Why a talented scientist gave up a promising career in the US to return home

CHINA - A promising talent in artificial intelligence has left his tenured assistant professor position in New York State (USA) to return home to seek new research opportunities and take advantage of the favorable environment at Nanjing University.

An expert in the field of applied artificial intelligence (AI) research who spent many years studying and working in the US has just returned to China to pursue new opportunities emerging from the rapid development of AI in China, especially in the field of pharmaceutical research, according to SCMP.

Fu Tianfan, a former tenured assistant professor at Rensselaer Polytechnic Institute in Troy, New York, joined the School of Computer Science at Nanjing University in December 2024.

Fu Tianfan, an expert in the field of AI applied science research, will pursue a career in drug discovery and materials development at Nanjing University after working and studying in the US. Photo: SCMP

According to the scientist's profile on the university's website, his work focuses on AI-powered drug discovery and materials development.

Dr. Pho Thien Pham, 32, said that China's increasing investment in higher education has created unprecedented opportunities for young scientists like him.

“Nanjing University combines rich cultural heritage with a strong leadership position in natural sciences. Its comprehensive strength in basic sciences provides a favorable environment for my research in applying AI to advance scientific discovery,” scientist Fu Tianfan told the Post last weekend.

He added that being closer to his family after many years abroad was also an important factor in his decision to return home to work.

According to expert Pho Thien Pham, artificial intelligence technology has profoundly changed the traditional scientific research process. In the field of drug discovery - which previously relied on costly and time-consuming trial-and-error experiments - can now be accelerated by machine learning and big data.

Instead of manually testing thousands of compounds, researchers can now train deep learning models based on known drug mechanisms and virtually screen huge molecular libraries to predict “candidates” with high therapeutic potential.

Source: https://vietnamnet.vn/ly-do-nha-khoa-hoc-tai-nang-tu-bo-su-nghiep-day-hua-hen-tai-my-de-ve-nuoc-2384661.html