Success Story: Advancing Privacy-Preserving Machine Learning: NIW Approval After RFE Secured for a Computer Science and Engineering Researcher
Client’s Testimonial:
“I wanted to personally thank for all of your help throughout my NIW case, especially William during the RFE process.”
On September 29th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for an Assistant Professor in the Field of Computer Science and Engineering (Approval Notice).
General Field: Computer Science and Engineering
Position at the Time of Case Filing: 5th year PhD Student
Position at the Time of Case Approved: Assistant Professor
Country of Origin: Bangladesh
State of Residence at the Time of Filing: Texas
Approval Notice Date: September 29th, 2026
Processing Time: 28 months (Premium Processing Upgrade Requested)
Case Summary:
Modern digital infrastructure increasingly depends on technologies that can protect sensitive information while allowing advanced artificial intelligence (AI) and machine learning systems to remain useful, efficient, and adaptable. The client’s work focuses on one such area by developing privacy-preserving machine learning algorithms that address data heterogeneity and privacy concerns in distributed environments. Holding a B.S. in electrical and electronic engineering, the client sought classification under the EB-2 National Interest Waiver (NIW) category through the exceptional ability pathway to continue research aimed at improving data security, cybersecurity, and efficient machine learning technologies.
At the time of filing, the client was conducting computer science and engineering research at a U.S.-based academic institution. The client’s proposed endeavor focused on advancing privacy-preserving machine learning, distributed system privacy, and cybersecurity-related technologies. The petition explained that this work is important because stronger privacy-preserving methods can help protect sensitive data, support secure digital systems, and improve the reliability of technologies used across public and private sectors.
A central part of the petition focused on demonstrating that the client was well positioned to carry this work forward. Rather than relying on a single achievement, North America Immigration Law Group (NAILG) presented a combination of academic training, professional memberships, scholarly output, citation impact, peer-recognition evidence, expert support, and research funding. The client had authored 2 peer-reviewed journal articles and 8 peer-reviewed conference articles, including 3 first-authored works. The published work had received 106 citations, showing that other researchers had relied on the client’s findings in areas such as cybersecurity, mobile IP security, sensor-based systems, ransomware detection, and privacy-related computing. The petition also noted research support connected to the National Science Foundation, which further reinforced the relevance of the client’s work to public welfare, national security, and practical computing solutions.
The record further included 2 recommendation letters, including 1 independent advisory opinion. After the petition received an RFE, additional expert support was submitted to further strengthen the record and address the issues raised during review. One expert noted:
“It is therefore within the interests of the United States to allow [Client’s] research to continue.”
The client’s significance was demonstrated through more than publication and citation numbers. The petition showed that independent researchers had directly used the client’s work to inform later studies involving mobile security models, ultrasonic sensor applications, environmental monitoring systems, and ransomware detection methods. These examples helped show that the client’s research had practical value beyond publication and had contributed to how other researchers approached real-world computing, security, and monitoring problems.
The petition also placed the citation record in context. One of the client’s publications ranked among the top 10% most-cited Computer Science articles for its publication year, while another ranked among the top 20%. This comparative evidence helped demonstrate that the client’s work had attracted meaningful attention within the field and that the research had been used by other scholars at a notable rate.
Another important component of the case involved connecting the client’s prior research to the proposed endeavor. The petition highlighted the client’s work on malware identification, low-cost monitoring systems, and privacy-preserving federated learning. Together, these projects supported the argument that the client had already made progress in developing practical computing methods that improve security, efficiency, and privacy. NAILG used this record to show that the client’s future research plans were not speculative, but instead built directly on prior contributions in computer science and engineering.
In responding to the RFE, we emphasized the client’s qualification through the exceptional ability pathway, the national importance of privacy-preserving machine learning, the client’s publication and citation record, expert support, peer-review service, and continued plans to advance secure and efficient computing technologies in the United States. USCIS ultimately approved the client’s I-140 NIW petition after RFE review, allowing the client to continue pursuing research that may contribute to stronger data privacy, improved cybersecurity, and more reliable AI-driven technologies.