Drawing from a unique array of knowledge, skills, and experience — a Bachelor's degree in Neuroscience from UCLA, an AWS Machine Learning (Specialty) certification, an AWS Solutions Architect (Associate) certification, an AWS AI Practitioner certification, and a full-stack web development certificate — I build systems that solve real operational problems. My flagship project is an event-driven, serverless platform that digitizes a paper-based trucking operation end to end: drivers' handwritten tickets are scanned, run through an OCR and entity-extraction pipeline (OpenCV + Amazon Textract), validated, and turned into invoices automatically. I architected the whole system on AWS — Lambda, DynamoDB, S3, SQS, EventBridge — with a React operations dashboard on top for real-time ticket review and invoicing. It's a real production system running a live business. More recently, I've moved into the data and evaluation side of AI. I'm a research contributor on a PhD-led study examining manufactured consensus in online attention markets — whether coordinated, inauthentic comment campaigns on YouTube can fake the appearance of agreement and measurably pull in real engagement. My role is the annotation and evaluation layer that makes the study's central finding credible: I was involved in labeling the golden dataset distinguishing coordinated from organic comment clusters, writing the annotation guidelines, running inter-annotator agreement, and reviewing LLM-generated classifications surfaced through a LangChain pipeline. Solving real-world problems, staying at the forefront of emerging technologies, and building a strong network are at the core of my career priorities.…Drawing from a unique array of knowledge, skills, and experience — a Bachelor's degree in Neuroscience from UCLA, an AWS Machine Learning (Specialty) certification, an AWS Solutions Architect (Associate) certification, an AWS AI Practitioner certification, and a full-stack web development certificate — I build systems that solve real operational problems. My flagship project is an event-driven, serverless platform that digitizes a paper-based trucking operation end to end: drivers' handwritten tickets are scanned, run through an OCR and entity-extraction pipeline (OpenCV + Amazon Textract), validated, and turned into invoices automatically. I architected the whole system on AWS — Lambda, DynamoDB, S3, SQS, EventBridge — with a React operations dashboard on top for real-time ticket review and invoicing. It's a real production system running a live business. More recently, I've moved into the data and evaluation side of AI. I'm a research contributor on a PhD-led study examining manufactured consensus in online attention markets — whether coordinated, inauthentic comment campaigns on YouTube can fake the appearance of agreement and measurably pull in real engagement. My role is the annotation and evaluation layer that makes the study's central finding credible: I was involved in labeling the golden dataset distinguishing coordinated from organic comment clusters, writing the annotation guidelines, running inter-annotator agreement, and reviewing LLM-generated classifications surfaced through a LangChain pipeline. Solving real-world problems, staying at the forefront of emerging technologies, and building a strong network are at the core of my career priorities.WWWW…