谢昌谕教授/博导，于加拿大多伦多大学获得工程物理学士学位、加拿大渥太华大学获得物理博士学位随后在多伦多大学与麻省理工进行理论化学的博士后研究。2018年加入腾讯量子实验室，从事量子计算和AI for Science 的前沿计算技术研究与落地探索。2022年加入浙大药学院，从事前沿计算技术在药物研发的应用研究和理论与算法开发。以一作或通讯身份在《Nature Machine Intelligence》、《Nature Communications》、《Physical Review Letter》、《Npj Quantum Information》、《Chemical Science》、《Journal of Physical Chemistry Letter》、《Journal of Medicinal Chemistry》、《ICML》、 《IJICA》等国际高水平期刊与会议上发表70于篇SCI 论文，获得第24届中国专利奖-银奖。

AI 辅助药物设计 AI for Science 量子计算

Zhenxing Wu, Jike Wang, Hongyan Du, Dejun Jiang, Yu Kang, Dan Li, Peichen Pan, Yafeng Deng, Dongsheng Cao, Chang-Yu Hsieh, Tingjun Hou, Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking, Nature Communications, 2023, 14, 2585. Shuo Liu, Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao; Discrete time crystal enabled by Stark many-body localization; Physical Review Letter, 2023, 130, 120403. Yuquan Li, Chang-Yu Hsieh, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Pengyong li, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, Huanxiang Liu, Shengyu Zhang, Xiaojun Yang, An adaptive graph learning method for automated molecular interactions and properties predictions, Nature Machine Intelligence, 2022, 4, 645. Yue Wan, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang, Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer, ICML, 2022 , Accepted. (CCF-A top conference) Yu-Qin Chen, Yu Chen, Chee-Kong Lee, Shengyu Zhang, Chang-Yu Hsieh, Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks, Nature Machine Intelligence, 2022, 4, 269. Shi-Xin Zhang, Zhou-Quan Wan, Chee-Kong Lee, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao, Variational quantum-neural hybrid eigensolver, Physical Review Letters, 2022, 128, 120502. Ziyi Yang, Zhaofeng Ye, Yijia Xiao, Chang-Yu Hsieh, Shengyu Zhang, SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance, Briefings in Bioinformatics, 2022, bbac050. Zhenxing Wu, Dejun Jiang, Jike Wang, Xujun Zhang, Hongyan Du, Lurong Pan, Chang-Yu Hsieh, Dongsheng Cao, Tingjun Hou, Knowledge-based BERT: a method to extract molecular features like computational chemists, Briefings in Bioinformatics, 2022, bbac131. Qing Ye, Chang-Yu Hsieh, Ziyi Yang, Yu Kang, Jiming Chen, Dongsheng Cao, Shibo He, Tingjun Hou, A unified drug-target interaction prediction framework based on knowledge graph and recommendation system, Nature Communications, 2021, 12, 6775. Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, Dongsheng Cao, Xi Chen, Tingjun Hou, Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning, Nature Machine Intelligence, 2021, 3, 914. Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu, Dongsheng Cao, Tingjun Hou, InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions, Journal of Medicinal Chemistry, 2021, 64, 18209. Chang-Yu Hsieh, Qiming Sun, Shengyu Zhang, Chee Kong Lee, Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations, NPJ Quantum Information, 2021, 7, 1. Zhenxing Wu, Dejun Jiang, Jike Wang, Chang-Yu Hsieh, Dongsheng Cao, Tingjun Hou, Mining toxicity information from large amounts of toxicity data, Journal of Medicinal Chemistry, 2021, 64, 6924. Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yao, RetroPrime: A Diverse, plausible and Transformer-based method for single-step retrosynthesis predictions, Chemical Engineering Journal, 2021, 420, 129845. Jonathan Allcock, Chang-Yu Hsieh, A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time, Quantum, 2020, 4, 342.