Case Summary:  

North America Immigration Law Group (Chen Immigration Law Associates) is pleased to share an I-140 National Interest Waiver (NIW) approval for the client, an artificial intelligence specialist with an M.S. in computer science. The petition presented a clear narrative: as large language models become widely deployed, the hardest engineering problem is not simply training bigger models, but making training and inference efficient, scalable, and reliable enough to support real-world use in knowledge-driven industries.

A Visionary in Efficient, Scalable Large Language Model Systems

The client’s proposed endeavor is to continue research on designing and enhancing artificial intelligence infrastructures and model architectures for efficient, high-quality, and scalable training and inference of large language models. The core objective is to drive productivity in sectors such as healthcare, finance, and education by strengthening task automation and human-AI collaboration, a direction with clear, substantial merit and national importance.

The client currently works in a software engineering role at a U.S.-based company, where the day-to-day focus aligns with the proposed endeavor. This includes implementing quantization kernels to reduce the computational cost of model training and inference, building backend infrastructure to support scalable deployment of large language models on custom AI accelerators, and optimizing transformer architectures to improve predictive accuracy and performance. We positioned these efforts as practical, infrastructure-level advances that make large language model adoption more sustainable and deployable at scale.

Distinguished Research Portfolio

For NIW cases, numbers are helpful, but they are not automatically decisive. The question is whether the record shows independent uptake and technical relevance beyond routine participation in the field. The petition highlighted a publication and innovation record consisting of:
– 1 peer-reviewed journal article
– 2 first-authored peer-reviewed conference articles
– 1 granted patent

The client’s published work has been cited 57 times. Importantly, the filing did not treat citations as self-proving. Instead, it framed citations as evidence of independent reliance, showing that other researchers have found the client’s methods useful enough to build on. The petition also emphasized that one recent article accounted for a substantial portion of the citation activity and ranked in the top percentile for its publication year under field-normalized benchmarks discussed in the case, helping USCIS interpret impact in context rather than as a raw number alone.

The Result

USCIS approved the NIW petition, reflecting that the case presentation successfully connected the client’s work on efficient and scalable large language model infrastructure to national-level needs and demonstrated that the client is well-positioned to continue advancing this endeavor. We congratulate the client on this important milestone and look forward to the continued evolution of their contributions to practical, high-impact artificial intelligence systems.