Shiyu Wang's homepage
Wang, Shiyu (王诗雨)
Applied Scientist@Salesforce AI Research
Address: 181 Lytton Ave, Palo Alto, CA 94301
Email: shiyu.wang@salesforce.com
[Google Scholar] [LinkedIn] [Curriculum Vitae] [ORCID]
Research interests: generative AI, graph neural networks, recommendation systems, Bayesian statistics, bioinformatics
Shiyu is currently an Applied Scientist at Salesforce AI Research.
Shiyu obtained his Ph.D. in Biostatistics from Emory University, where he was honored to be advised by Dr. Liang Zhao and Dr. Zhaohui (Steve) Qin, working on machine learning and its applications on complex structured data. At Emory, he was fortunate to be a Livingston Fellow, and won the NeurIPS 2022 Scholar Award. He has been serving as the independent reviewer and PC member for many top-tier conferences/journals including NeurIPS, ICLR, ICML, AISTATS, KDD, AAAI, SDM, TKDD and TKDE.
Prior to Emory, Shiyu earned his M.S. from Yale University in 2019, B.S. from Fudan University in 2017. He was also fortunate to intern at Salesforce AI Research and Amazon AWS AI Lab in 2023.
News
[2024-03] One paper of low-resource text-to-data generation is accepted by ICLR workshop on Practical Machine Learning for Low Resource Settings.
[2024-02] I'm starting the role as an Applied Scientist at Salesforce AI Research!
[2024-01] Our survey paper "Controllable Data Generation by Deep Learning: A Review" is accepted by ACM Computing Surveys (impact factor: 16.6)!
[2024-01] Our survey on efficient LLMs is officially out! Welcome to read here: Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models.
[2023-12] One paper of applying LLMs to disease-gene association discovery is accepted by AAAI Workshop on Large Language Models for Biological Discoveries!
[2023-12] I'm thrilled to announce that I will join Salesforce AI Research as an Applied Scientist after PhD graduation!
[2023-06] Our survey paper on healthcare knowledge graph resources, applications, and promises is now accessible online. To be appeared at ICML-IMLH'23.
[2023-04] I'm honored to be awarded Livingston Fellowship Award on "the most promising and best performing PhD student at RSPH".
[2023-03] I will join Amazon AWS AI Lab as an Applied Scientist Intern at Santa Clara in fall 2023!
[2022-11] I will join Salesforce AI Research as a Research Intern at Palo Alto in summer 2023!
[2022-11] One paper is accepted by BIBM 2022 on controllable molecule generation!
[2022-10] I'm honored to be awarded NeurIPS 2022 Scholar Award!
[2022-09] Two papers are accepted by NeurIPS 2022 on periodic graph generation and controllable data generation!
[2022-07] Our systematic survey paper on controllable data generation is available online. This is likely the first survey paper on the important domain of controllable data generation, in which we have proposed the formal problem formulation, a noval taxonomy of texhniques, introduction to the borad spectrum of applications, potential future directions and challenges.
[2022-06] One paper is accepted by DLG-KDD 2022 on controllable molecule generation!
[2022-05] Our GraphGT Data Repository is online, with 30+ attributed datasets & APIs for graph generation and transformation in machine learning!
[2022-05] One paper is accepted by PLOS Pathogens!
[2022-01] I'm honored to contribute to one chapter, Graph Neural Networks: Graph Transformation, of the newly published book Graph Neural Networks: Foundations, Frontiers, and Applications. You can also access various resources about it in our book website.
[2021-08] One paper is accepted by NeurIPS 2021 on the attributed database for graph generation and transformation in machine learning!
[2020-12] I'm honored to be awarded the Patel-Naik Award on the outstanding research proposal regarding celluar structure reconstruction via deep generative models!
[2020-07] I'm honored to be awarded the top performer (first place) in the PhD second-year theory qualifying exam, advanced to PhD candidate!
Working Experiments
Applied Scientist, Salesforce AI Research, Palo Alto, CA (Feb. 2024-present)
Applied Scientist Intern, Amazon AWS AI Lab, Santa Clara, CA (Aug. 2023-Dec. 2023)
Host: Yupeng Gu, Sergül Aydöre, Kousha Kalantari, Branislav Kveton, Anoop Deoras
Research Intern, Salesforce AI Research, Palo Alto, CA (June. 2023-Aug. 2023)
Host: Yihao Feng, Ning Yu, Tian Lan, Yu Bai, Ran Xu, Huan Wang
Selected preprints
Text2Data: Low-Resource Data Generation with Textual Control (intern project at Salesforce AI Research)
Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese
arXiv e-prints, arXiv:2402.10941
Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao
arXiv e-prints, arXiv:2305.11389
Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Joyce Ho, Fei Wang, Carl Yang
arXiv e-prints, arXiv:2306.04802
Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao
arXiv e-prints, arXiv:2310.07683
Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao
arXiv e-prints, arXiv:2401.00625
Selected publications
Shiyu Wang*, Yuanqi Du*, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao.
ACM Computing Surveys (CSUR), impact factor: 16.6
Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao.
Conference on Neural Information Processing Systems (NeurIPS 2022)
Shiyu Wang, Xiaojie Guo, Liang Zhao.
Conference on Neural Information Processing Systems (NeurIPS 2022)
Yuanqi Du*, Shiyu Wang*, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao.
Conference on Neural Information Processing Systems (NeurIPS 2021) Dataset and Benchmark Track 2021
Xiaojie Guo*, Shiyu Wang*, Liang Zhao.
Graph Neural Networks: Foundations, Frontiers, and Applications 12
Jiayu Chang, Shiyu Wang*, Chen Ling, Zhaohui Qin, Liang Zhao.
AAAI 2024 Workshop on Large Language Models for Biological Discoveries
Xingchao He*, Shiyu Wang*, Jiayi Shi, Zhonglin Sun, Zhentian Lei, Zili Yin, Zigang Qian, Huiru Tang, Hui Xie
Frontiers in plant science
Education
Ph.D. in Biostatistics, Emory University (Aug. 2019-Feb 2024)
M.S. in Biostatistics, Yale University (Aug. 2017-May 2019)
B.S. in Pharmaceutical Sciences, Fudan University (Sep. 2013-June. 2017)
Awards
Livingston Fellowship ("To the most promising and best performing PhD student in each department"), Emory University, 2023
NeurIPS Scholar Award, 2022
Patel-Naik Award (awarded to one student in the department for research grant), Emory University, 2020
Top performer (first place) in the second-year theory PhD qualifying exam, 2020
Outstanding Graduate Award, Shanghai City, 2017
Star Graduate, Fudan University (nominated top 20 selected among the undergraduate Class of 2017), 2017
CV Starr Scholarship, 2014
Service
Area chair: R2HCAI-AAAI'23
Program committee member: AAAI-LLMs4Bio'24, SDM'24, SIGKDD'23, SynS & ML-ICML'23, BIOKDD'23, DLG-AAAI'22-23, DLG-KDD'21-22
Independent reviewer: TKDD, TKDE, ECCV'24, ICLR'24, NeurIPS'22-23, ICML'22-24, AISTATS'22-24, SIGKDD'22, AI4D3-NeurIPS'23, MLPS-NeurIPS'22-23
Teaching assistant: Machine Learning (CS 534), Python Programming (BIOS 585); Databases Using SQL (INFO 521); Applied Linear Models (BIOS 509); Introduction to Statistical Inference (QTM 100)