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## Ananya Vasireddy

Hi! My name is Ananya, and I'm a senior majoring computer science and minoring in South Asian Studies here at Stanford. Here's a bit about my experience and past work!

## Work Experience

**SDE Intern, Amazon Web Services** (June 2024 - September 2024)

- Developed proof of concept to improve Elastic Container Registry’s lifecycle policy feature (Java backend development) that cut costs by 70%
- Worked with AWS Cloud Development Kit, used AWS services like EventBridge and IAM, and wrote comprehensive tests and documentation

**Healthcare Systems Engineering Intern, SAS Institute** (June 2023 - September 2023)

- Assisted in the creation of company demos via SAS Visual Analytics
- Cleansed, and performed statistical analysis on public health data using SAS programming
- Analyzed Twitter data using SAS Text Analytics (natural language processing platform)

**Product Management Intern, Ginkgo Bioworks** (July 2022 - August 2022)

- Led analysis and visualization of company-wide ticketing system data via Atlassian Analytics
- Conducted gap-analysis of company rework tracking process and interviewed staff on findings

## Projects

**Dress-UP: A Deep Unique, Personalized Fashion Recommender** (April 2024 - June 2024)

Relevant Paper: [Dress-UP: A Deep Unique, Personalized Fashion Recommender](https://cs231n.stanford.edu/2024/papers/dress-up-a-deep-unique-personalized-fashion-recommender.pdf)

- Final group project for CS 231N at Stanford University
- Extended OpenAI’s multimodal CLIP model to build a fashion recommender
- Focused on augmenting our outputs through the use of generative image models (used GANs and StabilityAI’s Stable Diffusion)

**Pediatric Apple Watch Study** (January 2023 - March 2023)

Relevant Paper: [Utility of smart watches for identifying arrhythmias in children](https://pmc.ncbi.nlm.nih.gov/articles/PMC10719318/#:~:text=Conclusions,traditional%20ambulatory%20monitors%20did%20not.)

- Built an IOS app to collect ECG data from Apple Healthkit with a team as a part of CS 342 (supported the Pediatric Apple Watch Study at Stanford School of Medicine)
- Particularly focused on UI/UX design and developed an FAQ page in Swift

**Random Forest Regression of Markov Chains for Accessible Music Generation** (June 2020 - October 2020)

Relevant Paper: [Random Forest Regression of Markov Chains for Accessible Music Generation](https://ieeexplore.ieee.org/document/9668909)

- Developed a generative music model using Markov chains and random forest regression with a team as part of the 2020 New Jersey Governor's School for Engineering & Technology
- Specifically worked on a post-processing Python script to prepare musically sound outputs