2026 International Conference on Frontiers of Data Science
作者：
时间：2026-01-09
阅读量：14375次
- 时间：May 15 -- May 17, 2026
- 地点：Xixi Hotel(杭州西溪宾馆)
- 主办单位：Center for Data Science, Zhejiang University
2026 International Conference on Frontiers of Data Science will be held in Hangzhou (中国杭州) from May 15 to May 17, 2026. Center for Data Science of Zhejiang University was established in May 2017, with the aim of promoting the theory and applications of data science. Internationally renowned experts in data science will deliver keynote speeches and invited talks during this conference, highlighting major theoretical breakthroughs, displaying the latest advances in technology innovation and applications, and exploring opportunities and challenges for development in data science.
Theme: Statistical Inference in the Age of AI
Date: Registration on May 15, 2026
Conference on May 16 -- May 17, 2026;
Venue: Conference & Registration at Hangzhou Xixi Hotel (杭州西溪宾馆)
Organized by the Center for Data Science, Zhejiang University
Operated by Hangzhou Qizhen Exhibition Service Co., Ltd
1. Committee
Advisory Committee
Jianfei Cai | Monash University |
Tony Cai
| University of Pennsylvania
|
Lu Tian | Stanford University (Chair) |
Yazhen Wang | University of Wisconsin-Madison |
Ming Yuan | Columbia University |
Heping Zhang | Yale University |
Local Organizing Committee
| Jia Gu | Zhejiang University
|
Zijian Guo | Zhejiang University (Chair) |
Wenguang Sun | Zhejiang University |
Xintao Xia | Zhejiang University |
2. Important Dates
- Early Bird Deadline: March 31, 2026
- On-Site Registration Deadline: May 15, 2026
- Hotel Reservation Deadline: May 7, 2026
3. Registration
Registration Method: Please visit the conference website (https://icfds2026.scimeeting.cn/) to register online. You may click on the "Individual Registration" button on the Homepage or at the bottom of this page. Please ensure that all registration information is filled out accurately.
4. Keynote Speech
Jun Liu
Tsinghua University
Bio: Dr. Jun Liu is Xinghua Distinguished University Professor, Chair of the Department of Statistics and Data Science at Tsinghua University, and a member of the National Academy of Sciences of the USA. He received his BS degree in mathematics in 1985 from Peking University and Ph.D in statistics in 1991 from the University of Chicago. From 1991-2025, he held Assistant, Associate, and Full professorship at Harvard and Stanford Universities. Liu won the COPSS Presidents' Award in 2002, the Morningside Gold Medal in Applied Mathematics in 2010, and the Pao-Lu Hsu Award by ICSA in 2016. He was elected to Fellow of IMS, ASA, and ISCB in 2004, 2005, and 2022, respectively, and to the National Academy of Sciences of the USA in 2025. Liu has served as the co-editor for the flagship statistics journal JASA from 2011-2014, as associate editor for leading statistical journals, and as a committee chair or member for various grant review panels. Dr. Liu has co-authored over 300 research articles published in leading scientific journals, conferences and books, with a Google Scholar citation count of more than 97,000. Over the past four decades, he has mentored more than 40 PhD students and 30 postdoctoral fellows. Liu’s research interests are: Bayesian methods and computation, statistical machine learning and AI, Monte Carlo methods, state-space models, bioinformatics and computational biology.
Title: Conditional sampling via diffusion flow and SMC
Abstract: Sequential Monte Carlo, aka particle filtering, refers to a class of Monte Carlo methods that accommodates dynamic structures and can be used as a learning mechanism. The scheme starts by creating the sampling distribution recursively and adjusting the obtained samples by sequentially adjusted weights so as to “learn” when new information is available. Recently, diffusion models have become a very popular tool for learning a high-dimensional data-distribution and generating from it. I will review a brief history of SMC and some developments in diffusion modeling. By combining the ODE-based flow method and SMC, we propose a training-free conditional sampling method for diffusion models. Because a naive application of importance sampling suffers from weight degeneracy in high-dimensional settings, ideas of resampling and rejection sampling are necessary. To encourage generated samples to diverge along distinct trajectories, we derive a stochastic flow with adjustable noise strength to replace the deterministic flow at the intermediate stage. Experimentally, our method significantly outperforms existing approaches on conditional sampling tasks for MNIST and CIFAR-10.
Richard J. Samworth
University of Cambridge
Bio: Richard Samworth obtained his PhD in Statistics from the University of Cambridge in 2004, and has remained in Cambridge since, becoming a full professor in 2013 and the Professor of Statistical Science in 2017. His main research interests are in nonparametric and high-dimensional statistics, as well as the statistical foundations of AI; he has developed methods and theory for shape-constrained inference, missing data, subgroup selection, deep learning, data perturbation techniques, changepoint estimation, variable selection and independence testing. Richard received the COPSS Presidents' Award in 2018, was elected as a Fellow of the Royal Society in 2021 and was awarded the David Cox Medal for Statistics in 2025. He served as co-editor of the Annals of Statistics (2019-2021) and is currently IMS President-Elect.
Title: Outrigger local polynomial regression
Abstract: Standard local polynomial estimators of a nonparametric regression function employ a weighted least squares loss function that is tailored to the setting of homoscedastic Gaussian errors. We introduce the outrigger local polynomial estimator, which is designed to achieve distributional adaptivity across different conditional error distributions. It modifies a standard local polynomial estimator by employing an estimate of the conditional score function of the errors and an 'outrigger' that draws on the data in a broader local window to stabilise the influence of the conditional score estimate. Subject to smoothness and moment conditions, and only requiring consistency of the conditional score estimate, we first establish that even under the least favourable settings for the outrigger estimator, the asymptotic ratio of the worst-case local risks of the two estimators is at most 1, with equality if and only if the conditional error distribution is Gaussian. Moreover, we prove that the outrigger estimator is minimax optimal over Hölder classes up to a multiplicative factor $A_{\beta,d}$, depending only on the smoothness $\beta \in (0,\infty)$ of the regression function and the dimension $d$ of the covariates. When $\beta \in (0,1]$, we find that $A_{\beta,d} \leq 1.69$, with $\lim_{\beta \searrow 0} A_{\beta,d} =1$. A further attraction of our proposal is that we do not require structural assumptions such as independence of errors and covariates, or symmetry of the conditional error distribution. Numerical results on simulated and real data validate our theoretical findings; our methodology is implemented in the R package \texttt{outrigger}.
5. Accommodation
Xixi Hotel(杭州西溪宾馆)
Hangzhou Xixi Hotel is located in the northwest corner of Xixi wetland. The grand and elegant hotel main building is built in the forest. The rooms are designed to be warm and elegant, eqquiped with a variety of comfortable and pleasant facilities, and the view of the West River outside the window is pure and unworldly. Whether indoors or outdoors, guests can relax in the stream, grass, and trees of Xixi.
The designated hotel for the conference is Hangzhou Xixi Hotel. The accommodation fee is by yourself. The number of rooms is limited. Please book online before May 7 after registration. If you want to extend your stay, please contact qizhenhz@zju.edu.cn.
6. Contact
Academic Contact:
Su, Weina (Zhejiang University)
Tel: +86-0571-88208268
E-mail: suweina@zju.edu.cn
Meeting Contact(Hotel Reservation):
Zhan, Yanmin (Qizhen Exhibition)
Tel: +86-0571-88177983 18814880579
E-mail: qizhenhz@zjuyh.com
Financial consulting (payment enquiries, invoice services, etc.):
Cheng, Wenxiu(Qizhen Exhibition)
Tel: 15990153584
E-mail:qizhenhz@zjuyh.com
Welcome！
Center for Data Science, Zhejiang University
