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.
|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.!!!|
Best Paper AwardLearning Individualized Treatment Rules with Estimated Translated Inverse Propensity ScoreIn 2020 IEEE International Conference on Healthcare Informatics (ICHI) 2020
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel LearningIn 2021 IEEE International Conference on Healthcare Informatics (ICHI) 2021
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time ModelsIn Machine Learning for Healthcare Conference 2021
Representation Learning for Uncertainty-Aware Clinical Decision SupportIn Elektronische Hochschulschriften der LMU München 2022