Project 2: Machine Learning and Causal Inference to Optimize Genomic Interventions using Disease State Representations

Bowen Fan

ETH Zürich
Advisor: Karsten Borgwardt

Project description

The human genome contains a torrent of information that gives clues not only about human origin, evolution, biological function, but also diseases. The goal of my project aims at developing novel machine learning techniques to better understand the complex genomic data and also other forms of data that can represent patient diseases. Towards this goal, we may be able to reveal more about disease mechanisms and therapy outcomes, which therefore shed new lights on the findings for personalized medicine and healthcare for each patient.


  1. Including network models in the search for patient state representations
    Host: University of Liege
    Planned date: November 2020 – January 2021 Postponed due to COVID
  2. Deep Learning for patient state representation
    Host: Pharmatics
    Planned date: December 2021 – February 2022