Success Stories: EB-1A Approved for Computer Scientist in Illinois in the Field of Machine Learning
Client’s Testimonial:
Thanks so much for your help through these months! You are so amazing! Your kindness, patience, and professional work greatly impressed me. […] I have already recommended you on bbs. It’s worth recommending your work!
On January 14, 2015, We Received Another EB1-A (Alien of Extraordinary Ability) Approval for a Computer Scientist in the Field of Machine Learning (Approval Notice)
General Field: Machine Learning
Position at the Time of Case Filing: Computer Scientist
Country of Origin: China
Service Center: Nebraska Service Center (NSC)
Country of Residence at the Time of Filing: Illinois
Approval Notice Date: January 14th, 2015
Processing Time: 1 Month, 28 Days (9 Days After Requesting Premium Processing)
Case Summary:
For this case we worked with a computer scientist from China with a specialized focus on mathematical theories and practical applications of pattern recognition analysis in machine learning. His highly prolific career thus far had produced 26 peer-reviewed scientific articles, numerous presentations at national and international conferences, and 1 patent, and he had been cited at least 55 times, primarily by independent researchers at prominent and leading institutions and organizations worldwide. This remarkable citation record clearly indicates the major significance of our client’s work. He had also reviewed at least 37 manuscripts for 8 or more distinctive, internationally-circulated journals. It was our goal to prove that our client qualified for classification as an Alien of Extraordinary Ability given that he sought to remain in the United States to continue work in the area of Machine Learning, and that his continued research would substantially and prospectively benefit the United States. An independent recommender affirmed the significance of our client’s research: “Feature selection is a critical step in the process of building learning models, but sometimes with such complex sets of features, it is difficult for existing methods of selection to determine the most appropriate for use in the model. … [Client] has created a novel and efficient feature selection method which considers “group features simultaneously,” meaning that if features are similar enough, hey will be grouped accordingly for selection. … the resulting algorithm has been shown to be one of the fastest while maintaining the best performance among existing approaches, signifying that not only is [Client’s] work important, but it is among the best in the field.” With the proof and documentation that we provided, his case was approved in just 1 month and 28 days.