Shiyu is currently an Applied Scientist at Salesforce AI Research. He 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. 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

• [2024-12] One paper for low-resource text-to-data generation is accepted by AAAI 2025!

• [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

• Cross-tissue Graph Attention Networks for Semi-supervised Gene Expression Prediction [project page]

Shiyu Wang, Mengyu He, Muran Qin, Yijuan Hu, Liang Zhao, Zhaohui Qin

bioRxiv e-prints

• Domain Generalization Deep Graph Transformation [project page]

Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao

arXiv e-prints, arXiv:2305.11389

• A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

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

• Controllable Data Generation Via Iterative Data-Property Mutual Mappings

Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao

arXiv e-prints, arXiv:2310.07683

• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

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

• Controllable Data Generation by Deep Learning: A Review [project page]

Shiyu Wang*, Yuanqi Du*, Xiaojie Guo, Bo Pan, Zhaohui Qin, Liang Zhao.

ACM Computing Surveys (CSUR), impact factor: 23.8

• 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

Association for the Advancement of Artificial Intelligence (AAAI 2025)

• Multi-objective Deep Data Generation with Correlated Property Control [project page]

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) 

• Deep Generative Model for Periodic Graphs [project page] 

Shiyu Wang, Xiaojie Guo, Liang Zhao.

Conference on Neural Information Processing Systems (NeurIPS 2022) 

• GraphGT: Machine Learning Datasets for Graph Generation and Transformation [project page]

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

• Graph Neural Networks: Graph Transformation [project page]

Xiaojie Guo*, Shiyu Wang*, Liang Zhao. 

Graph Neural Networks: Foundations, Frontiers, and Applications 12

• Gene-associated Disease Discovery Powered by Large Language Models

Jiayu Chang, Shiyu Wang*, Chen Ling, Zhaohui Qin, Liang Zhao. 

AAAI 2024 Workshop on Large Language Models for Biological Discoveries

• Genotypic and Environmental Effects on the Volatile Chemotype of Valeriana jatamansi Jones

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)