with a minor in entrepreneurship & innovation.

Iā€™m most interested in the intersection of product development, software design, and cities ā€” specifically, human-centered design and building software for social good. At MIT, Iā€™m currently involved in Asian American Initiative, DanceTroupe, and Filipino Students Association, and was previously a part of Ethical Technology Initiative, Code for Good, StartLabs, and Divest.

I most recently worked on product management at Suno AI and was a research assistant in MIT CSAIL's Software Design Group. I was also an Innovation Fellow at The Lab @ MassDOT, worked on policy at Transportation for America, and was a 2022 Civic Innovation Corps Fellow at the City of Boston, where I worked on data analysis and visualization. I've also worked in MIT's Data + Feminism Lab and Civic Data Design Lab.

In my free time, I enjoy playing tennis and basketball, searching for new books on Goodreads, and exploring places via bus, train, or by foot.

You can reach me at jcamac [at] mit [dot] edu or find me on LinkedIn and Github.

research

MMIW/P Grassroots Resource Map
2022ā€“2023, Data + Feminism Lab at MIT
Collaborated with the Waking Women Healing Institute to co-design and build software for data collection & storytelling to eliminate violence against Indigenous women and girls.
Distance Unknown: Risks and Opportunities of Migration in the Americas
2022, Civic Data Design Lab at MIT
Performed data analysis for and designed and built components of 'Distance Unknown', an interactive exhibition for the United Nations World Food Programme visualizing Central American migration data.
DeepGlio: A Novel Computational Pipeline for the Segmentation, Classification, and Characterization of Low and High-Grade Brain Tumors
2019ā€“2020, advised by Dr. Xuexia Wang at UNT
Designed, built, and tested DeepGlio, a computational pipeline utilizing convolutional and deep neural networks to segment, classify, and characterize low and high-grade gliomas using deep learning methods and patient MRI, genetic, demographic, and survival data.
Predicting the Development of Secondary Central Nervous System Cancer Through Ensemble Learning Methods
2018ā€“2019, conducted under Dr. Xuexia Wang at UNT
Developed a computational method for the prediction of secondary central nervous system cancer through ensemble machine learning approaches utilizing both clinical and genetic data curated through multiple feature selection methods.
A Computational Method for Predicting Cancer Drug Effectivity Using Machine Learning Techniques
2017ā€“2018, conducted independently
Used machine learning to predict drug effectivity through IC50 values based on the genomic and physicochemical characteristics of cell lines and drugs in order to speed up drug development processes and create more effective compounds.

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