About Hao

Live like an anonymous flower.

Welcome to my homepage! I am Hao Xu (@nyxflower), a second-year CS PhD student at the University of California, San Diego, working under the supervision of Prof. Nuno Banderia. Previously, I was a researcher at Queen’s Computational Systems Biology Lab, led by Prof. Laurence Yang. I also worked as a machine learning consultant with Dr. James Yurkovich at Samumed LLC.

I hold an honoured master’s degree in Computer Science from the University of Sheffield, where I completed a dissertation on multirelational link prediction heterogeneous graph under the supervision of Prof. Haiping Lu, earning the the Fretwell-Downing Prize for the best M.SC. Dissertation. Prior to focusing on computer science, I graduated form Central South University and worked as a geotechnical engineer.

Research Interests

My research is centered on developing trustworthy machine learning and computational methods to address challenges in biomedical science, especially in proteomics. Specifically, I am interested in graph neural networks for mining graph-type bio-data, mass spectral library search for peptide identification, and exploring the synergy of machine learning and simulation methods in computational biology. Learn more about my research statement and view a list of all projects.

Publications

All publications can be found on Google Scholar.

• GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs [Dataset and code]
  Hao Xu, Shengqi Sang, Peizhen Bai, Ruike Li, Laurence Yang, Haiping Lu
  Pattern Recognition, 2023
• Compound Screening with Deep Learning for Neglected Diseases: Leishmaniasis [Code]
  Jonathan Smith, Hao Xu$^*$, Xinran Li, Laurence Yang, Jahir Gutierrez
  Proceedings of the 16th Machine Learning in Computational Biology (MLCB) meeting, 2022
  ($^*$ joint first author)
• A dynamic metabolic map for diabetes
  Jiao Zhao, Hao Xu, Laurence Yang
  Nature Computational Science, 2021

Presentations

• (06/05/2024) Excited to present “Benchmarking peptide spectral library search” at the 72th American Society for Mass Spectrometry (ASMS) Conference, Anaheim, California. [Slides]

• (05/11/2023) Presented “Enhancing interpretability of equivariant neural networks for protein structures” at D.E. Shaw Research, New York. [Slides]

• (11/10/2022) Glad to give a guest lecture on “Neural Networks for Biological Networks: An Introduction” in CHEE 886 at Queen’s University, Kingston, Canada. [Slides]

• (07/13/2022) Presented “Enhancing interpretability of equivariant neural networks for protein structures” at the 30th Conference on Intelligent System for Molecular Biology (ISMB), 3D Special Interest Group (3DSIG) COSI, Madison, Wisconsin. [Poster]

• (07/14/2022) Presented “Exploring the Mechanisms of Polypharmacy Side Effects via Two-stage Graph Neural Networks” at the 30th Conference on Intelligent System for Molecular Biology (ISMB), Machine Learning in Computational and Systems Biology (MLCSB) COSI, Madison, Wisconsin. [Video]

• (07/13/2022) Presented “Enhancing interpretability of equivariant neural networks for protein structures” at the 30th Conference on Intelligent System for Molecular Biology (ISMB), 3D Special Interest Group (3DSIG) COSI, Madison, Wisconsin. [Poster]

• (11/29/2021) Glad to give a guest lecture on “Neural Networks for Biological Networks: An Introduction” in CHEE 807 at Queen’s University, Kingston, Canada. [Slides]

• (12/14/2019) Presented “Tri-graph Information Propagation for Polypharmacy Side Effect Prediction” at NeurIPS 2019 (Graph Representation Learning Workshop), Vancouver, Canada. [Poster]