Case Summary:      

In an era where artificial intelligence influences decisions ranging from climate models to portfolio management, researchers capable of translating theory into usable, secure, and interpretable systems are crucial. One such researcher, a machine learning expert from China, recently obtained EB-2 NIW (National Interest Waiver) approval in recognition of his groundbreaking contributions to artificial intelligence-based financial forecasting.

This success follows an expected Request for Evidence (RFE) issued in February 2025—an opportunity we strategically anticipated and addressed with extensive documentation, ultimately demonstrating the national importance of his work and his qualifications to carry it forward.

A Mission to Modernize Forecasting with AI

The petitioner’s endeavor is firmly rooted in applied machine learning and optimization theory, with a focus on designing robust, secure, and theoretically efficient models for financial forecasting, investment strategies, and market risk management. Working in a research capacity at a leading U.S. institution, the petitioner builds machine learning algorithms that can predict market behavior under uncertainty. His work is already informing the development of secure, AI-enhanced forecasting systems that serve both private-sector investors and public institutions concerned with economic resilience.

An Exceptional Record of Scientific Influence

The petitioner’s research record is both deep and impactful. He has authored 9 peer-reviewed conference papers, 2 preprints, and contributed to 1,428 citations

At least seven of his papers fall within the top 1–20% most cited papers in their publication years—a clear indicator of his influence across machine learning, optimization, and financial systems modeling.

Peer Recognition and National Relevance

The strength of the case was further elevated by a detailed letter from one of the professors, who highlighted the petitioner’s work on machine learning convergence in non-convex environments:

“[Client] has significantly improved the performance of machine learning algorithms that use non-convex optimization, which has resulted in significant improvement in reliability and accuracy of AI-based financial forecasting models… His research enables more secure and precise AI-based financial forecasting, which ultimately benefits decision-making processes and financial outcomes.”

The letter underscores how the petitioner’s work advances the security and robustness of AI systems, especially important as the U.S. prioritizes resilient, intelligent financial systems.

Why This Work Matters to the U.S.

We demonstrated that the petitioner’s endeavor aligns directly with U.S. national interests:

● His work advances critical and emerging technologies, as identified by the National Science and Technology Council, including AI and cybersecurity.

● It addresses financial risk management, one of the top economic concerns in both the public and private sectors.

● His research has received indirect support via funding from DARPA, NSF, DOE, and ARO—agencies that back high-impact, security-relevant research.

Outcome and Recognition

Filed in March 2024, RFE issued in February 2025, and approved in June 2025, the case followed a classic but carefully constructed NIW timeline. The response to the RFE included an expanded argumentation on the Dhanasar criteria, particularly the national importance and the petitioner’s demonstrated capacity to advance the endeavor through high-profile publications, citation influence, and continued research trajectory.

This EB-2 NIW approval celebrates not only a personal achievement but also the role of rigorous AI science in shaping the stability of financial ecosystems. As the client continues to pursue cutting-edge research on algorithmic forecasting, his work stands to influence national economic strategy, cybersecurity, and the future of intelligent investing.