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CS + EE @ UVA
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About Me

Software Engineer & Machine Learning Researcher

I am a student studying Computer Science and Electrical Engineering at the University of Virginia. I'm extremely passionate about solving challenging problems in the world of machine learning and MLOps. I thrive working in fast-paced competitive environments. Outside of academics, I enjoy playing basketball and chess.

Resume

Work Experience

University of Virginia, Undergraduate Teaching Assistant

January 2023 - December 2023

  • Reinforcing concepts for 500+ students in Data Structures, Algorithms, Computer Architecture, and Operating Systems

University of Virginia, Research Intern

August 2023 - May 2024

  • Working on efficient training and evaluation frameworks for LLMs

Apple, Software Engineer Intern

May 2023 - August 2023

  • Simplified app launch process for billions of devices running iOS and iPadOS by decoupling system components to provide higher level of synchronization and reducing overall system complexity using Objective-C
  • Optimized application display and interaction by managing the async rendering state of active applications
  • Proved the capability of new infrastructure by developing new procedures not possible before and displaying them

Acture, Research Scientist

December 2021 - August 2023

  • Enabled users to enhance image quality set by user-controlled dynamic scale factor by generating image training pairs by applying programmatic multi-stage degradation to images
  • Decreased inference cost and latency by utilizing tensor algebra to decompose Convolutional blocks to low rank using TensorLy. Used Variational Bayes Matrix Factorization to find optimal rank for each block and implement iterative decomposition/fine-tuning strategy to reduce performance degradation
  • Further decreased inference cost and allow model to run in resource-constrained environments by Quantizing the model to 8-bit while achieving similar performance to full model

Synergy, Software Engineer Intern

May 2022 - September 2022

  • Achieved over 80% code coverage on the app and microservice by writing comprehensive unit tests using Jest
  • Resolved 35 security concerns with software dependencies by refactoring code to reduce vulnerabilities
  • Collaborated with other engineers in a scrum team to reduce vulnerabilities by performing code reviews

University of Virginia, Research Intern

May 2022 - September 2022

  • Conducted comprehensive experiments to validate the robustness of deep reinforcement learning algorithms against adversarial attacks, contributing to a 15% improvement in system resiliency under simulated attack scenarios
  • Created an adaptive weighting mechanism to dynamically adjust state importance during reinforcement learning training, increasing model stability when subjected to adversarial inputs
  • Delivered 7% better efficacy in quantifying importance of the current state, resulting in a lower rate of adversarial attack success

Education

University of Virginia, BSCS, Minor in EE

August 2020 - May 2024

  • Clubs/Activities: Google Developers Student Club, Machine Learning CLub, Intramural Basketball
  • Relevant Courses: Data Mining(Graduate), Software Engineering, Theory of Computation, Data Structures and Algorithms, Machine Learning(Graduate), Computer Architecture(Graduate), Optimization(Graduate), Natural Language Processing(Graduate), Signal Processing, Computer Networks, Optimization

Skills

Python

Python

Python

C

Python

C++

Python

Java

Python

HTML

Python

CSS

Python

Javascript

Python

SQL/NoSQL

Python

Bash

Python

Objective-C

Python

Rust

Python

R

Python

React

Python

Node.js

Python

Redux

Python

Express.js

Python

Flask

Python

Django

Python

jQuery

Python

Bootstrap

Python

Tensorflow

Python

Pytorch

Python

Keras

Python

NumPy

Python

pandas

Python

ROS

Python

Jest

Python

CUDA

Python

Apache Kafka

Python

Apache Flink

Python

ksqldb

Python

git

Python

GraphQL

Python

AWS

Python

GCP

Python

Kubernetes

Python

Docker

Python

MLFlow

Python

Apache Airflow

Python

TensorLy

Python

Transformers

Python

JAX

Python

Flax

Projects

Continually Learning Real-Time ML Infrastructure

MAY 2023 - OCT 2023

The convergence of real-time infrastructure and machine learning holds great promise, and this project explores the pivotal role GraphQL plays at this intersection. Delve deep into the intricacies of setting up a robust real-time infrastructure using GraphQL, Apache Kafka, Apache Flink, and ksqlDB and how it complements continual learning in real-time ML systems by allowing us to test in production by leveraging federated GraphQL.

Check out my talk on this at GraphQL Summit 2023.

Project 2 Image

Semi-Online Matrix Factorization RecSys Library

DEC 2023 - AUG 2023

  • Semi-online matrix factorization recsys library that allows for semi-online updates allowing to adapt in real-time as new user-item interactions occur in near real-time
View on GitHub
Project 3 Image

RISC-V CPU

DEC 2022 - MAY 2023

  • Designed and implemented RISC-V CPU using VHDL
  • Extensive testing done with VHDL Testbenches, Assembly, and Modelsim
View on GitHub