Machine Learning Engineer

I am a machine learning researcher and engineer with a strong background in computer science, electrical engineering, and large-scale recommendation systems. My work combines academic research with industry experience, and I focus on building practical ML systems that solve real-world problems.

I am currently pursuing a Ph.D. in Computer Science and Electrical Engineering with a machine learning focus at the University of Maryland, Baltimore County. My research spans dictionary learning, multimodal fusion, graph neural networks, retrieval-augmented generation, and optimization methods for intelligent systems.

I have also gained industry experience at Meta Platforms, where I worked on large-scale creator recommendation and social graph representation learning. In that role, I built machine learning pipelines, trained embeddings on massive graph data, wrote high-quality SQL for very large datasets, and contributed to offline and online A/B testing.

My recent independent research includes agentic multi-hop RAG systems, multimodal product retrieval, and attention-based sequential models for e-commerce CTR ranking. These projects strengthened my skills in retrieval, ranking, deep learning, feature engineering, and evaluation for recommendation and search applications.

Earlier in my graduate research, I worked on blockchain systems with confidentiality and smart contract support, improving throughput significantly through multithreading. I have also contributed to several publications in biomedical imaging, signal processing, and distributed systems.

Overall, I am interested in roles in machine learning, recommendation systems, graph representation learning, and related applied research areas. I enjoy combining theory, implementation, and experimentation to build scalable and effective intelligent systems.