Below you'll find highlights from my professional journey, showcasing the impact I've made across various technical domains. For more details about my work and projects, please visit my LinkedIn and GitHub.

Meta - Production Engineering Intern

At Meta, I made significant contributions to the Database as a Service team within Enterprise Engineering. My work focused on improving the operational efficiency of MySQL Clusterset infrastructure that powers critical internal operations. I developed comprehensive dashboards and automated alerting systems that reduced diagnosis time for MySQL Clusterset issues by 70%, enabling faster incident response and minimizing downtime. Additionally, I engineered a CLI tool in Rust that provides oncall engineers with instant visibility into cluster health, allowing them to quickly identify and address unhealthy clusters. This tooling has become essential for maintaining the high availability standards required for Meta's global-scale database infrastructure.

Hangry Indonesia - Backend Engineering Intern

During my time at Hangry Indonesia, I drove substantial improvements in system performance and cost efficiency. I completely rewrote the notification system in Golang, optimizing the delivery of push notifications (FCM), SMS, and email communications, which resulted in a 90% improvement in processing efficiency. Recognizing opportunities for architectural enhancement, I replaced the existing RabbitMQ infrastructure with a Redis Streams-based message queue system, achieving a 70% reduction in cloud hosting costs while maintaining system reliability. I also automated the supply order verification workflow, eliminating manual processes and reducing the finance team's workload by 75%, allowing them to focus on higher-value activities.

Open Source Contributions

I actively contribute to major open source projects in the Kubernetes ecosystem. My current work includes contributing to inference-perf within Kubernetes and KServe, where I focus on performance optimization and scalability improvements for ML model serving infrastructure. Additionally, I contributed to MIT App Inventor through Google Summer of Code, where I integrated the IDraggable interface into Blockly's multi-select plugin, eliminating dependency on monkey patches and ensuring compatibility across multiple existing plugins. I provided comprehensive documentation to support future development and community adoption.

I'm pursuing a Bachelor of Science in Computer Science & Engineering at Bucknell University, complemented by a minor in Mathematics. My academic journey provides me with deep technical expertise in software engineering, algorithms, systems design, and machine learning, while my mathematical foundation strengthens my ability to approach complex problems analytically. Beyond the classroom, I apply these skills through internships, open source contributions, and entrepreneurial ventures, bridging the gap between theoretical knowledge and real-world impact.

My research experience spans machine learning model development and ML infrastructure. I've conducted in-depth research on convolutional neural networks (CNNs), where I developed models using PyTorch with a focus on understanding training dynamics through the Adam optimization algorithm. By meticulously tracking parameters and gradients throughout the training process, I gained valuable insights into model behavior. I even recreated a parallel model implementation in Excel to demystify the mathematical foundations of neural network training. I've also explored time-series forecasting using deep echo state networks for stock price prediction, applying advanced ML techniques to financial data analysis. Currently, my focus has shifted toward ML infrastructure and model serving, where I contribute to open source projects that make production-grade machine learning systems more efficient and accessible.

Programming Languages: Proficient in Python, Go (Golang), Rust, JavaScript/TypeScript, Java, and C/C++. Experienced in selecting the right language for each use case, from high-performance systems programming to rapid web development.

ML & Data Science: Hands-on experience with PyTorch, TensorFlow, scikit-learn, and Pandas for building and deploying machine learning models. Strong foundation in CNNs, time-series forecasting, and sentiment analysis.

Infrastructure & DevOps: Extensive experience with Kubernetes, Docker, Redis, RabbitMQ, and MySQL. Skilled in building scalable distributed systems and optimizing cloud infrastructure for cost and performance.

Web Development: Full-stack capabilities with the MERN stack (MongoDB, Express, React, Node.js), Flask, and Java Spring Boot. Proven ability to build responsive, production-ready applications.

Tools & Platforms: Git/GitHub, CI/CD pipelines, cloud platforms, monitoring and observability tools, and various development environments tailored to different technology stacks.