Success Story: A Machine Learning Researcher Advancing Robust and Trustworthy AI Achieved NIW Approval with NAILG’s Support
Client’s Testimonial:
"I will choose wegreened again if I need to do EB1-A in the future."
On April 28th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for a PhD Student in the Field of Machine Learning (Approval Notice).
General Field: Machine Learning
Position at the Time of Case Filing: PhD Student
Country of Origin: China
State of Residence at the Time of Filing: Pennsylvania
Approval Notice Date: April 28th, 2026
Processing Time: 11 months (Premium Processing Requested)
Case Summary:
North America Immigration Law Group (Chen Immigration Law Associates) first established that this researcher already had a substantial record in machine learning. At the time of filing, the client was a Ph.D. Student whose work has resulted in 25 peer-reviewed conference articles, including multiple first-authored and co-first-authored papers, along with 485 citations and at least 40 completed peer reviews for leading conferences such as NeurIPS, ICLR, ICML, and
The researcher’s work centers on the development of sophisticated machine learning frameworks designed to uncover the underlying causal structures within complex datasets. By engineering methods that prioritize automatic knowledge discovery and system integrity, his research addresses the essential technical requirements for building AI systems that are not only high-performing but also inherently robust and dependable under real-world conditions.
Our legal team demonstrated that this research agenda is a direct response to urgent national priorities. The petition strategically framed his work as a dual solution: accelerating the pace of scientific innovation through automated inference while simultaneously bolstering public and institutional trust in autonomous systems. By connecting his advancements in causal discovery and adversarial robustness to federal mandates for safe, secure, and trustworthy AI development, the firm established the profound value of his continued contributions to the United States' technological leadership and domestic security.
The evidence also showed that other researchers were already relying on his results. Independent teams had used his work in later studies involving adversarial training, hierarchical clustering, structure learning, and natural language model security. That pattern of reliance helped demonstrate that his findings were already shaping how others approached robustness and causal inference in machine learning, which strengthened the argument that he was well-positioned to continue advancing the field.
USCIS approved the NIW petition on April 28th, 2026, after 11 months, with Premium Processing requested on March 2nd, 2026. We were proud to help secure this result for a machine learning researcher whose work supports more robust, trustworthy, and scientifically useful AI systems in the United States.