I'm currently a student at the University of Pennsylvania pursuing a BSE and MSE in Computer Science with a concentration in AI. My interests are mainly in systems. This includes ML for systems, AI systems, database systems, and distributed systems in general. I am also a D1 athlete on the varsity squash team. In my free time, I like to do photography.
Relevant Coursework
- CIS 1600: Discrete Mathematics
- CIS 1210: Data Structures and Algorithms
- CIS 3200: Data Structures and Algorithms 2
- CIS 2400: Computer Systems
- CIS 2400: Computer Systems
- CIS 5200: Machine Learning
- CIS 5300: Natural Language Processing
- CIS 5480: Operating Systems
- CIS 5530: Networked Systems
- CIS 5510: Computer Security
- CIS 5710: Computer Organization and Design
- CIS 8000: AI Systems
- ESE 5140: Graph Neural Networks
- ESE 6450: Deep Generative Models
Work Experience
May 2025-August 2025
Santa Clara, CA
- As an intern on Tim Kraska's Learned Systems Group, introduced the first concurrent query model and scheduling algorithm in Amazon Redshift based off the paper: Improving DBMS Scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries.
- Implemented BiLSTM neural networks in Redshift to create its first concurrent query model. Applied the model to improve execution time predictions by ~2x over previous predictors.
- Integrated a novel query scheduling algorithm that takes advantage of the concurrency model. Decreased tail query latency by ~50% when compared to the previous scheduling algorithm as evaluated on the TPCDS dataset.
May 2024-May 2025
Philadelphia, PA
- Developed generative image watermarking architectures based off CNNs and diffusion models to synthesize images with hidden and robustly embedded watermarks.
- Implemented generative watermarking models such as Stable Signature and Recipe for Watermarking Diffusion Models using a new constrained objective with dual learning as described in PACC Learning.
- Conducted in-depth analysis between watermarking models with different architectures varying loss constraints, dual learning rates, and other hyper parameters.
May 2024-Present
Philadelphia, PA
- Independently devised comprehensive solutions for machine learning labs, including Variational Autoencoders, Variational Diffusion Models, Actor-Critic Reinforcement Learning, and Large Language Models.
- Authored in-depth explanations accompanying solutions, ensuring clarity and rigor by integrating detailed mathematical proofs. Some explanations were published in notes.
July 2022-May 2023
Madison, WI
- Managed cloud infrastructure, CI/CD pipelines, and DevOps services for datachat.ai, optimizing deployment processes and enhancing system reliability.
- Designed a novel microservice architecture to handle machine learning tasks concurrently, improving system scalability and reducing request dead locks.
- Transferred our entire AWS Kubernetes deployment from UI-based to IAC (Infrastructure as Code) using Terraform, while automating infrastructure updates through CircleCI, Docker Compose, and AWS.
Projects
Generative Diffusion Model for Video
Jun 2024-Aug 2024
- In collaboration with researchers from Google Deepmind and ASU, helped develop a generative diffusion model for video using the DDPM algorithm, implementing it from scratch using Hugging Face UNets.
- Automated data collection in Unreal Engine using the Unreal Python API, generating 2D projections, depth maps, and normal maps to create a comprehensive dataset for model training.
Aug 2021-May 2022
- Created Rift, an iOS application built with SwiftUI, offering students an enhanced in-app experience for managing their academic journey.
- By reverse-engineering the Infinite Campus API, Rift provides students with seamless access to vital information such as grades, homework assignments, and messages, all within a user-friendly interface.
- Currently being maintained for over 15,000 middle and high school students.
Research
Jul 2021-Dec 2021
- Co-authored a research paper focused on leveraging deep learning computer vision techniques for predicting poverty levels in various global regions using overhead satellite imagery.
- Addressed the challenge of limited reliable economic data in developing areas, offering a more cost-effective alternative to traditional surveys.
- Explored the impact of data quantity and augmentation on network performance in order to improve the model.
Awards
Nov 2019
- Competed in USA Computing Olympiad coding contests and reached the gold division.