Hans Hao Hsun Hsu
About Hans Hao Hsun Hsu
Hans Hao Hsun Hsu is a Machine Learning Researcher specializing in uncertainty quantification and graph neural networks in AI drug discovery. He has a diverse background in engineering and research, with experience at several institutions including Huawei and Technical University Munich.
Work at Celeris Therapeutics
Hans Hao Hsun Hsu currently works as a Machine Learning Researcher at Celeris Therapeutics. He has been in this role since 2023, contributing to projects that focus on AI drug discovery. His work emphasizes the application of machine learning techniques to enhance the understanding and development of ternary complexes in drug discovery.
Previous Experience in Machine Learning and Engineering
Prior to his role at Celeris Therapeutics, Hsu gained valuable experience in various positions. He worked as a Deep Learning Working Student at Huawei from 2020 to 2021 in Munich, Germany. He also served as a Mechanical Engineer at Delta Electronics, Inc. for one month in 2017 in Taoyuan County, Taiwan. Additionally, he held positions as a Student Research Assistant at the Technical University of Munich from 2019 to 2020 and as a Research Assistant from 2021 to 2022.
Education and Expertise
Hsu holds a Master of Science degree in Robotics, Cognition, and Intelligence from the Technical University of Munich, where he studied from 2019 to 2022. He participated in an exchange program at the University of Stuttgart from 2017 to 2018. Hsu also earned a Bachelor's degree in Mechanical Engineering from National Taiwan University, completing his studies from 2013 to 2018. His expertise includes uncertainty quantification and graph neural networks (GNNs), particularly in the context of AI drug discovery.
Research Background
Hsu's research background includes significant contributions as an Undergraduate Researcher at National Taiwan University from 2016 to 2017 and as a Student Research Assistant at the Technical University of Munich. His focus has been on applying machine learning methodologies to complex problems in drug discovery, specifically targeting ternary complexes.