Date of Decision: October 29, 2015
Service Center: Texas Service Center
Form Type: Form I-140
Case Type: EB-2 National Interest Waiver (NIW)
Field of Expertise: Mathematics, specifically high-dimensional statistics and machine learning
Petitioner Information
Profession: Mathematician
Field: High-Dimensional Statistics and Machine Learning
Nationality: [Not specified]
Summary of Decision
Initial Decision: Denied
Appeal Outcome: Approved
Evidentiary Criteria Analysis
Criteria Met:
- Substantial Intrinsic Merit: The petitioner’s work in high-dimensional statistics and machine learning has significant intrinsic merit. His research introduces innovative algorithms that enhance computational efficiency and practical applications in fields such as personalized medicine and big data analysis.
- National in Scope: The petitioner’s contributions are applicable on a national scale, impacting diverse areas such as biomedical, financial, and military data analysis. His work facilitates improved decision-making and data processing in these critical fields.
Criteria Not Met:
- Impact on the Field (Initial Decision): Initially, the Director found insufficient evidence of the petitioner’s influence and impact on the field, particularly regarding the third prong of the NYSDOT analysis, which requires showing a degree of influence greater than that of an available U.S. worker with similar qualifications.
- Supplemental Evidence (Initial Decision): The initial decision noted a lack of supplemental evidence to contextualize the citations of the petitioner’s work and demonstrate its impact.
Key Points from the Decision
Proposed Endeavor:
The petitioner proposed to continue his research and teaching in high-dimensional statistics and machine learning, focusing on developing efficient algorithms for data analysis. His work aimed at enhancing personalized medicine through innovative data analysis techniques, enabling physicians to design individualized treatment plans for complex diseases.
Substantial Merit and National Importance:
The petitioner’s research holds substantial merit and national importance due to its innovative approach and wide-ranging applications. His methods in active learning and high-dimensional statistics allow for efficient data processing and analysis, crucial for handling massive datasets in various sectors.
Supporting Evidence:
- Peer-Reviewed Publications: The petitioner provided copies of 11 papers he authored or co-authored, citation data, and evidence of his role as a peer reviewer.
- Letters of Recommendation: Letters from supervisors, colleagues, and independent researchers detailed the petitioner’s contributions and their significance.
- Research Applications: His work in personalized medicine and big data analysis was highlighted as particularly impactful.
Inconsistencies in Proposed Endeavor:
No significant inconsistencies were noted in the petitioner’s proposed endeavor. The appeal addressed previous concerns about the impact and influence of his work, providing additional context and evidence.
Supporting Documentation
Letters of Intent:
- Summary: Letters from collaborators and researchers emphasized the practical applications of the petitioner’s methods in various fields.
- Key Points: These letters underscored the efficiency and adaptability of his algorithms, validating their significance.
Business Plan:
- Summary: Not applicable.
Advisory Letter:
- Summary: Advisory letters from experts in the field affirmed the petitioner’s groundbreaking contributions and the applicability of his methods.
Any Other Supporting Documentation:
- Additional Letters: Three new letters from independent academics were submitted, reinforcing the importance and influence of the petitioner’s work.
- Publication Citations: Updated citation data and contextual information about the impact of his publications were provided.
Conclusion
Final Determination: The appeal was sustained, and the petition was approved.
Reasoning: The evidence demonstrated that the petitioner’s work in high-dimensional statistics and machine learning has had a significant influence on the field. His innovative methods and their wide-ranging applications justify the waiver of the job offer requirement, serving the national interest to a greater degree than an available U.S. worker with similar qualifications.
