I’m a Master’s student in Computer Science at Northeastern University (GPA: 3.5/4.0) with a foundation in artificial intelligence and data science. My interests lie at the intersection of applied machine learning, backend development, data pipelines, and full-stack applications, where I enjoy learning how data and models translate into reliable software.

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Previously, I interned as a Quantitative Analyst at ORIM Advisors, where I worked on Python-based financial data pipelines processing 5,000+ stocks, contributing to system optimizations that reduced trade placement latency by ~75%. I also explored causal links between news and market movements and experimented with open-source LLMs for real-world financial workflows.

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Beyond internship work, I actively explore applied projects and research-driven learning. I am a published researcher with two peer-reviewed papers in deep learning and large language models, and I have built RAG-based applications and full-stack AI platforms such as CourseAid.

 

I also maintain a growing portfolio of ML projects across NLP, time-series forecasting, and computer vision, driven by curiosity and a desire to build impactful, real-world systems.