New Faculty: Yun William Yu
By Adam Kohlhaas
Yun William Yu grew up in Indiana, where he eventually earned degrees in math, chemistry and German from Indiana University as a Wells Scholar. He continued his education with a Master of Research (MRes) in biomedical physical chemistry and a Master of Philosophy in applied mathematics at Imperial College London. Later, he pursued his Ph.D. at MIT, focusing on compressive algorithms for genomics, and completed a postdoc at Harvard Medical School in biomedical informatics. He spent four years as an assistant professor at the University of Toronto's math department before joining the Carnegie Mellon University (CMU) Computational Biology Department (CBD).
Yu’s journey into computational biology began in earnest when he realized the potential applications for mathematics in life sciences during his MRes studies at Imperial College London. This sentiment solidified while he worked on his Ph.D., which combined his interest in mathematics with a desire to make an impact in genomics research.
Yu has co-authored three significant papers that demonstrate the interplay between theory and practical software in bioinformatics. The first published in Transactions on Knowledge and Database Engineering, reduces the space-complexity of the MinHash probabilistic Jaccard index sketch. The second paper, published in Genome Research, provides a rigorous justification for the seed-chain-extend alignment heuristic, a commonly used bioinformatics heuristic. The third paper, which just appeared in Nature Methods, leverages theoretical understanding of biases in probabilistic sketching to produce a more robust and faster average nucleotide identity estimator for metagenomic sequences.
As a researcher bridging the gap between theory and practice, Yu often translates results across different disciplines, adhering to multiple standards of proof. This approach is evident in his recent projects, which emphasize both mathematical and empirical proof. Yu believes in tailoring the presentation of his work to the audience’s interests, whether it’s the elegance of the solution or its practical impact.
“My primary aspiration for my future work is that it is on problems I enjoy, and that my students enjoy as well,” Yu says. “With luck, solving them will also be useful for the research community and society at large, but I think it's important to have fun while doing research.”
In the past, Yu danced and did recreational gymnastics in his free time, but being a professor has made these hobbies difficult to maintain. During the pandemic, Yu took up longboarding — a skateboarding variant — in Toronto and has made progress despite some initial challenges. After seeing the hills around Pittsburgh, though, Yu is considering switching to a different sport that may prove less injurious.