I studied computer science and cognitive science at Rutgers university, at which I was a presidential scholar (top 2% of class). After interning at Amazon, I developed an interest in NLP and learned how AI can be used to solve problems in healthcare. I currently work on the Amazon Translate team where I work with research scientists to develop and host scalable neural machine translation (NMT) systems for 70+ languages across 16 regions worldwide. .
During my free time, I enjoy volunteering in settings where I can apply my technical knowledge to help others. Over the past few years, I have learned about various industries by volunteering at research labs, political campaigns and data-centric nonprofits, such as DataKind. I am passionate about using machine learning and data science to solve social problems. In particular, I hope to address issue related to the lack of interpretability in machine learning models and how this leads to the propagation of unseen biases.
Aug 2020 - Present: I'm excited to announce that I will be joining the Amazon Translate team! The Amazon Translate is a neural machine translation service that processes large amounts of multilingual data and allows you to use real-time and batch translation via a simple API call.
Summer of 2019: I returned to Amazon but I will be joining the Comprehend Medical team. My new team works on using NLP to parse patient medical records and drastically expedite medical research. In doing so, we automate the extraction of information and will empower researchers to collect and analyze data at scale, and mitigate the computational bottleneck of research.
Summer of 2018: I worked in Seattle as a software engineering intern within Amazon's Aurora Database team. As a member of one of the largest cloud database business, I learned how systems are built to manage a huge numbers of databases and vast amounts of storage across multiple datacenters worldwide. To achieve this level of reliability, my team builds control and monitoring systems that automatically detect and handle many types of failures within seconds, and data replication options that accommodate various geographical distribution and disaster recovery objectives. I built a tool using AWS ElasticSearch to parse unstructured log data and allow developers to search for errors using ad hoc queries.
Shared Autonomous Vehicles Simulation: a simulation of Shared Autonomous Vehicle (SAV) Deployment. I analyzed the efficacy of using electric vehicles in NY as well as locating the optimal charging stations based on historical taxi data. I mainly worked on creating the simulation as well as the data analysis for this project.
Shor's Quantum Integer Factorization Algorithm: Many modern encryption algorithms (including RSA) rely on the assumption that factoring large integers is computationally intractable. This is true to classical computers, but the Shor's algorithm shows that factoring integers is efficient on an ideal quantum computer, so it may be feasible to defeat RSA by constructing a large quantum computer. I have implemented a Shor's algorithm using IBM's Qiskit and have ran experiments on small quantum computers.
Knight Market: As a freelance android app developer, I helped create a mobile e-commerce app for students to buy, sell, or trade items within the Rutgers area.
#Farewell Obama: During Obama's farewell address, I scraped tweets from Twitter and conducted sentiment analysis (using IBM Alchemy) and network analysis of all tweets with the keyword #Obama