About Me

Hi, my name is Shirley Su. I am a freshman at the University of Southern California (USC), pursing my Bachelors in Computer Science.

My passion is to bring proactive change in my community: whether it is creating technology to develop innovative solutions, or volunteering to help out local organizations, I always want to bring positive change. I am especially interested in learning Machine Learning, and its potential to solve several key issues such as climate change.

At USC, I am a developer at Code the Change (CTC), an organization that drives social change by building applications for non-profits. This year, I am a part of ImagineLA, a nonprofit that is dedicated to ending the cycle of family homelessness. We are building a mortgage calculator that allows families to calculate how much government benefits they will receive and lose depending on job payment. I am also a member of Center for AI in Society (CAIS++). I am currently attending a 12 week deep learning curriculum, and next semster I will apply my newfound knowledge into building AI applications for social goood. I also joined the Chinese American Student Association (CASA). Within this close-knit group, I have found an inclusive community and formed meaningful friendships. I'm super grateful to have met them, especially during the uncertain times of covid.

In my free time, I enjoy competitive swimming and playing tennis. I also love reading, especially Agatha Christie murder mysteries and Steven Pinker books. My favorites include "And Then There Were None" and "Enlightenment Now". With friends, I enjoy playing board games such as Catan - tip: always secure a wheat spot!

Experience

June 2020 - August 2020

This summer, I served as an Onnx Open Source Contributor. Working with my previous mentors, I deployed EfficientNet-Lite4, an image classification model, as well as SSD-MobilenetV1, an object detection model, to the Onnx Model Zoo. This process involved converting its Tensorflow framework to Onnx format, running validation and inferencing, and writing the preprocessing and postprocessing code for the models. In addition, I also contributed a Model Zoo API, which allows users to download pretrained models from a script and obtains the model's metadata. I created a function that allows the user to download a specificed model. Moreover, I also utilized regular expression and model folder traversal to create the necessary dictionaries to map the model's file name to its download url.

June 2019 - August 2020

I served as a Microsoft AI Platform Intern. I worked with the Onnx team to train the Super Resolution model, and then convert it from its Pytorch framework into Onnx format. I used a virtual machine during the training process, which allowed me to gain hands-on experience with working with virtual environments and operating systems. I also helped reorganize the Onnx Model Zoo Github to improve readability and usability. For example, I organized the model folders based on its function, such as classification, object detection and segmentation, machine translation, etc. Moreover, I also improved the descriptions for the model by giving brief details about its accuracy, architecture, and functionalities.

Projects

Credit Smarter

Credit Smarter is a web application that helps users choose the right credit card for them. By taking to account the user's spending habits and credit score, Credit Smarter recommends the optimal credit card to maximize cashback for the user. This project was built with Angular6 as the frontend and Firebase as the backend. At the HackNC 2018, this project won the Best Financial Hack.

Diagnose Me

Diagnose Me is a web application that enables individuals to self-diagnosis diseases with confidence levels over 400 diseases by allowing them to filter and select from hundreds of common symptoms ranging from the common sicknesses such as the Flu, to the more complex issues such as mental disorders. This project was built with Angular8, TypeScript as the Frontend, and Python (Flask), MongoDB and Google/Symptoms API as the Backend. At the Pearl Hacks 2020, this project won Best Hack to Simplify an Everyday Process.

Resume

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