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Zhi-Qin John Xu, 许志钦 Tenure-track Associate Professor, 副教授 Institute of Natural Sciences, 自然科学研究院 School of Mathematical Sciences, 数学科学学院 Shanghai Jiao Tong University, 上海交通大学 Consultant, AI for Science Institute, Beijing. 北京科学智能研究院, 顾问 |
I work on deep learning and computational neuroscience. I obtained my B.S. in Physics (Zhiyuan College) and a Ph.D. degree in Mathematics from SJTU under the supervision of Profs. David Cai and Douglas Zhou. Before joining SJTU, I worked as a Postdoc at NYUAD and Courant Institute from 2016 to 2019.
New Journal: Journal of Machine Learning
We launched a new journal: Journal of Machine Learning (JML). Welcome to submit papers to JML.
Editor-in-Chief: Prof. Weinan E
Scope: Journal of Machine Learning (JML) publishes high quality research papers in all areas of machine learning, including innovative algorithms of machine learning, theories of machine learning, important applications of machine learning in AI, natural sciences, social sciences, and engineering etc. The journal emphasizes a balanced coverage of both theory and practice. The journal is published in a timely fashion in electronic form.
WHY: Although the world is generally over-populated with journals, the field of machine learning (ML) is one exception. In mathematics, we just do not have a recognized venue (other than conference proceedings) for publishing our work on ML. In AI for Science, ideally, we would like to publish our work in leading scientific journals such as Physical Review Letters. However, this is a difficult task when we are at the stage of developing methodologies. Although there are many conferences in ML-related areas, publishing in journal form is still the preferred venue in many disciplines.
The objective for Journal of Machine Learning (JML) is to become a leading journal in all areas related to ML, including algorithms and theory for ML, as well as applications to science and AI. JML will start as a quarterly publication. Considering the fact that ML is a vast and fast-developing field, we will do our best to carry out a thorough and responsive review process. To this end, we will have a group of young and active managing editors who will handle the review process, and a large, interdisciplinary group of experienced board members who can offer quick opinions and suggest reviewers when needed.
Open access: Yes.
Fee: NO.
Sponsor: Center for Machine Learning Research, Peking University & AI for Science Institute, Beijing
Publisher: Global Science Press, but the editorial board owns the journal.
materials
机器学习联合研讨计划及机器学习与科学应用大会主页: c2sml.cn
A suggested notation for machine learning (通用机器学习符号) published by BAAI (北京智源), see page in github or the page in BAAI
slides at CSIAM 2020
Contact
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更多信息请关注我的公众号 |
Email: xuzhiqin at sjtu dot edu dot cn, Tel: 021-54742760
Office: 326, No.5 Science Building，No. 800 Dongchuan Road，
Shanghai Jiao Tong University，Minhang District, Shanghai
Research Interest
I am interested in understanding deep learning from training process, loss landscape, generalization and application. For example, we found a Frequency Principle (F-Principle) that deep neural networks (DNNs) often capture target functions from low frequency to high frequency in order during the training. The overview paper of frequency principle is now in: ArXiv 2201.07395; we found an embedding principle that the loss landscape of a DNN “contains” all the critical points of all the narrower DNNs.
I am also interested in computational neuroscience, ranging from theoretical study and simulation to data analysis.