Biography

A Computer Science researcher specializing in artificial intelligence for medical imaging. I am currently pursuing a Master of Science in Computer Science and Systems (Data Science track, thesis pathway) at the University of Washington Tacoma. Her thesis focuses on developing AI methods for pelvic ultrasound analysis, with the goal of advancing early detection of infertility-related diseases such as pelvic adhesions. As someone personally affected by infertility due to pelvic adhesions, I am deeply committed to advancing non-invasive, AI-assisted diagnostic tools that can improve early detection and clinical outcomes for women facing similar challenges. My academic background includes a B.S. in Computer Science, complemented by undergraduate research in multimodal generative AI for medical imaging. In addition to research, she has experience building full-stack applications, working with machine learning pipelines, and leading student technology initiatives. Her long-term goal is to pursue a Ph.D. and contribute to patient-centered AI solutions for women’s health diagnostics.

Academic Status

Masters Student - 1st

Research Area/Department

Computer Science; Data Science; Machine Learning/AI

Major/Specialty

Pursuing M.S in Computer Science and Systems with a thesis "AI for Early Pelvic Adhesion Detection in Cine-MRI and Transvaginal Ultrasound "

Degrees Earned or in Progress

B.S in Computer Science and Systems, June 2025

Academic Preparation

By the time of the summer internship, I will have completed: TCSS 543: Advanced Algorithms TCSS 555: Machine Learning TCSS 598: Research Seminar TCSS 551: Big Data Analytics TCSS 558: Applied Distributed Computing TCSS 598: Research Seminar TCSS 700: Thesis Research TCSS 588: Bioinformatics

Research/Publications

(see resume)

Research/Academic Interests

My past research has focused on generative AI in medical imaging, where I explored multimodal models for diagnostic support of sepsis and clinical question answering using datasets such as MIMIC-CXR. Building on this work, my current master’s thesis centers on developing AI methods for pelvic ultrasound analysis to support early detection of infertility-related conditions, with a particular focus on pelvic adhesions

Computational and Data Science Areas

Applied Computer Science; Artificial Intelligence and Intelligent Systems; Basic Medicine; Computer Science; Condensed Matter Physics; Health Sciences; Other Medical Sciences

Motivation

I am motivated to pursue this internship because it represents an opportunity to expand my expertise in artificial intelligence while contributing to impactful research. My academic journey and thesis work have strengthened my technical foundation in machine learning and medical imaging, but I am eager to apply these skills in a collaborative research environment where I can learn from experienced mentors and peers. I see this internship as a chance to deepen my knowledge of AI methodologies, gain hands-on experience with real-world datasets, and further develop the research skills necessary for my long-term goal of pursuing a Ph.D. and advancing AI applications in Women's healthcare.

Lightning Talk Title

AI for Early Pelvic Adhesion Detection Sonography Imaging

Keywords (Maximum 20 words)

Women's Infertility;Women's Health; Medical Imaging;