Ph.D. Student in Computer Science
Scientific Computing and Imaging Institute
School of Computing, University of Utah
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Email: shikai.fang at utah.edu
I am on job market now! I'm open for research positions in both academy and industry!
I am a fifth-year Ph.D. candidate, coadvised by Prof. Mike Kirby and Prof. Shandian Zhe in both SCI Institute and School of Computing, University of Utah.
I received my B.S. in both Statistics and Computer Science(as double degree) from School of the gifted young(少年班学院), University of Science and Technology of China(USTC) in 2018, and Master of in Computer Science from Temple University in 2019. I also spent time at ISTBI of Fudan University to play with computational neuroscience.
My research interests include but are not limited to Bayesian machine learning, Tesnor learning, Sparse learning , Streaming & Sequence inference and Physical-infomed machine learning(ODE/PDE/SDE/physical-prior + AI models, which is the cricial part of AI4Science ).
On the application side, 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 Discover), which contribute as core patents for a start-up that has raised over $10M+. Another first-author patent in AI-based fast-clinical-diagnosis for prostate cancer is in process of application.
I also 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. ). Although I'm not with such hungry and sharpness required for a top trader, the efficient feedback-loop from the real market is so attractive to me.
I am a dedicated writer to introduce Bayesion machine learning(in Chinese) with more than 10000 followers on Zhihu (知乎) — Chinese Quora. See my Zhihu page.
I am also a half-professional nature photographer,but on the highway to be full-professional :). See my photos at 500px page .
Zheng Wang, Shibo Li,Shikai Fang, Shandian Zhe, "Diffusion-Generative Multi-Fidelity Learning for Physical Simulation"[arxiv]
(*: Equal contribution)
13. Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun, "BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition", The 41st International Conference on Machine Learning(ICML 2024)[arxiv]
12. Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe, "Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor",The 12th International Conference on Learning Representations(ICLR 2024) [PDF][Code]
11. Shikai Fang*, Madison Cooley*, Da Long*, Shibo Li, Robert Kirby, Shandian Zhe, "Solving High Frequency and Multi-Scale PDEs with Gaussian Processes", ,The 12th International Conference on Learning Representations(ICLR 2024) [PDF][Code]
10. Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Robert Kirby, Shandian Zhe , “Streaming Factor Trajectory Learning for Temporal Tensor Decomposition ”, The 37th Conference on Neural Information Processing Systems (Neurips 2023) [PDF][Code][Poster][Slides]
9. Zheng Wang*, Shikai Fang*, Shibo Li, Shandian Zhe, “Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes ”,The 37th Conference on Neural Information Processing Systems (Neurips 2023) [accepted as a spotlight! (top 10%)] [PDF][Code][Poster]
8. Shikai Fang , Shandian Zhe, Hui-Ming Lin, Arun A. Azad , Heidi Fettke , Edmond M Kwan , Lisa Horvath , Blossom Mak, Tiantian Zheng, Pan Du, Shidong Jia, Robert M. Kirby, Manish Kohli MD, "Multi-Omic Integration of Blood-Based Tumor Associated Genomic And Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer", JCO Clinical Cancer Informatics (JCO CCI) [most read paper on JCO CCI!] [Page][PDF][Supp]
7. Yu Chen*, Wei Deng*, Shikai Fang*, Fengpei Li*, Tianjiao Nicole Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, and Yuriy Nevmyvaka, “Provably Convergent Schrodinger Bridge with Applications to Probabilistic Time Series Imputation”, The 40th International Conference on Machine Learning (ICML 2023)[PDF][Code]
6. Shikai Fang, Akil Narayan, Robert Kirby, and Shandian Zhe, “Bayesian Continuous-Time Tucker Decomposition ”, The 39 International Conference on Machine Learning (ICML 2022) [accepted as a long presentation! (top 2%)] [PDF][Code][Slides][Poster][Video]
5. Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, and Shandian Zhe, “Streaming Bayesian Deep Tensor Factorization”, The 38th International Conference on Machine Learning (ICML 2021) [PDF][Code][Supp][Video][Slides].
4. Shikai Fang, Robert. M. Kirby, and Shandian Zhe, “Bayesian Streaming Sparse Tucker Decomposition ”, The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021)[PDF][Code][Slides][Supp].
3. Shikai Fang, Shandian Zhe, Kuang-chih Lee, Kai Zhang, and Jennifer Neville, “ Online Bayesian Sparse Learning with Spike and Slab Priors ”, IEEE International Conference on Data Mining (ICDM 2020) [PDF][Code][Slides].
2. Conor Tillinghast, Shikai Fang, Kai Zhang, and Shandian Zhe, “ Probabilistic Neural-Kernel Tensor Decomposition ”, IEEE International Conference on Data Mining (ICDM 2020)[PDF][Code] [Slides].
1. Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, and Qingyao Ai, “ Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank ”, Proceedings of the 29th ACM International Conference on Information and Knowledge Management(CIKM 2020)[PDF][Video]
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