Case Summary:

An expert in the field of computational science hailing from Bangladesh came to NAILG seeking help filing his NIW (National Interest Waiver) application. His proposed endeavor was to continue designing predictive models of drug sensitivity in order to create more effective personalized therapies for cancer and other medical conditions. His work was thus of value to the advancement of deep learning, particularly in its translation to predictive models, personalized therapies, and other applications in medicine and healthcare. This is why it was also of great interest to the country. Given his achievements in this area, including his design of transfer learning and deep learning algorithms with greater predictive accuracy, his work also possessed inherent medical value.

We made sure to show that his proposed endeavor also has broad implications for the United States, given the severe impact that cancer has on the well-being of this nation. We presented data from the National Cancer Institute which has reported that in 2020, more than 1.8 million new cancer cases were projected to be diagnosed in the country, killing more than 600,000 Americans. It was due to this evident national importance, that his research has in fact been supported with funding from the National Institutes of Health (NIH). One of his recommenders was thus noted saying:

“Data indicate that nearly forty percent of Americans will be diagnosed with cancer at some point in their lives, and nearly two million cancer diagnoses are made every year. Cancer treatment is thus a huge part of the responsibility of the American health care system, and treatments based on our understanding of individual genetic traits are some of the most useful recent developments in this area. The development of such treatments, however, requires reliable, consistent data to avoid wasted or counterproductive efforts. By developing means of clearly and unambiguously interpreting the contradictory information in pharmacogenomic databases, [the client] has enhanced the resources available to researchers working on all types of personalized cancer treatment.”

On the other hand, his work also stretches into the academic field and has resulted in 6 peer-reviewed journal articles (2 of them first-authored) and 2 peer-reviewed conference articles (1 of them first-authored) which have been cited 65 times already. He has also been invited to conduct reviews some 6 times in the past. In his 5 long years of working in the field, he has indeed gathered appreciation from several peers and experts who have relied on his work to further their own research. One such expert said the following in a recommendation written for our client:

“More than 16 million people living in the US have received a cancer diagnosis, and a huge proportion of them are being treated with some variety of drug regimens. The effectiveness of treatment and the severity of side effects, however, vary widely between individual patients, and researchers and physicians alike have found it extremely difficult to anticipate these outcomes. [The client] has addressed this issue with a method of modeling drug sensitivity more accurately and thus predicting patient responses to medication. Accurate predictions, in turn, inform doctors’ decisions about prescriptions and doses, not only reducing mortality rates but also improving the quality of life for millions of survivors.”

As is clear from this statement, our client was a very talented researcher whose work in the field is of interest to the U.S. and the world. We assured the USCIS via our petition that he will continue to conduct nationally-significant research in computational science and thus prioritize the interest of the United States. These factors worked in his favor and won him the NIW approval and thus a labor certification waiver without an RFE.

We are glad to have been of help to him in this process and we wish him all the best in his future endeavors.