Fu Tianfan, a 32-year-old AI expert, has left a promising academic career in the US to join Nanjing University in China, attracted by its strong emphasis on natural sciences and family proximity. He highlights AI’s transformative role in drug discovery, enabling faster research through machine learning and big data.

Fu Tianfan, a 32-year-old AI expert, has made the significant decision to leave his tenure-track assistant professor position in New York for opportunities at Nanjing University. He reflects that China’s substantial investment in higher education is creating unprecedented avenues for young scientists, leading him to seek a return to his homeland.

Fu expressed that “Nanjing University combines a rich cultural heritage with strong leadership in the natural sciences”. He believes that the university’s broad strengths in fundamental science provides an ideal environment for applying artificial intelligence to enhance scientific discovery.

Another crucial factor in his decision was the desire to be closer to family after spending several years overseas. He noted the impact of AI technologies, which have transformed traditional scientific methodologies, particularly in drug discovery. Through machine learning and big data, cumbersome trial-and-error experiments can now be expedited.

Fu elaborated that, rather than manually testing thousands of compounds, researchers can utilize deep learning models informed by known drug mechanisms to efficiently screen vast molecular libraries. This approach enables the prediction of candidates with high therapeutic potential, showcasing the powerful capabilities of AI in modern science.

In summary, Fu Tianfan’s transition from a successful academic career in the United States to Nanjing University in China underscores the significant opportunities burgeoning within China’s education sector for young scientists. With a strong academic background and a commitment to leveraging AI for scientific advancements, Fu’s return is motivated by both professional prospects and personal connections.

Original Source: www.scmp.com

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