AI Engineer
Building production AI systems for security, data infrastructure, and intelligent applications.
MSE in Data Science at the University of Pennsylvania, with enterprise AI and engineering experience across Huawei, ByteDance, and Sanofi.
Selected Impact
Instead of a public portfolio dump, the site highlights production-facing work through clear outcomes and public-safe descriptions.
Enterprise data security workflows
As the responsible lead, designed and shipped agent capabilities around intent recognition, context engineering, tool calling, and RAG for production security scenarios.
Storage-side ransomware defense
As the responsible lead, combined classical machine learning, deep learning, and custom feature extraction for real storage system environments.
A benchmark project for evaluating LLM capabilities
Designed and maintained a public-facing evaluation project to make model behavior easier to inspect across practical tasks and key capability dimensions.
Featured Experiment
I did not stop at a single score. I kept pushing on a GPT-5.4 teacher with best-of-k distillation, strict prompting, and repeat-3 evaluation to separate real signal from noise.
Includes the design, pipeline, latest numbers, and how my judgment changed.
Read the experiment case studyExperience
The arc moves from large-scale product engineering into enterprise AI, security systems, and production-grade machine learning.
Sanofi
AI Engineer
2025.10 - Present
Huawei
Algorithm Engineer / Algorithm Expert
2022.01 - 2025.09
ByteDance
Software Development Engineer
2020 - 2022
Expertise
The profile is strongest where model capability, system design, and production execution need to work as one loop.
Architecture design, context engineering, tool calling, protocol integration, and reliability tuning for enterprise agent systems.
Applied AI for ransomware detection, data protection, agent safety, and secure deployment in production environments.
From classical machine learning to graph-based methods and deep learning systems designed around concrete business and infrastructure problems.
Cross-stack execution across system-level engineering, product development, experimentation, and deployment workflows.
Education
The academic path reinforces the engineering path: rigorous data science training paired with enterprise delivery across AI, security, and infrastructure.
Primary Credential
MSE in Data Science
GPA 3.7 / 4.0, top 10%, with coursework spanning machine learning, big data analytics, databases, systems, and algorithms.
Patents
These patents highlight sustained work across AI security, data protection, storage systems, and agent engineering in real enterprise environments.
First Inventor
First Inventor
First Inventor
Second Inventor
Second Inventor
Second Inventor
Contact
Open to AI engineering opportunities, applied AI collaboration, and technical consulting.