As a University Professor at University of Waterloo, Ming Li has made pioneering and enduring contributions to modern information theory and bioinformatics. Li won Killam Prize, and was elected as fellow of RSC, ACM, IEEE, and ISCB.
Together with his colleagues, especially Bennett, Gacs, Vitanyi, and Jiang, Li has systematically developed Kolmogorov complexity and its applications, one of the fundamental pillars for computer science. He extended Kolmogorov complexity from one sequence to two (STOC’93), to measure not just information within one sequence, but also information between two sequences. He has also developed the incompressibility method to solve several long-standing open problems in average-case analysis of algorithms. His book with Paul Vitanyi shaped this new field and is regarded by many readers on Amazon.com as a classic, rated 5 stars. In today’s information society, his book helped to educate a generation of researchers and practitioners about what is information, and provided a foundation or spiritual guide for many research fields including deep learning and large language models.
Li is a pioneer in bioinformatics. His work (with Blum, Jiang, Tromp, Yannakakis) on shortest common supersequence, provided a fundamental background for shotgun sequencing and is described in detail in well-known computational biology books. The optimized spaced seeds by Li’s team (with Ma and Tromp) have changed the way we do homology search. In 2017, Nature Biotechnology has raised the challenge of neoantigen discovery and validation using mass spectrometry. Li solved the problem with a series of cross-field publications in PNAS 2017, Nature Methods 2019, Nature Machine Intelligence 2020, 2021 and 2023, Nature Comm 2022 and 2023. His pipeline is serving the community: from a tumor sample to immunogenic neoantigens. His PEAKS Online (Nature Comm. 2022) is serving over 4000 pharmaceutical and institution users, and was used in over 5000 papers as a tool.
