Fangzhou Mu (穆方舟)

Department of Computer Sciences
University of Wisconsin-Madison
CV | GitHub | LinkedIn

I am a 2nd-year Computer Sciences graduate student at the University of Wisconsin-Madison, working with Prof. Yin Li. My research interest lies in computer vision and deep learning. My current focus is on visual representation learning with deep generative models. I am also interested in deep learning for geometric vision. Previously, I worked with Prof. Anthony Gitter on an educational project for teaching biologists machine learning.

I was a molecular biologist and have a peripheral interest in bioinformatics. I received my MS in Pharmaceutical Sciences from UW-Madison in 2018, under the supervision of Prof. Michael Taylor. My research was on understanding the development of blood-brain barrier in zebrafish. Before that, I completed my BS in Biological Sciences at Zhejiang University in 2014. My undergraduate research was on marine natural product chemistry and cancer cell biology.



ML4BIO: Machine Learning for Biologists

ML4Bio is a Python package with graphical interface for introducing machine-learning concepts to biologists in a workshop format. It focuses on classification and guides users through training, evaluation and inference phases. The workshop includes example datasets drawn from various biological domains and tutorials on the machine-learning workflow and selected classifiers.


Modeling Analysis of Potential Target of Dolastatin 16 by Computational Virtual Screening
Chemical and Pharmaceutical Bulletin
Tingting Liang, Qi Zhao, Shan He, Fangzhou Mu, Wei Deng, Bingnan Han
CNS angiogenesis and barriergenesis occur simultaneously
Developmental Biology
Robyn Umans, Hannah Henson, Fangzhou Mu, Chaithanyarani Parupalli, Bensheng Ju, Jennifer Peters, Kevin Lanham, Jessica Plavicki, Michael Taylor

Teaching & Coursework

Graduate Teaching Assistant

  • BMI/CS 776 Advanced Bioinformatics (Spring 2018, 2019)
  • BMI/CS 576 Introduction to Bioinformatics (Fall 2018)
  • PHMSCI 540 Drug Delivery Systems (Fall 2017)
  • PHMSCI 522 Pharmacology II (Spring 2014, 2018)

Graduate Courses in CS

  • CS 726 Nonlinear Optimization I (Michael Ferris)
  • CS 730 Nonlinear Optimization II (Steve Wright)
  • CS 760 Machine Learning (Mark Craven)
  • CS 761 Advanced Machine Learning (Jerry Zhu)
  • CS 766 Computer Vision (Mohit Gupta)
  • BMI/CS 776 Advanced Bioinformatics (Anthony Gitter)
  • CS 838 Data Science: Principles, Algorithms and Applications (AnHai Doan)
  • BMI 826/CS 838 Learning Based Methods in Computer Vision (Yin Li)