I am currently a ML engineer at Meta working within the Business AI org generally working on monetizing LLMs.
Currently, I support Meta Business Agent.
Previously in this role, I worked on a Meta support chatbot embedded on Shopify websites and new agentic ad experiences.
And prior to that, I worked in Meta Monetization and WhatsApp.
Outside of work, I enjoy studying Vietnamese, throwing pottery, and playing couch co-op video games with friends and family.
School of Engineering and Applied Science
Master of Science in Engineering in Computer Information Science
Thesis: Empathic Conversation Models For Distressful Events
Advised by Professor Lyle Ungar
Developed empathetic LLMs using ParlAI to discuss distressful news by evaluating fine-tuning on curated empathic data, long-term memory, retrieval modules, loss functions, and varying context depths. Studied early LLMs and conversational agents (GPT-2, GPT-3, BlenderBot) across prompt design, context management, subjective human evaluation, and ML eval benchmarks for NLP.
Bachelor of Science in Engineering in Computer Science
Bachelor of Science in Economics with a concentration in Operations Management
Senior Machine Learning Engineer
- Developed new agentic e-commerece experiences on a variety of Meta surfaces including Meta ads, WhatsApp / Instagram, and a Meta-owned shopify plugin
- Led the developement of new agentic capabilities and conversation eval and improvements.
Software Engineer
- Rearchitected and improved how interactive message types are parsed and rendered on WhatsApp Android which now enables more than $1 million in daily revenue for the business messaging org
Software Engineer Intern
- Automated 29% of support tickets by implementing an intelligent root cause analysis tool written in Lua and TypeScript.
- Built a distributed trace visualizer for 1000+ Bloomberg services using Ogma to identify performance bottlenecks.
Teaching Assistant for Applied Machine Learning
- Developed theoretical and applied problem sets across core machine learning topics, including optimization, loss formulations, regularization, and deep neural networks.
- Guided capstone ML projects from conception to deployment, debugging training pipelines and advising on multimodal architectures (e.g., zero-shot vision models for fine-grained bird classification)
Teaching Assistant for Decision Processes
- Supported instruction of students in decision theory, business psychology, and behavioral economics
Chief Information Officer
- Led IT team of 8 managing operations of a $8M AUM credit union which included negotiations with software vendors, migrating core banking systems, and developing in-house fraud detection algorithms
Research Assistant
- Conducted research within the intersection of neuroscience and machine learning within the Kording lab.
- Engineered PyTorch DNN probes to decode intermediary representations of pre-trained VGG architectures, evaluating whether computer vision feature spaces exhibit human-like optical illusion susceptibility
- Presented original findings, “Shared visual illusions between humans and artificial neural networks,” at the 2019 Conference on Cognitive Computational Neuroscience
- Contributor: Nature Communications, “Efficient neural codes naturally emerge through gradient descent learning” (Acknowledged for engineering model fine-tuning pipelines and training infrastructure)
Software Engineer Intern
- Created dev-tools for WDL and Cromwell for high-throughput DNA sequencing, reducing debug-time for biologists which in turn accelerates fundamental research in genomics and cancer
