Hi, this is Shuai Liu (刘帅）, a second-year Phd student at Nanyang Technological University (NTU) in Singapore. I am under the supervision of Professor Cong Gao. My research focus is on spatial-temporal data mining, artificial intelligence, and machine learning. Before joining NTU, I completed my Bachelor of Science degree in computer science at Peking University in China, under the guidance of Professor Guojie Song.
♥ News
My paper ‘Multivariate Time-series imputation with disentangled representations’ has been accepted to ICLR 2023.
📝 Publications
[SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting]
Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F. Lentzakis, Gao Cong
We present a Scaleable Adaptive Graph Diffusion Forecasting Network (SAGDFN) to capture complex spatial-temporal correlation for large- scale multivariate time series and thereby, leading to exceptional performance in multivariate time series forecasting tasks. The proposed SAGDFN is scalable to datasets of thousands of nodes without the need of prior knowledge of spatial correlation.
[Multivariate Time-series imputation with disentangled representations]
SHUAI LIU, XIUCHENG LI, GAO CONG, YILE CHEN, YUE JIANG
We propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. The learned disen- tanglement makes the imputation process more reliable and offers explainability for imputation results.
[Real-time Transportation Prediction Correction using Reconstruction Error in Deep Learning, TKDD 2020]
SHUAI LIU, GUOJIE SONG, WENHAO HUANG
We propose traffic prediction correction strategy using the reconstruction error in the deep neural network. The reconstruction error can reflect the model’s ability on feature representation and then determine the fitness of an input data to the model. We propose two mechanisms of real-time prediction correction using this reconstruction error: the data driven approach and the model-driven approach.
[高科西路浦东南路口交通优化设计, 2016 上海市青少年人文社会科学论文大赛一等奖 ]
刘帅, 谢欣
论文采样了上海浦东新区高科西路浦东南路路口的实地车辆通行数据，通过计算分析道路平面立交道口通行能力影响系数，调整路口红绿灯时长以及路口车道分布情况。论文的研究结果已经被成功应用于上述路口。
🎖 Honors and Awards
1, 1st prize, Humanity, social & science innovation paper competition in the teen-group of Shanghai, 2016
2, Outstanding student of the junior league school in PKU, 2017
3, Outstanding student leader of EECS, PKU, 2018
4, Outstanding student leader of PKU, 2018
5, 1st prize, Data Analysis Knowledge Contest for Chinese Univeristy Students, 2022
6, Sophisticated student leader, Deecamp 2022, 2022
7, Prompt Engineer, assessed by Datawhale & iFLYTEK SPARK, 2024
📖 Educations
Nanyang Technological University, SCSE, PHD (2021.07~present)
Peking University,EECS, undergraduate student (2016.09-2020.06)
Shanghai Experimental school, middle school student (2010.09~2016.06)
Shanghai Experimental school, elementary school student (2006.09~2010.06)
💻 Internships
Deecamp 2022, virtual activity, group leader (2022.06-2022.08)
Baidu, BIL, research assistant (2020.08-2021.07)
USC, Melady lab, research assistant (2019.06-2019.09)
※ Other Activities
1, Chair of Student Union in EECS, PKU, 2018
2, Group Member in Deecamp 2020 (AI for generating PRG games), 2020
3, Teaching Assistant for CZ 4032 Data Mining, NTU, 2021
4, Team Leader in Deecamp 2022 (AI in accurate spine segmentation), 2022
※ Reviewers
External Reviewer AAAI、IJCAI、KDD、VLDB、PAKDD、ICLR、