Zhiliang Wu | 吴志良

LMU Munich. Siemens AG.

Munich, Germany

From 2018 to 2022, I was a Ph.D. student under the supervision of Prof. Dr. Volker Tresp. The work was generously supported by the MLwin project at LMU Munich and Siemens AG.

My research focuses on machine learning and deep learning in healthcare applications. We are especially interested in developing uncertainty-aware clinical decision support to address the challenges in the high-dimensional longitudinal Electronic Health Record (EHR) data.

Before joining LMU Munich & Siemens AG, I received my master’s degree in Electrical Engineering and Information Technology from the Technical University of Munich in 2018. I spent six months at Mercateo AG to conduct my master’s thesis about Recommender Systems under the supervision of PD. Dr. Martin Kleinsteuber.

news

Mar 22, 2022 I have successfully defended my thesis “Representation Learning for Uncertainty-Aware Clinical Decision Support”!
Jun 5, 2021 Our paper about uncertainty-aware Time-to-event Prediction is accepted to MLHC 2021!
May 1, 2021 Our paper about Deep Kernel Learning on medical imaging data got accepted to ICHI 2021!
Dec 1, 2020 Our paper received the best paper award at the ICHI 2020!
Nov 30, 2020 My first collaboration work with Jindong was presented at ACCV2020!
Jun 10, 2019 My first collaboration work with Yinchong and Volker got published at ICHI 2019!
Nov 1, 2018 I started my Ph.D.!!!

selected publications

  1. ICHI2020
    Best Paper Award
    Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
    Wu, Zhiliang, Yang, Yinchong, Ma, Yunpu, Liu, Yushan, Zhao, Rui, Moor, Michael, and Tresp, Volker
    In 2020 IEEE International Conference on Healthcare Informatics (ICHI) 2020
    arXiv PDF Code
  2. ICHI2021
    Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
    Wu, Zhiliang, Yang, Yinchong, Gu, Jindong, and Tresp, Volker
    In 2021 IEEE International Conference on Healthcare Informatics (ICHI) 2021
    arXiv PDF Code
  3. MLHC2021
    Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
    Wu, Zhiliang, Yang, Yinchong, Fasching, Peter A., and Tresp, Volker
    In Machine Learning for Healthcare Conference 2021
    arXiv PDF Code
  4. Dissertation
    Representation Learning for Uncertainty-Aware Clinical Decision Support
    Wu, Zhiliang
    In Elektronische Hochschulschriften der LMU München 2022
    PDF