- 时间:July 8 -- July 10, 2024
- 地点:Xixi Hotel(杭州西溪宾馆)
- 主办单位:Center for Data Science, Zhejiang University
2024 International Conference on Frontiers of Data Science will be held in Hangzhou (中国杭州) from July 8 to July 10, 2024. 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: Raising the Impact of Data Science Research: from Theory to Practice
Date: Registration on July 8, 2024 (10:00 – 21:00) *Dinner will be provided before 8 p.m. to those paid registration fee.
Short Course on July 8, 2024 (13:00-17:30)
Conference on July 9 -- July 10, 2024;
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. Program Committee
Tianxi Cai | Harvard University (Chair) |
Rui Duan | Harvard University |
Zijian Guo | Rutgers University |
Boris Hejblum | University of Bordeaux |
Junwei Lu | Harvard University |
Layla Parast | University of Texas at Austin |
Advisory Committee
Jianfei Cai | Monash University |
Tianxi Cai | Harvard University |
Tony Cai | University of Pennsylvania(Chair) |
Lu Tian | Stanford University |
Yazhen Wang | University of Wisconsin-Madison |
Ming Yuan | Columbia University |
Heping Zhang | Yale University |
Local Organizing Committee
Yifan Cui | Zhejiang University |
Wei Luo | Zhejiang University |
Xiaoye Miao | Zhejiang University |
Andre Python | Zhejiang University (Chair) |
Wenguang Sun | Zhejiang University |
2. Important Dates
- Early Bird Deadline: May 15th, 2024
- Abstract Submission Deadline: June 25th, 2024
- Hotel Reservation Deadline: July 1st, 2024
3.Program
(Updated on July 1st)
Program | ||||||
July 8th, Monday | ||||||
10:00-21:00 | Registration | Xixi Hotel | ||||
13:00-15:00 | Short Course: Federated Learning | Meizhu Hall (梅竹厅) | Instructor:: Rui Duan, Harvard T.H. Chan School of Public Health | |||
15:30-17:30 | Short Course: Data Visualization and Reproducibility | Meizhu Hall (梅竹厅) | Instructor: Kimberly Zhang, Microsoft Inc, Ming Yang, SZMS Technology, Tim CD Lucas, University of Leicester | |||
July 9th, Tuesday | ||||||
8:30-8:40 | Opening Ceremony | Xixi Hall (西溪厅) | Chair: Andre Python | |||
8:40-9:30 | Keynote Speech Peter L. Bühlmann | Title:Causality-inspired Statistical Machine Learning | Xixi Hall (西溪厅) | Chair: Tony Cai | ||
9:30-10:20 | Keynote Speech Huazhen Lin | Title: Deep Regression Learning with Optimal Loss Function | Xixi Hall (西溪厅) | Chair: Wenguang Sun | ||
10:20-11:00 | Tea Break | |||||
11:00 - 12:40 | Emerging Challenges and Innovations in Causal Inference | Statistical Analysis on Complex Data | Recent Advances in Transfer Learning, Federated Learning and Causal Discovery | Recent developments in large-scale and high-dimensional inference | Analysis of High-dimensional and High-order Data | Statistical learning meets causal inference: Modern theory and methods |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Rui Duan | Chair: Heping Zhang | Chair: Yang Ning | Chair: Wenguang Sun | Chair: Anru Zhang | Chair: Larry Han | |
Organizer: Rui Duan | Organizer: Heping Zhang | Organizer: Yang Ning | Organizer: Wenguang Sun | Organizer: Anru Zhang | Organizer: Larry Han | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
Wenjie Hu | Wenliang Pan | Jiwei Zhao | Weijie Su | Jianbin Tan | Larry Han | |
Kaizheng Wang | Long Feng | Yang Ning | Yin Xia | Shan Luo | Yige Li | |
Xiao Wu | Canhong Wen | Sai Li | Linjun Zhang | Yuefeng Han | Lars van der Laan | |
Xiaofei Wang | Ting Li | Zhou Zhou | Anru Zhang | Luke Keele | ||
Lunch | ||||||
13:30 - 15:10 | Data Science for Transcriptomic Data Analysis | Recent Progress in Causal Inference | Analysis of Complex Dependent and High-dimensional Data | Learning Algorithms in Computational Sciences | Studies on Large Language Models and Knowledge Graphs in Medicine | Statistical Principles in Modern Biomedical Research |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Andre Python | Chair: Zijian Guo | Chair: Han Xiao | Chair: Yuan Cao | Chair: Sheng Yu | Chair: Rong Ma | |
Organizer: Boris Hejblum | Organizer: Zijian Guo | Organizer: Han Xiao | Organizer: Yuan Cao | Organizer: Sheng Yu | Organizer: Rong Ma | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
YingYing Wei | Ruoyu Wang | Ching-Kang Ing | Cong Fang | Xuezhong Zhou | Jingshu Wang | |
Yi Yang | Xinwei Shen | Han Xiao | Yunwen Lei | Le Bao | Qi Long | |
Zhe Li | Ben Dai | Dan Yang | Pengkun Yang | Shan Gao | Yao Zhang | |
Xuekui Zhang | Yumou Qiu | Ting Zhang | Difan Zou | Sheng Yu | Changxiao Cai | |
15:10 - 15:30 | Tea Break | |||||
15:30 - 17:10 | Modern Challenges in Statistical Inferences: From Non-Causal to Causal | Learning and Decision-making Based on Heterogeneous Data | Practical Statistical Inference to Inform Precision Medicine | Advanced Techniques in Data Analytics | Statistical Learning Methods for Complex Data | Method and Theory for Generative AI |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Wei Huang | Chair: Kaizheng Wang | Chair: Layla Parast | Chair: Zhao Chen | Chair: Wenzhuo Zhou | Chair: Qiang Liu | |
Organizer: Wei Huang | Organizer: Kaizheng Wang | Organizer: Layla Parast | Organizer: Zhao Chen | Organizer: Yifan Cui | Organizer: Qiang Liu | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
Susan Wei | Rong Ma | Emily Hsiao | Siqi Sun | Rui Pan | Jian Li | |
Pavel Krupskiy | Yichen Zhang | Fatema Shafie Khorassani | Luo Luo | Yuhao Wang | Cheng Zhang | |
Matteo Bonvini | Xiaojie Mao | Amanda Glazer | Siming Chen | Yuehan Yang | Chongxuan Li | |
Shiyuan He | Yaqi Duan | Zijun Gao | Wenzhuo Zhou | |||
18:00 | Banquet | |||||
Xixi Hall (西溪厅) | ||||||
July 10th, Wednesday | ||||||
9:00 - 10:00 | Panel Discussion | Peter L. Bühlmann, Tianxi Cai, Yazhen Wang, Ming Yuan, Heping Zhang | Title: Future Direction of Data Science Research in the Rise of AI | Xixi Hall (西溪厅) | ||
10:00 - 10:40 | Tea Break | |||||
10:40 - 12:20 | Recent Advances in Statistical Network Analysis | Statistical and Machine Learning Inference | Recent Developments of Changepoint Detection | Exploring Complex Data Landscapes: EHR and AI Innovations in Clinical Research | Recent Advances in Causal Learning and Statistical Learning with Sparsity | Recent Advances in Distribution-free Inference |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Hengrui Cai | Chair: Jia Gu | Chair: Yazhen Wang | Chair: Chuan Hong | Chair: Gemma Moran | Chair: Zhimei Ren | |
Organizer: Ji Zhu | Organizer: Jinchi Lv | Organizer: Zhou Yu | Organizer: Chuan Hong | Organizer: Gemma Moran | Organizer: Zhimei Ren | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
Danyang Huang | Lan Gao | Feiyu Jiang | Hulin Wu | Jiaqi Zhang | Shuangning Li | |
Tianxi Li | Zhao Ren | Guanghui Wang | Ricardo Henao | Hengrui Cai | Ying Jin | |
Xiaoyue Niu | Songshan Yang | Weichi Wu | Yucong Lin | Xin Bing | Yu Gui | |
Wanjie Wang | Zemin Zheng | Xuehu Zhu | Chuan Hong | Gemma Moran | Cong Ma | |
Lunch | ||||||
13:30 - 15:10 | Scalable and Advanced Techniques in Statistics | Analysis of Complex Data, Some Recent Advances | Recent Advances in Causal Inference and Discovery | Recent Advances in Statistical Genetics | Recent Statistical Developments for Precision Medicine | Recent Progress in Machine Learning |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Xudong Li | Chair: Cun-Hui Zhang | Chair: Matteo Bonvini | Chair: Rui Duan | Chair: Ruoqing Zhu | Chair: Zijian Guo | |
Organizer: Xudong Li | Organizer:Cun-Hui Zhang | Organizer: Matteo Bonvini | Organizer: Rui Duan | Organizer: Yifan Cui | Organizer: Zijian Guo | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
Yangjing Zhang | Jian Huang | Yuming Zhang | Liyang Song | Xinyi Li | Xin Xiong | |
Xiaojun Mao | Cheng-Der Fuh | Wei Huang | Zilin Li | Yiyun Luo | Nian Si | |
Yuling Jiao | Qiyang Han | Lin Liu | Tian Ge | Ruoqing Zhu | Juntong Shen | |
Cun-Hui Zhang | Armeen Taeb | Tianying Wang | Tian-Zuo Wang | Minge Xie | ||
15:10 - 15:30 | Tea Break | |||||
15:30 - 17:10 | Advanced Methods and Theories in AI | Topics in Adaptive Experimentations | Advancements in Optimization for Machine Learning | Novel Machine Learning and Statistical Methods for Spatial Genomics | Advanced Methods in Medicine | Machine learning and Financial Econometrics |
Xixi Hall A (西溪厅A) | Xixi Hall B (西溪厅B) | Dongwan Hall A (董湾厅A) | Dongwan Hall B (董湾厅B) | Meishu Hall (梅墅厅) | Meizhu Hall (梅竹厅) | |
Chair: Dan Wang | Chair: Koulik Khamaru | Chair: Yingzhou Li | Chair: Siyuan Ma | Chair: Emily Hsiao | Chair: Yazhen Wang | |
Organizer: Dan Wang | Organizer: Koulik Khamaru | Organizer:Yingzhou Li | Organizer: Siyuan Ma | Organizer: Emily Hsiao&Andre Python | Organizer: Yazhen Wang | |
Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | Speakers: | |
Ke Zhu | Koulik Khamaru | Ke Wei | Hao Wu | Layla Parast | Xiangyu Cui | |
Zhanrui Cai | Wenlong Mou | Cheng Cheng | Ruibin Xi | Xiao Tan | Di Wang | |
Yuan Cao | Avishek Ghosh | Tao Luo | Siyuan Ma | Tim CD Lucas | Lequan Yu | |
Zhenyu Wang | Tianyu Wang | Shouhao Zhou | Huiling Yuan |
4. Keynote Speech
Peter L. Bühlmann
ETH Zürich
Title: Causality-inspired Statistical Machine Learning
Abstract:
Reliable, robust and interpretable machine learning is a big emerging theme in data science and statistics, complementing the development of pure black box prediction algorithms. New connections between distributional robustness, external validity and causality provide methodological paths for improving the reliability and understanding of machine learning algorithms, with wide-ranging prospects for various applications.
Huazhen Lin
New Cornerstone Science Laboratory,
Center of Statistical Research and School of Statistics,
Southwestern University of Finance and Economics
Title: Deep regression learning with optimal loss function*
Abstract:
In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data structure. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoids the specification of the distribution and thus is flexible to any kind of distribution, such as heavy tails and multimodal or heterogeneous distributions. Third, the proposed loss function relies on probabilities rather than direct observations as in least square loss, hence contributing to the robustness of the proposed estimator. Finally, the proposed loss function involves a nonparametric regression function only. This enables the direct application of the existing packages, simplifying the computational and programming requirements. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE where the density function is known. Our simulation studies show that the proposed estimator outperforms the existing methods in terms of prediction accuracy, efficiency and robustness. Particularly, it is comparable to the true MLE and even gets better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods.
*Joint work with Xuancheng Wang and Ling Zhou
5.Short Course
July 8th, 1:00-5:30 p.m
Meizhu Hall
1. Federated learning(1:00~3:00 p.m)
Rui Duan
Assistant Professor of Biostatistics
Harvard T.H. Chan School of Public Health
Title: Principles and Practices of Federated Learning: Methods, Challenges, and Case Studies
Abstract: In many areas, data has been collected in a decentralized way and federated learning emerges as an important methodology for training statistical and machine learning models without the need to centralize data. In this short course, we will delve into the fundamental principles and state-of-the-art techniques of federated learning. We will introduce practical considerations and challenges, including privacy concerns and communication barriers, within real-world scenarios. Additionally, we will discuss innovative strategies to enhance the effectiveness and applicability of federated learning. We will explore case studies that demonstrate the application of federated learning in biomedical research, aimed at facilitating multi-institutional data integration and collaboration.
2. Data visualization and reproducibility(3:30~5:30 p.m)
Subtitle: Have a Shiny Day!
Topic: Mini-course on R Shiny for data visualization and reproducibility
Schedule:
1. Introduction to the mini-course; Andre Python, ZJU100 Young Professor, Zhejiang University; 5 minutes
2. Introduction to R Shiny; Tutor: Kimberly Zhang, Senior Data Scientist, Microsoft; 45 minutes
3. Shiny scalability; Tutor: Yang Ming, Data Scientist, SZMS Technology; 45 minutes
4. Shiny reproducibility; Tutor: Tim CD Lucas, Lecturer, University of Leicester ; 20 minutes
6. 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.
Fee: ¥600/650/750/800 RMB per room per night (Breakfast included)
*Please book the rooms online after registration before July 1st, 2024.
7. Registration&Submission
Please register on this website and submit abstract online byJune 25.
境内单位参会人员注册通道(Mainland of China)
https://www.zjuyh.com/data2024/df
境外单位参会人员注册通道(Outside of the Chinese Mainland)
https://www.zjuyh.com/data2024en/df
Please log in for registration and payment.
8. Registration Fee
Payment method:
(1) Online payment
Support wechat, Alipay, bank transfer,paypal
The registration fee of this conference is entrusted to "Hangzhou Qizhen Exhibition Service Co., LTD." and the VAT electronic ordinary invoice is issued.
(2) Public transfer
NAME:HANGZHOU QIZHEN EXHIBITION SERVICE CO.,LTD
ACCOUNT NO:19042514040001265
BANK:AGRICULTURAL BANK OF CHINA,ZHEJIANG BRANCH,HANGHZOU ZHEDA SUB-BRANCH
Note Information (very important) : Name +HICFDS 2024
(Please note "Name of the participant +HICFDS 2024" in the postscript when making the official remittance)
(3) On-site payment
On-site payment on the day of the meeting registration (do not enjoy early bird price), support credit card, Alipay, wechat pay.
* Cancellation Policy
·No refund will be issued if registration is cancelled after June 18,2024
·Full refund (minus processing fee) for registration fee will be made if cancellation is requested on or before June 18,2024
* Invoice:
The electronic invoice of the conference expenses will be sent to the email address of the participants within 7 working days after the conference.
9.Contact
Academic Contacts:
Su, Weina (Zhejiang University)
Tel: +86-0571-88208268
E-mail: suweina@zju.edu.cn
Niu,Qian(Zhejiang University)
Tel: +86-0571-88208302
E-mail: niuqian@zju.edu.cn
Meeting Contacts(Hotel Reservation):
Zhan, Yanmin (Qizhen Exhibition)
Tel: +86-0571-88177983
E-mail: qizhenhz@zju.edu.cn
Welcome!
Center for Data Science, Zhejiang University