About Me
Student at Johns Hopkins University pursuing degrees in Computer Science, Physics, and Applied Mathematics & Statistics. My interests lies at the intersection of quantum computing, machine learning, and software engineering.
With experience at Amazon, Sierra Nevada Corporation, and the JHU Quantum Computing Theory Group, I bring a unique combination of technical skills and research experience to challenging problems.
Technical Skills
- Programming Language: C++, C, Java, Python, SQL, TypeScript/JavaScript
- Full-Stack Development: Flask, React, Django, Node.js, Next.js, HTML5, CSS3
- ML/AI: TensorFlow, PyTorch, scikit-learn
- Quantum Computing: Qiskit, Cirq, Quantum Algorithms
- Tools: Git, Docker, Unix/Linux, MATLAB, AutoCAD, Ansible
Education
Johns Hopkins University
B.S. in Physics, Computer Science, and Applied Mathematics & Statistics
- Relevant Coursework: Operating Systems, Computer Networks, Data Structures and Algorithms, Deep Learning, Probability, Statistics, Quantum Mechanics 1 and 2
- Vice President, Alpha Epsilon Pi Fraternity
- Member, Baila: Latin Dance Team
Work Experience
Software Development Engineer Intern
Amazon | Summer 2025
- Deployed a feature on the Amazon Store for an underutilized region of the shopping flow that delivered personalized product recommendations to millions of users with a strategy focused on surfacing products aligned with user intent and past behavior
- Built front-end components using TypeScript and AWS frameworks (Lambda, EC2, etc.) to deliver a responsive experience
- Architected backend services in Java to support scalable delivery of contextual recs, leveraging internal ML APIs to optimize for latency
- Gained experience in large-scale distributed systems, service-oriented architecture, AB testing, AWS, and production environments
Software Engineer Intern
Sierra Nevada Corporation | Summer 2024
- Spearheaded the design and implementation of nanosecond-scale resolution and accuracy of timestamp in software-defined radio RF communication to solve critical synchronization issues, enabling accurate message ordering in defense signal chains
- Achieved 88ns resolution using FPGA-driven timing logic and C/C++ backend integration via the Sidekiq SDK framework, significantly outperforming previous latency bounds and creating a roadmap to further improve resolution
- Led backend development for an internal RF testing suite, integrating Django and FastAPI, reducing testing from hours to minutes
- Deployed containerized services using Azure, Docker, and CI/CD pipelines to operational environments, ensuring reliability
Machine Learning & Data Analysis Researcher
JHU Quantum Computing Theory Group | Summer 2023 - Summer 2024
- Employed TensorFlow through Python in a deep learning model for the characterization and solution control of quantum systems in real time to tackle the computational and mathematical challenge of stabilizing noisy quantum systems
- Processed and analyzed training and testing data using NumPy and SciPy to create an optimized dataset for training deep learning models that would be used by the team in their goal to utilize deep learning in quantum noise error correction
- Interpreted foundations of quantum mechanics and translated them into testable code to examine noise characterization
- Acquired expertise in various quantum programming languages and data analysis tools, including Qiskit, Cirq, and SciPy
Mechanical Engineer Intern
U.S. Army Corps of Engineers | Winter 2022 - Fall 2022
- Designed mechanical systems for biochemistry lab using AutoCAD
- Created HVAC and exhaust system for six-building complex
- Contributed to project documentation ensuring federal compliance
Projects
PotLock - Crypto-based treasury for groups
Solana, Anchor, Rust, React, TypeScript, Convex, Google Gemini API, Phantom, JWT, OpenID Connect, Mastra AI
- Built a Solana-based group treasury dApp end-to-end at a hackathon, implementing a 669-line Anchor (Rust) smart contract with a doubly-linked on-chain contract version history, k-of-n multi-sig approval logic, and four proposal types (spending, add member, amend contract, switch contract) — enabling trustless fund management for groups without a single point of control
- Designed a full-stack governance workflow using React 19, TypeScript, Convex (real-time off-chain DB), and the Google Gemini API to convert plain-language group rules into structured contract JSON, validate proposed transactions against active contracts, and gate on-chain SOL transfers behind Convex-tracked member votes and approval thresholds
- Implemented wallet-signature-based authentication using Solana's @solana/wallet-adapter-react with a nonce challenge-response flow and JWT session tokens, integrating Phantom wallet sign-in, OpenID Connect endpoints, and a Mastra AI validation pipeline for optional URL verification on transaction proposals
AccessInvest - Stock Outlining Assistant
MongoDB, React, Node.js, Google Gemini API
- Built an AI-driven, full-stack web application that provides users with personalized portfolio insights and suggestions on stocks they own by giving daily updates based on financial filings, news, and trends in the stock market, awarded finalist at MorganHacks (4 of 96)
- Aggregated and processed financial data from SEC filings (10-K, 10-Q, 8-K, etc.), real-time news sources, and multiple financial APIs
- Integrated Google's Gemini API to perform sentiment analysis and understanding on complex financial documents
- Utilized a React front end alongside a Node.js backend hosted on MongoDB Atlas for scalable data storage and a seamless UX
Smart-Alert Hackathon App
Flask, Google Maps API, Twilio
- Designed an application that improves emergency response communications between emergency responders, business owners, and everyday people through a UX that allows users to access lists of emergency contacts based on location
- Instrumented Google Maps and Twilio messaging APIs, along with Flask, to produce an admin page where business and landowners can draw their geolocation and create custom instructions to be carried out when an emergency is detected by a user
- Prioritized accessibility and real-time latency in system architecture, simulating mass-notification scenarios to test throughput
Skin Cancer Detection ML Model
Python, TensorFlow, scikit-learn
- Built and validated a computer vision pipeline to differentiate between benign and malignant skin moles in a dataset of photos
- Utilized Python, TensorFlow, and scikit-learn for data preprocessing, model development, and performance evaluation
- Employed computer vision techniques (CNNs) to build a robust classification model to acquire a 79.8% accurate detection rate
Navy Federal Credit Union Co-op
UX Design
- Redesigned customer experience for new credit union members
- Iteratively refined UI based on user feedback
- Focused on accessibility and visual consistency
Get In Touch
Contact and Socials
Seattle, Washington