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Machine Learning in Modern Cancer Treatment

Sia is a 17 year-old high school student from East Greenbush, NY.
High School
Albany Academy for Girls
Graduation Year
Student review
I was very impressed with the way Polygence helped me discover my interest in both machine learning and oncology. Akshaya was an amazing mentor and made sure I was understanding everything I was reading. If I came across a hard topic that I struggled to fully understand, she would use creative methods to explain it to me and help me talk through it. While I did expand my knowledge immensely through working on this project, I would say the most valuable take away was gaining confidence in myself to conduct my own research and produce my own review article. Many of the papers I read were a challenge to understand and I don't think I could have pushed myself to understand them without the help of Akshaya and Polygence. The Polygence Team, including Staci, was also very supportive in making sure that I was happy with my project and was a great help when it came to submitting my paper for publication in a journal and publicizing my blog post.
Project description

When Sia started out, she had minimal knowledge of Artificial Intelligence and the many subcategories within it. Through writing a research paper and creating a blog, she was able to research various types of machine learning and the ways they have been, and continue to be, incorporated into the medicine and healthcare field. Sia specifically focused on cervical cancer, lung cancer, and brain cancer. The majority of her time was spent researching for and writing her research paper, which addresses the promising applications of AI as well as the potential pitfalls. The blog was a reservoir of intriguing article, which reviewed them in simple language so they could be accessible to a broader audience.

Machine Learning in Modern Cancer Treatment
Project outcome

Sia wrote a research paper (currently under review) and in tandem, started a blog to review key research papers for a general audience.

MD/PhD Doctor of Medicine and of Philosophy candidate
Medicine, Biology, Comp Sci, Social Science, Quantitative
Data Science, Machine Learning, Mathematical Modeling, Political Writing, Policy Analysis, Technical Writing

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