Xinming Tu

Univerisity of Washington. Seattle.

prof_pic_PKU.jpg

Weiming Lake, Bo Ya Ta, Peking University

I am a second year PhD student at the Paul Allen School of Computer Science and Engineering at University of Washington supervised by Sara Mostafavi. Before my PhD, I earned BSs in Biology and Computer Science from Peking University. During my undergraduate, I did some bioinformatics research at Gao Lab. In addition, I also worked as a summer intern at Zhao Lab at Yale University and Sheng Lab at the University of Washington. I also spent six months on ML Group of Microsoft Research Asia as a Full-time research Intern. Please see my CV for more information.

My research interest is using machine learning to decipher the principle of genomics. Recently my research focused on regulatory genomics under the single-cell context, including sequence to function, multi-omics integration, perturbation modeling.

news

Feb 7, 2023 I posted my third blog - AI for General Science - Large language models for scientific hypothesis/research ideas generation
Jan 5, 2023 This summer, I will join in the Aviv Regev’s Group at Genentech as a Research Intern, work with Romain Lopez.
Sep 14, 2022 CLUE is accepted to NeurIPS 2022 as Oral presentation! See you in New Orleans!
Jun 30, 2022 I posted my second blog - Collection of some resources related to the PhD journey
Feb 16, 2022 I gave a talk about our CLUE method on the 9th Winter q-bio conference!

latest posts

selected publications

2024

  1. A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data
    Xinming Tu, Jan-Christian Hutter, Zitong Jerry Wang, Takamasa Kudo, Aviv Regev, and Romain Lopez
    bioRxiv, 2024

2023

  1. HDSR
    What Should Data Science Education Do with Large Language Models?
    Xinming Tu, James Zou, Weijie J. Su†, and Linjun Zhang†
    Harvard Data Science Review, 2023

2022

  1. Cross-Linked Unified Embedding for cross-modality representation learning
    Xinming Tu*, Zhi-Jie Cao*, Chen-Rui Xia, Sara Mostafavi†, and Ge Gao†
    NeurIPS(Oral), 2022

2019

  1. Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding
    Xiao Luo*, Xinming Tu*, Yang Ding, Ge Gao†, and Minghua Deng†
    Bioinformatics, 2019