Success Story: An AI Researcher Earns NIW Approval in under 2 months! Thanks To Our Expert Assistance
Client’s Testimonial:
"Thank you for everything that led to my approval of I-140, I really appreciated the overall process with you."
On May 12th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for a Graduate Research Assistant in the Field of Artificial Intelligence (Approval Notice).
General Field: Artificial Intelligence
Position at the Time of Case Filing: Graduate Research Assistant
Country of Origin: Turkey
State of Residence at the Time of Filing: Massachusetts
Approval Notice Date: May 12th, 2026
Processing Time: 1 month, 18 days
Case Summary:
This approval marks another successful NIW case for a researcher from Turkey specializing in AI for distributed and edge systems, whose work sits at the intersection of deep learning, neural network optimization, and energy-efficient computing infrastructure. As a graduate research assistant in the field of artificial intelligence, the proposed endeavor is to continue developing scalable, communication-aware optimization methods for pruning, mapping, and compressing deep neural networks, enabling resilient, energy-efficient, and low-latency multimodal inference across large-scale distributed edge systems.
At the time of filing, the petitioner was already advancing this work through an active research position in the United States. The petition was approved in under 2 months without an RFE, an outcome that reflects both the strength of the petitioner's record and the thoroughness of the petition our team prepared on their behalf.
Research with National Importance
In the petition, we demonstrated substantial merit and national importance by linking the proposed endeavor to urgent U.S. priorities in AI infrastructure, energy sustainability, and national security. This work on optimizing deep neural network deployment across distributed edge systems directly supports the nation's efforts to build more efficient, scalable, and resilient AI infrastructure, an area explicitly recognized as a critical and emerging technology by the National Science and Technology Council and a stated federal priority under America's AI Action Plan.
Academic Contributions and Recognition
The petition documented an established record of scholarly output and peer recognition. The published work includes 3 peer-reviewed conference articles (2 of them first-authored and 1 co-first-authored) and 3 preprints. These publications have received 153 citations.
We further highlighted that the research has been supported by funding from the National Science Foundation, one of the most selective and prestigious funders in U.S. science and technology, as additional evidence of the recognized importance and national relevance of this work. At least 8 peer reviews have also been completed for authoritative venues, confirming recognized expertise and standing within the field.
Expert Endorsements
The filing included three expert recommendation letters from leading academics at prominent U.S. research institutions. These letters significantly strengthened the petition by corroborating the national importance of the proposed endeavor and affirming the depth of expertise and track record demonstrated in this field. As one expert stated:
“[Client]’s research meaningfully advances the deployment of large-scale, distributed multimodal artificial intelligence across edge networks, reinforcing the United States’ leadership in AI and improving the performance, scalability, and resilience of critical real-world systems.”
NIW Approval and Outlook
In the filing, we showed that the endeavor has substantial merit and national importance, and that the petitioner is well-positioned to advance it through a strong publication and citation record backed by major research funding from federal and defense agencies. We are proud to have supported this petitioner through the process and look forward to seeing the continued contributions this work will bring to AI infrastructure, energy sustainability, and U.S. technological competitiveness.