Success Story: Accelerating Materials and Molecular Discovery Through Artificial Intelligence: NIW Approval Secured for a Bangladeshi AI Researcher
Client’s Testimonial:
"I am extremely grateful to the Chen Immigration team for their outstanding support throughout my EB-2 NIW process. Their expertise, attention to detail, and deep understanding of immigration law made the entire experience smooth and stress-free. They carefully prepared a strong petition package, provided clear guidance at every step, and were always responsive to my questions. Thanks to their professionalism and dedication, my NIW petition was approved successfully. I highly recommend Chen Immigration to researchers and professionals pursuing employment-based immigration petitions.”
On May 29th, 2026, we received another EB-2 NIW (National Interest Waiver) approval for a Graduate Research/Teaching Assistant in the field of Artificial Intelligence (Approval Notice).
General Field: Artificial Intelligence
Position at the Time of Case Filing: Graduate Research/Teaching Assistant
Country of Origin: Bangladesh
State of Residence at the Time of Filing: South Carolina
Approval Notice Date: May 29th, 2026
Processing Time: 7 months, 6 days (Premium Processing Requested)
Case Summary:
The discovery of new materials and molecules is often slowed by costly experiments, complex structural analysis, and time-intensive trial-and-error testing. This case involved a Bangladeshi artificial intelligence researcher whose work focuses on developing machine learning and generative AI methods to automate and accelerate the discovery and design of materials and molecules. At the time of filing, the client was conducting research at a U.S. research institution, where he continued building scalable AI models for materials property prediction, crystal structure analysis, generative materials design, and computational discovery tools with applications in energy storage, semiconductors, pharmaceuticals, and advanced manufacturing.
Research Contributions in Artificial Intelligence
The client’s research helps replace resource-intensive discovery processes with data-driven models that can predict material properties, generate new chemical compositions, and identify promising structures more efficiently. His work includes scalable graph neural networks for materials property prediction, transformer-based models for generative materials design, and machine learning-guided approaches for crystal structure prediction. These contributions support faster innovation in materials science, molecular science, computational chemistry, battery technologies, and semiconductor-related materials development.
Academic Contributions and Recognition
The client’s record included 14 peer-reviewed journal articles, 2 preprints, 452 citations, and at least 30 peer reviews for authoritative journals in artificial intelligence, computational materials science, machine learning, and related fields. His work appeared in highly regarded journals across computer science, materials science, and computational chemistry. Several of his publications ranked among the most cited computer science papers for their publication years, including multiple papers ranked within the top 1% and top 10%, showing substantial independent reliance on his research. The petition also emphasized the client’s peer review service, open-source research contributions, high citation impact, and documented use of his models by independent researchers to demonstrate the broader significance of his work.
NIW Approval and Outlook
The I-140 NIW petition was filed on October 23rd, 2025, later upgraded to Premium Processing, and approved on May 29th, 2026. This approval recognized the national importance of the client’s work in artificial intelligence and the value of his research in accelerating materials and molecular discovery for sectors central to U.S. technological leadership.
With this approval secured, the client is well-positioned to continue developing AI-driven tools that can improve materials design, support semiconductor and energy innovation, advance computational chemistry, and help make scientific discovery faster and more efficient in the United States.