Success Story: NIW Success Without Any RFE! We Helped an Indian Data Scientist Secure Approval
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“I am very satisfied with the level of service and the expertise the immigration firm holds. I highly recommend them to anyone seeking such assistance. Their team demonstrated deep knowledge of the U.S. immigration system and carefully guided me through the entire process.”
On November 21st, 2025, we received another EB-2 NIW (National Interest Waiver) approval for a Data Scientist in the Field of Machine Learning in Healthcare (Approval Notice).
General Field: Machine Learning in Healthcare
Position at the Time of Case Filing: Data Scientist
Country of Origin: India
State of Residence at the Time of Filing: California
Approval Notice Date: November 21st, 2025
Processing Time: 2 months, 10 days (Premium Processing Upgrade Requested)
Case Summary:
We are pleased to share the success story of an EB-2 NIW (National Interest Waiver) approval granted to a machine learning expert in healthcare with an M.S. in Management Science. The client approached NAILG for representation to obtain immigration approval for research focused on advancing explainable artificial intelligence for clinical decision-making. After carefully presenting the client’s record of achievement and the national value of the proposed work, NAILG successfully demonstrated that the client’s endeavor holds substantial merit and national importance, leading to NIW approval.
Research Focus and Contributions
The client’s proposed endeavor centers on advancing explainable machine learning and deep learning frameworks to build transparent, high-performing clinical models. The research aims to optimize clinical workflows and improve diagnostic precision, supporting early disease detection, reducing healthcare costs, and strengthening trust in AI-driven medical decisions across the U.S. healthcare system. By creating methods that extract actionable insights from complex clinical data and improve the interpretability of AI outputs, the client’s work helps clinicians make faster, safer, and more reliable decisions in real-world care settings.
To date, the client has authored 9 peer-reviewed journal articles (including 2 first-authored) and 1 peer-reviewed conference article. The client’s body of work has received 61 citations, demonstrating growing influence and recognition in the field. Independent researchers have cited and extended the client’s models for tasks such as extracting clinical meaning from unstructured records and strengthening automated diagnostic pipelines. This pattern of reliance confirms that the client’s research advances the broader scientific community and contributes to elevating clinical AI performance.
The petition also highlighted that some of the client’s publications rank among the top-cited works in computer science for their publication years, reflecting exceptional impact relative to field norms and demonstrating that the client’s contributions are both original and widely adopted.
Well Positioned to Advance the Proposed Endeavor
With advanced interdisciplinary training in management science, machine learning, and healthcare data modeling, the client is well-positioned to continue advancing this vital research area. The petition emphasized the client’s strong technical foundation in explainable AI, deep learning, and clinical data analysis, along with a clear forward-looking research plan focused on improving transparency, interpretability, and predictive performance in healthcare models.
The client’s ongoing work in the U.S. healthcare AI ecosystem demonstrates the ability to operate independently of any single employer and to produce research with broad, scalable applications across multiple clinical contexts. This independence supports national benefit by ensuring that the client’s innovations can be applied widely throughout healthcare systems, research institutions, and public-health initiatives.
Support from Experts in the Field
To substantiate the petition, NAILG presented two strong letters of recommendation from prominent experts in machine learning and clinical AI. These letters attested to the originality, practical application, and national importance of the client’s research, emphasizing that transparent diagnostic AI is essential for reducing errors and improving outcomes in U.S. medicine.
“I should note that [Client’s] expertise in management science makes him an excellent researcher to pursue research on real-time anesthesia risk detection in dental surgery using AI, and I am eager to continue our work on this important topic.”
NIW Approval and Outlook
The client’s EB-2 NIW petition was approved after NAILG demonstrated the depth and national significance of the client’s contributions to explainable AI and healthcare diagnostics. This approval reflects USCIS recognition that the client’s work strengthens early disease detection, enhances clinical reliability, and supports more cost-effective healthcare delivery in the United States.
NAILG is proud to have represented the client in this successful NIW petition and commends the client’s dedication to advancing trustworthy, high-impact AI tools for healthcare. The client’s continued research will further improve diagnostic quality, optimize clinical practice, and reinforce U.S. leadership in responsible medical artificial intelligence.