Success Story: Overcoming Two RFEs, a Computer Science Researcher Secures NIW Approval for Time Series Deep Learning With Our Assistance
Client’s Testimonial:
"That's great news! Thank you so much!"
On February 9th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for a Graduate Teaching Assistant in the Field of Computer Science (Approval Notice).
General Field: Computer Science
Position at the Time of Case Filing: Graduate Teaching Assistant
Country of Origin: China
State of Residence at the Time of Filing: Virginia
Approval Notice Date: February 9th, 2026
Processing Time: 11 months, 2 days
Case Summary:
Many AI systems fail not because the models are too small, but because the data they must interpret is messy, temporal, and incomplete. When time series models are inaccurate or inefficient, the result can be weaker diagnostic support, unreliable financial forecasting, and delayed environmental risk detection. The client’s endeavor targets that bottleneck by developing and refining deep learning models for time series analysis to increase the effectiveness of AI across public sectors, including healthcare, finance, and environmental risk analysis.
North America Immigration Law Group (Chen Immigration Law Associates) guided the case through a demanding adjudication arc that included two Requests for Evidence (RFEs). Our responses re-centered the analysis on the proposed endeavor rather than a job title, and we organized the record under the Dhanasar framework to show national importance, strong positioning, and why a waiver of the job offer and labor certification requirements benefits the United States.
Strengthening AI Through Better Time Series Modeling The client holds an M.S. in Computer Information Systems - Data Analytics and is currently conducting research in the United States in a university-based research role aligned with the proposed endeavor. The petition framed the client’s work as practical infrastructure for modern AI: improving how models learn from temporal patterns, reducing computational bottlenecks in time series pipelines, and enabling more accurate, deployable methods that can be integrated into real-world systems.
Government and Community Trust Signals as Objective Anchors NAILG emphasized that NIW strength comes from independent validation, not self-asserted importance. The record presented objective indicators that the client is already influencing the field and is well-positioned to keep advancing the endeavor: ●Peer-review trust: at least 27 completed peer reviews for authoritative journals and selective conferences, reflecting trusted technical judgment ●Scholarly output: 1 co-first-authored peer-reviewed journal article and 2 first-authored peer-reviewed conference articles ●Independent reliance: 17 citations at the time of filing, including evidence that at least two papers ranked among the top 10% most cited in computer science for their publication years
The petition explained what they signal in context. In computer science, rigorous peer-reviewed conferences are often primary publication venues, so repeated success there reflects meaningful peer screening. Citations matter most when framed as independent reliance, meaning other researchers are building on the work rather than merely acknowledging it. Peer-review invitations provide a separate trust signal because venues typically select reviewers they consider technically authoritative and reliable.
Responding to the RFEs With a Clearer NIW Theory When USCIS questioned the record, our RFE responses clarified the legal framing and tightened the evidentiary logic:
- Defined the endeavor: We presented the client’s work as a specific, feasible program of research in time series deep learning, rather than a broad interest in AI.
- Clarified national importance: We connected time series optimization to high-impact applications where temporal data drives decisions, including healthcare diagnostics support, financial stability analysis, and environmental risk detection.
- Demonstrated momentum: We used peer-reviewed publications, field-normalized citation performance, and extensive peer-review service to show the work is already being evaluated, selected, and relied upon by the research community.
“To enhance the cost-effectiveness and efficiency of time series classification applications in critical sectors, particularly healthcare, it is imperative that the US support his work.”
The Result With the case positioned around a nationally important AI endeavor, supported by documented scholarly output, independent reliance reflected in citations and citation percentiles, sustained peer-review trust, and expert endorsements, the NIW petition was approved despite two RFEs.