Nil Tianchen Mu is an HPC Systems Administrator at Arizona State University Research Computing, where his work centers on high-performance computing for scientific discovery. His research interests include research software engineering, the development and optimization of reproducible, standards-based scientific computing pipelines on shared HPC systems, and the benchmarking and tuning of heterogeneous and AI-accelerated architectures. Mu's recent work focuses on deploying and optimizing Nextflow pipelines on ASU supercomputers, including the refactoring of genomic sequencing software to nf-core standards in support of the Earth BioGenome Project. He is developing a large-scale virus detection and discovery pipeline as a part of the NASA OSDR Microbiome Analysis Working Group, and he benchmarks ASU supercomputers and Intel Gaudi2 accelerators for institutional HPC and AI workloads. He also maintains the scientific software stacks and bioinformatics databases that support genomics research across the institution. Mu contributes to the wider community as a member of the Energy Efficient HPC Working Group, where he analyzes HPC power and water usage data, and as a Carpentries instructor. Trained as a computational biologist, Mu's earlier research applied explainable machine learning and metagenomic analysis to microbiome and viral genomics. As part of the Arizona State Department of Health Services COVID-19 Sequencing Project, he contributed to genomic surveillance pipelines, and in separate work, he developed a genomic workflow that identified two novel bat coronaviruses. His peer-reviewed research has appeared in mSystems, mBio, and the Journal of Biological Chemistry, with conference contributions to PEARC, US-RSE, and RMACC.