Case Summary:  

A central challenge in modern statistical science is how to build models that remain reliable when data are unevenly distributed, interconnected, and constantly changing, especially when decisions depend on what those models predict. The client’s work sits in that space, advancing probabilistic machine learning and statistical network analysis to help researchers and practitioners interpret complex patterns, from infectious disease diffusion to large-scale networked systems.

One independent expert captured the value of the client’s work: “Ensuring the uninterrupted continuation of [Client’s] work is vital for the United States to fully benefit from his contributions and maintain its competitive edge in these critical fields.”

For EB-1A, the most persuasive cases tend to look less like a résumé and more like a pattern: the field repeatedly signals that the work is worth publishing, worth citing, and worth trusting. North America Immigration Law Group (Chen Immigration Law Associates) built the filing around those field behaviors, emphasizing that the client’s contributions were not isolated results but part of a sustained trajectory in statistics with documented downstream reliance.

Instead of presenting disconnected projects, the case framed a cohesive set of contributions across three connected areas. First, the client’s work on disease diffusion modeling examined how uneven population distribution influences COVID-19 diffusion behavior. Second, the client advanced probabilistic machine learning methods for modeling spatial point pattern data. Third, the client developed statistical tools for modeling network data used across multiple scientific domains. Presented together, these contributions showed a consistent technical focus: creating methods that help others draw valid inferences from complex, real-world data.

NAILG organized the evidence so USCIS could quickly verify impact through objective indicators:
● Peer-review activity: at least 30 completed journal reviews, reflecting repeated trust in the client’s technical judgment
● Publication record: 8 peer-reviewed journal articles (4 first-authored), 2 peer-reviewed conference papers (1 first-authored), and 3 preprints (2 first-authored)
● Citation reliance: 240 citations, with documented citing activity spanning at least 29 countries
● Research support: funding associated with major U.S. research agencies
● Expert validation: 6 letters of recommendation, including multiple independent advisory opinions from experts familiar with the client’s work through reading and relying on published research

The filing also showed the client’s technical foundation and continuity in the field. With a Ph.D. in Statistics, the client has continued applying probabilistic machine learning and statistical network analysis in a U.S.-based industry research role, developing statistical and machine learning methods that support data-driven decision-making at scale. This continuity helped demonstrate that the EB-1A profile reflects sustained accomplishment and an ongoing capacity to contribute at the highest level.

USCIS approved the EB-1A petition in 45 days, recognizing a record shaped by selective publication, independent reliance, sustained peer-review trust, and strong expert corroboration. We congratulate the client on this important milestone and look forward to the continued evolution of the client’s work in statistics and probabilistic machine learning.