About Me
I am a senior undergraduate at the Massachusetts Institute of Technology (MIT), pursuing a degree in Computer Science with a specialization in Artificial Intelligence & Decision Making. With hands-on experience in AI engineering, software development, and data science, I am driven to use technical depth and collaborative problem-solving to build impactful real-world systems.
As an AI Engineer Intern at IBM, I co-created and deployed solutions on the IBM watsonx platform, working with generative AI, agentic systems, large language models, and multi-agent orchestration frameworks. Partnering with clients and cross‑functional teams, I translated complex business needs into scalable technical architectures that delivered measurable business value.
Previously, at the Foundation for Resilient Societies, I built an end-to-end data pipeline for U.S. power plant analytics and developed machine learning models to forecast outage risk—directly supporting decision-making for critical energy infrastructure.
At CDM Smith, I applied electrical engineering fundamentals and automation by building Python‑based regression models to accelerate solar array design and standardize workflows across engineering projects.
At Quill AI, I gained full-stack experience by developing user features using SvelteKit and improving the reliability of backend systems powered by OpenAI.
Through MIT’s Undergraduate Research Opportunities Program (UROP) at the Device Realization Design Lab and Tangible Media Group, I led and contributed to advanced machine learning research, including 3D reconstruction pipelines in PyTorch and a retrieval‑augmented generation (RAG) question‑answering platform. These projects strengthened my ability to take ML systems from research to deployment.
In addition, my leadership as President of the MIT Bitcoin Club demonstrates my ability to manage teams, coordinate large‑scale initiatives, and drive projects from concept to execution.
TECHNICAL SKILLS
My technical toolkit includes a diverse range of skills. I quickly learn new systems, work effectively both independently and in agile teams, and communicate technical outcomes with clarity.
Programming Languages
- Python
- Java
- TypeScript / JavaScript
- SQL
- Bash
Machine Learning & AI
- Large Language Models (LLMs)
- Generative AI & Agentic AI Systems
- Multi‑Agent Orchestration
- Retrieval‑Augmented Generation (RAG)
- Supervised & Unsupervised Learning
- Regression & Forecasting Models
Frameworks & Libraries
- PyTorch
- TensorFlow
- NumPy, Pandas, scikit‑learn
- LangChain / LLM tooling (conceptual & applied)
Data Engineering & Analytics
- End‑to‑End Data Pipelines
- Data Cleaning, Feature Engineering
- Time‑Series Analysis
- Risk Modeling & Forecasting
- SQL‑Based Analytics
Software Engineering & Web Development
- SvelteKit
- RESTful APIs
- Backend Integration with AI Services
- Performance Optimization
- Modular & Scalable System Design
Cloud & DevOps
- Docker
- AWS (EC2, S3, deployment workflows)
- Model Deployment & Inference Pipelines
- CI/CD Concepts
Research & Technical Tools
- Git & Version Control
- Reproducible ML Workflows
- Technical Documentation
Collaboration & Methodologies
- Agile & Cross‑Functional Team Environments
- Client‑Facing Technical Communication
- Translating Business Requirements into Technical Solutions