MIT Technology Review China and DeepTech jointly launched the "China Intelligent Computing Innovators" selection, which aims to recognize researchers, engineers and industry practitioners who have made outstanding contributions to intelligent computing — individuals who have not only achieved breakthroughs in academic research, but also demonstrated exemplary leadership in the dissemination of technology and in commercial application.
Following a year-long process of solicitation, nomination and review, in April 2024, MIT Technology Review China × DeepTech officially released the list of the "2023 China Intelligent Computing Innovators," and Prof. Xiangtao Li of the School of Artificial Intelligence was named to the list. MIT Technology Review's "China Intelligent Computing Innovators" has become a benchmark for technology and talent in the intelligent computing industry, representing a dedication to frontier science and technology, an emphasis on the intelligent computing ecosystem, and a deep commitment to "Tech for Good."
According to the organizers of the MIT Technology Review "China Intelligent Computing Innovators" selection, Prof. Xiangtao Li has achieved outstanding research results in the field of intelligent computing, focusing on AI-driven scientific discovery: applying artificial intelligence technologies to the analysis and understanding of biological data, and using multi-omics data to probe biological mechanisms in depth, thereby facilitating drug discovery.
Li and his team are committed to developing deep learning models for single-cell transcriptomics in order to improve clustering performance on complex, high-dimensional and sparse spatial data. They successfully addressed the difficulty faced by conventional clustering algorithms in accurately measuring and identifying the distributions and structures of complex biological data — an advance that has driven progress in cellular data analysis and in the understanding of gene regulatory mechanisms. This work was published inNature Communications, with Zhuohan Yu, a doctoral student who enrolled in 2022, as first author; he was also among the first cohort of recipients of the National Natural Science Foundation of China (NSFC) doctoral program grant. In addition, the team has conducted in-depth research on the widespread problems of missing data and noise in cancer genomics data. His work has helped screen out variant genes that are highly associated with cancer, providing important theoretical support for cancer research. He has also explored a deep sequence language learning method based on proteomics data, improving the representation of biological sequences. This approach takes into account not only the sequential encoding of sequences, but also makes full use of the biological features contained within them, offering a new perspective for the interpretation of biological data. This new method has also been published inNature Communications, with Haoran Zhu, a doctoral student who enrolled in 2023, as first author.
