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Projects Directory

Innovation & Research

I love building cool stuff. Stuff that works, stuff that is fun to build, and stuff that is useful. Software development for me is all about that.
My goal is to continue finding peace in what I do.

Work Experience

Campus Partner

Perplexity AI Aug 2025 - Present

Led campus-wide integration of Perplexity's AI solutions, utilizing REST APIs, analytics dashboards, and cross-platform software for event management.

AI Solutions REST APIs Analytics

Software Developer Intern

Investopedia Inc. | Dotdash Meredith Sep 2023 - May 2025

Designed and implemented key features for web applications using Nuxt.js and Vue.js, enhancing user experience and functionality, while leveraging AWS, Jenkins, and Docker to ensure seamless deployment and scalability in a cloud-based environment.

  • Architected and containerized a FastAPI microservice using Docker, orchestrated with Kubernetes (EKS), and deployed on AWS for web widgets
  • Engineered robust Playwright frameworks utilizing TypeScript to transform team operations
  • Enhanced test coverage by ~40% by integrating Cypress and Selenium for additional test scenarios
Nuxt.js Vue.js AWS Docker

Data Scientist Intern

Ascenta Management Consulting May 2022 - Sep 2022

Applied data science techniques to solve business problems, developed predictive models, and created data visualizations for stakeholder presentations.

Data Science Predictive Modeling Visualization

Streaming Deep Reinforcement Learning

Developing novel integration of Real-Time Recurrent Learning (RTRL) with streaming DRL algorithms to address partial observability in continuous data streams. Optimizing ObGD adaptive optimization for online credit assignment in POMDP environments, improving sample efficiency by 2.1× in MuJoCo benchmarks.

Overview

This research project addresses one of the fundamental challenges in reinforcement learning: handling partial observability in continuous data streams. By integrating Real-Time Recurrent Learning (RTRL) with streaming Deep Reinforcement Learning algorithms, we enable agents to maintain and update memory of past observations in real-time without requiring full episode replay.

Key Innovations

  • RTRL Integration: Implemented online gradient computation that eliminates the need for backpropagation through time, enabling true streaming learning
  • ObGD Optimization: Developed adaptive optimization techniques specifically designed for online credit assignment in Partially Observable Markov Decision Process (POMDP) environments
  • Sample Efficiency: Achieved 2.1× improvement in sample efficiency on MuJoCo continuous control benchmarks compared to baseline streaming algorithms
  • Memory Management: Designed efficient memory architectures that balance computational cost with information retention

Technical Stack

Languages & Frameworks: Python, PyTorch, JAX, NumPy

RL Libraries: Stable-Baselines3, RLlib, Gymnasium

Benchmarks: MuJoCo, Atari, DeepMind Control Suite

Impact & Results

The research demonstrates significant improvements in both computational efficiency and learning performance. The streaming approach reduces memory requirements by 60% while maintaining comparable or superior performance to traditional experience replay methods. This work has implications for real-world applications where agents must learn continuously from streaming data, such as robotics, autonomous systems, and adaptive control.

AI Research Python

DataMod

A database management program that allows users to view, create, delete, and modify databases. Applied Python and MongoDB and used data manipulation statements in SQL to implement database calls.

Overview

DataMod is a comprehensive database management tool that provides a unified interface for interacting with both SQL and NoSQL databases. The application bridges the gap between traditional relational databases and modern document-oriented storage, offering developers and database administrators a single platform for all their data management needs.

Core Features

  • Multi-Database Support: Seamlessly work with MongoDB (NoSQL) and SQL databases through a unified interface
  • CRUD Operations: Complete Create, Read, Update, and Delete functionality with intuitive UI controls
  • Query Builder: Visual query construction tool that generates optimized SQL and MongoDB queries
  • Schema Management: Create and modify database schemas with validation and constraint support
  • Data Visualization: Built-in tools to visualize data relationships and query results
  • Import/Export: Support for CSV, JSON, and XML data formats for easy data migration

Technical Implementation

Backend: Python with PyMongo for MongoDB connections and SQLAlchemy for SQL database abstraction

Database Support: MongoDB, PostgreSQL, MySQL, SQLite

Architecture: MVC pattern with modular design for easy extension to additional database types

Security: Parameterized queries to prevent SQL injection, connection pooling for performance

Use Cases

DataMod is ideal for development teams working with hybrid database architectures, database administrators managing multiple database types, and students learning database concepts. The tool simplifies complex database operations while maintaining the power and flexibility needed for advanced use cases.

Python MongoDB SQL

CitiWatch

A full-stack web application using Python, Flask, and React to detect weapon risks with YOLOv5 image recognition and display them on a dashboard for law enforcement. Integrated Firebase Real-time Database for secure user authentication, threat history storage, and a global risk map. Won 1st place for the AI track at GovTech hackathon.

Overview

CitiWatch is an AI-powered public safety platform designed to assist law enforcement agencies in identifying and responding to potential weapon threats in real-time. The system leverages state-of-the-art computer vision technology to analyze video feeds and images, providing immediate alerts and comprehensive threat assessment tools through an intuitive web dashboard.

Key Features

  • Real-Time Weapon Detection: YOLOv5-based image recognition system trained on extensive weapon datasets, achieving 94% accuracy in threat identification
  • Interactive Dashboard: React-based frontend displaying live threat alerts, historical incident data, and risk analytics
  • Global Risk Map: Geographic visualization of threat patterns using Mapbox GL, enabling strategic resource allocation
  • Threat History: Comprehensive logging system storing incident details, timestamps, and associated metadata for investigation and analysis
  • Multi-User Authentication: Role-based access control with Firebase Authentication, supporting different permission levels for various law enforcement roles
  • Alert System: Configurable notification system with SMS and email integration for immediate threat response

Technical Architecture

Frontend: React.js, Redux for state management, Material-UI components, Mapbox GL for mapping

Backend: Flask (Python), RESTful API design, WebSocket for real-time updates

AI/ML: YOLOv5 (PyTorch), OpenCV for image preprocessing, custom training pipeline

Database: Firebase Realtime Database for live data, Cloud Firestore for historical records

Deployment: Docker containerization, Google Cloud Platform hosting

Achievement

★ 1st Place - AI Track, GovTech Hackathon

CitiWatch was recognized for its innovative approach to public safety, practical implementation, and potential for real-world impact. The judges praised the system's accuracy, user-friendly interface, and comprehensive feature set that addresses genuine law enforcement needs.

AI Full-Stack Python

QRchive

Led a team of 6 to build an Android app as an online gaming platform where users can scan QR codes and compete with other players. Set up CI/CD pipeline with GitHub Actions for automated testing and deployment.

Overview

QRchive is a location-based mobile gaming platform that transforms the real world into a competitive playground. Players scan QR codes placed throughout their city to collect points, unlock achievements, and compete on global leaderboards. The app combines elements of scavenger hunts, geocaching, and social gaming to create an engaging outdoor experience.

Core Features

  • QR Code Scanning: Fast and reliable QR code detection using Android Camera2 API and ZXing library
  • Point System: Dynamic scoring algorithm based on QR code rarity, location difficulty, and time-based challenges
  • Leaderboards: Real-time global and local rankings with weekly competitions and seasonal events
  • Social Features: Friend system, team challenges, and in-app chat for coordinating hunts
  • Achievement System: 50+ unlockable achievements with unique badges and rewards
  • Map Integration: Google Maps integration showing nearby QR codes and player locations
  • Profile Customization: Customizable avatars, themes, and player statistics

Technical Implementation

Platform: Native Android (Java), minimum SDK 24 (Android 7.0)

Backend: Firebase Realtime Database, Cloud Firestore, Firebase Cloud Functions

Authentication: Firebase Authentication with Google Sign-In and email/password support

Storage: Firebase Cloud Storage for user-generated content and profile images

Maps: Google Maps Android API with custom markers and clustering

Testing: JUnit, Espresso for UI testing, Robolectric for unit tests

Team Leadership & DevOps

As team lead, I coordinated a group of 6 developers using Agile methodologies with 2-week sprints. Implemented comprehensive CI/CD pipeline using GitHub Actions for automated testing, code quality checks, and deployment to Google Play internal testing track. Established code review processes, documentation standards, and conducted regular knowledge-sharing sessions to ensure consistent code quality across the team.

Java Android Firebase