About Me

I am a tenure-track Assistant Professor (百人计划研究员) at Zhejiang University. Prior to that, I was a Senior Researcher at Microsoft Research Asia. I received my Ph.D. in Computer Science in Summer 2024 from the University of Utah. I earned my B.S. in both Statistics and Computer Science (double degree) from the School of the Gifted Young (少年班学院) at the University of Science and Technology of China (USTC) in 2018, and my Master’s in Computer Science from Temple University in 2019.

Research Interests

• Bayesian Methods and Generative Models
• Scientific Machine Learning for Physical Simu. & Signal Process
• Collaborative Multi-Agents
• Tensor Learning (flexible low-rank representation for high-order data)
• Advanced Time Series (High-Dim Tensor Time Series, Spatial-Temporal data)

Industry & Application

• AI4Science: I’ve been involved in several research and start-up projects on AI + medical/bio scenarios. I own two first-author patents in AIDD (AI for Drug Discovery), which contribute as core patents for a start-up that has raised over $10M+.

• AI4Finance: I have quite experiences of AI-based quantitative trading/pricing strategy development during the internship/competition in top hedge fund and investment bank (Morgan Stanley, World Quant and UBS etc.). During the Microsoft I’m a core contributor of MarS, an open-source financial market simulation engine powered by generative foundation Model.

• Others: I am a dedicated writer to introduce Bayesian machine learning (in Chinese) with 15,000 followers on Zhihu (知乎) — Chinese Quora. See my Zhihu page.

News

• Sep. 2025: Two Papers Accepted by NeurIPS 2025! Great Congratulations to my interns: Panqi and Yifan to get their first NeurIPS!
• Sep. 2025: I will join Zhejiang University as a tenure-track Assistant Professor（百人计划研究员） in 2025 fall!
• May 2025: We release R&D-Agent , which is verified by OpenAI-MLE-Bench! as Best MLE Agent ! !
• Spring 2025: Three Papers accepted by KDD 2025, ICLR 2025 and JASA!
• May 2024: I will join Microsoft Research Asia as a Senior Researcher in 2024 summer!

Selected Publications

Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
Panqi Chen, Yifan Sun, Lei Cheng, Yang Yang, Weichang Li, Yang Liu, Weiqing Liu, Jiang Bian, Shikai Fang†
NeurIPS 2025 [arXiv] [code]
TL;DR: [GenAI & AI4Phy] Generation as reconstruction and forecasting for off-grid sparse physical dynamics

CATTE :Functional Complexity-adaptive Temporal Tensor Decomposition
Panqi Chen, Lei Cheng, Jianlong Li, Weichang Li, Weiqing Liu, Jiang Bian, Shikai Fang†
NeurIPS 2025 [arXiv] [code]
TL;DR: [Tensor & Time Series] Rank-adaptive trajectory learning for physical tensor time series

R&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution
Xu Yang, Xiao Yang, Shikai Fang, Bowen Xian, Yuante Li, Jian Wang, Minrui Xu, Haoran Pan, Xinpeng Hong, Weiqing Liu, Yelong Shen, Weizhu Chen, Jiang Bian
[arXiv] [code] [公众号]
TL;DR: [LLM Agent] AI-driven AI — Autonomous R&D Evolving Agents .

MarS: A Financial Market Simulation Engine Powered by Generative Foundation Model
Junjie Li*, Yang Liu*, Weiqing Liu†, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian
ICLR 2025
[arXiv] [code] [Page] [公众号]
TL;DR: [Finance & Foundation Model] Large Market Model for generative simulation of financial market.

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun
ICML 2024 Spotlight! (top 3%)
[arXiv] [code] [slides] [知乎]
TL;DR: [Time Series & Bayesian] Efficient online imputation for functional multivariate time series.

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Shikai Fang*, Madison Cooley*, Da Long*, Shibo Li, Robert Kirby, Shandian Zhe
ICLR 2024 [arXiv] [code] [slides] [知乎]
TL;DR: [AI4Phy & Bayesian] Gausisan Process based surrogate model for High-freq. PDE

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor
Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe,
ICLR 2024 [arXiv] [code] [slides]
TL;DR: [Tensor & AI4Phy] Functional Tucker Decomposition for Physical Signal reconstruction

Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Robert Kirby, Shandian Zhe
NeurIPS 2023
[arXiv] [code] [slides]
TL;DR: [Tensor & Time Series] Online Trajectory Learning for Tensor-Valued Time Series

Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
Zheng Wang*, Shikai Fang*, Shibo Li, Shandian Zhe
NeurIPS 2023 Spotlight! (top 10%) [arXiv] [code]
TL;DR:[Tensor & Time Series] Temporal Tensor <=> Diffusion-Reaction Process on Hypergraph

Provably Convergent Schrodinger Bridge with Applications to Probabilistic Time Series Imputation
Yu Chen*, Wei Deng*,Shikai Fang*, Fengpei Li*, Tianjiao Nicole Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, and Yuriy Nevmyvaka,
ICML 2023 [arXiv] [code]
TL;DR: [Generative & Time Series] Schrodinger Bridge for Time Series Imputation

Bayesian Continuous-Time Tucker Decomposition
Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe
ICML 2022 Oral! (top 2%) [paper] [code] [slides]
TL;DR: [Tensor & Time Series] Continuous-Time Tucker Model for Tensor-Valued Time Series

Streaming Bayesian Deep Tensor Factorization
Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, and Shandian Zhe
ICML 2021 [paper] [code] [supp]
TL;DR: [Tensor & Online learning] Bayesian deep learning + Tensor

Bayesian Streaming Sparse Tucker Decomposition
Shikai Fang, Robert. M. Kirby, and Shandian Zhe
UAI 2021 [paper] [code] [supp]
TL;DR: [Tensor & Online learning] Sparse Tucker + Online Learning