Case Summary:   

We are proud to share the EB-2 National Interest Waiver (NIW) approval of a statistician and data scientist from China, whose innovative work is reshaping how industries, from healthcare to finance, forecast the future. This approval, granted through premium processing, recognizes the exceptional merit and national importance of the client’s development of large-scale deep learning models that support data-driven decision-making across high-impact sectors.

Building Smarter Predictions with Deep Statistical Insight

Specializing in statistical modeling, deep learning, and predictive analytics, the client has devoted her career to improving how we understand and predict complex systems. Her work has addressed real-world problems in:

● Food security, by modeling the nutritional impact of food pantries
● Public health, by forecasting disease transmission trends
● Cybersecurity, by detecting bot attacks using ML frameworks
● E-commerce, by analyzing and predicting user behavior
● Supply chain logistics by forecasting product demand and inventory

Through her current role as a senior data scientist in industry, she develops and deploys end-to-end machine learning systems designed to forecast consumer needs, monitor digital threats, and provide actionable intelligence across massive cloud-based datasets.

Publishing Research That Shapes Policy and Practice

The client has published 5 peer-reviewed journal articles, including two first-author papers, and has amassed 38 total citations. Two of her articles rank among the top 10% most cited papers in the field of Mathematics for their respective years, according to ESI citation metrics.

Among these standout contributions:

● Her 2021 study in the British Journal of Nutrition (31 citations) challenged assumptions about food pantry offerings and revealed their significant role in supporting dietary health.
● A 2023 paper in the Journal of Computational and Graphical Statistics introduced a scalable predictive model for complex dynamic systems, earning notable recognition in the statistics community.
● Her 2024 work in Neural Networks focused on deep network embedding and behavior classification, further demonstrating her command over both theoretical and applied machine learning.

Recognition from Global Scholars and U.S. Institutions

Her research has influenced a wide array of investigations worldwide, cited in academic literature focused on:

● Food access and equity
● Public health interventions
● Remote sensing and dynamic system prediction

Notably, one independent recommender, a professor of statistics, stated:

“[Client’s] deep learning architecture not only estimates evolving system states but learns unknown parameters dynamically. She has directly advanced U.S. leadership in statistical modeling and machine intelligence”.

These sentiments are echoed in multiple letters from U.S.-based professors and government researchers who affirmed the broad national utility and technical sophistication of her work.

Driving Progress with Federal Research Support

The client’s work has been backed by major national funding agencies, including:

1. The National Science Foundation’s Division of Mathematical Sciences (DMS)
2. The NSF’s Division of Computer and Network Systems (CISE/CNS)
3. The National Institute of General Medical Sciences (NIGMS)

These agencies support projects that enhance national security, public health, and economic innovation—goals directly advanced by the client’s predictive models in disease detection, fraud mitigation, and supply chain forecasting.

Why Her Work Matters to the U.S.

From reducing healthcare expenditures (which currently total $4.5 trillion annually) to addressing the $10 billion lost to fraud in 2023 alone, the client’s deep learning models address some of the United States’ most urgent systemic challenges. Her ability to derive actionable insight from high-dimensional data makes her contributions indispensable in a data-driven economy.

In alignment with Critical and Emerging Technologies (CET) identified by the U.S. government, including AI, cybersecurity, and data science, her research ensures that American institutions remain at the forefront of innovation and competitiveness.

A Rapid, Well-Evidenced Approval

Filed on February 27, 2025, and approved on May 26, 2025, this EB-2 NIW petition presented compelling evidence under the Matter of Dhanasar framework. Her strong publication record, high citation impact, secured funding, and forward-looking research plan all demonstrated her value to U.S. scientific advancement.

We are honored to support visionaries like this client—individuals whose talent and drive enable transformative discoveries and technologies. From statistical innovation to real-world application, her journey exemplifies what it means to combine intellectual rigor with societal relevance.