学术交流

2024 International Conference on Frontiers of Data Science

作者：

时间：2024-02-08

阅读量：5772次

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 5th）

2024 International Conference on Frontiers of Data Science Program Book

Program
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 Data, Some Recent Advances	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: Cun-Hui Zhang	Chair: Yuan Cao	Chair: Sheng Yu	Chair: Rong Ma
	Organizer: Boris Hejblum	Organizer: Zijian Guo	Organizer:Cun-Hui Zhang	Organizer: Yuan Cao	Organizer: Sheng Yu	Organizer: Rong Ma

	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:
	YingYing Wei	Ruoyu Wang	Jian Huang	Cong Fang	Xuezhong Zhou	Jingshu Wang
	Yi Yang	Xinwei Shen	Qiyang Han	Yunwen Lei	Le Bao	Qi Long
	Zhe Li	Ben Dai	Cun-Hui Zhang	Pengkun Yang	Shan Gao	Yao Zhang
	Xuekui Zhang	Yumou Qiu		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:Andre Python
	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
18:00	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	Analysis of Complex Dependent and High-dimensional Data	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: Han Xiao	Chair: Yazhen Wang	Chair: Chuan Hong	Chair: Gemma Moran	Chair: Zhimei Ren
	Organizer: Ji Zhu	Organizer: Han Xiao	Organizer: Zhou Yu	Organizer: Chuan Hong	Organizer: Gemma Moran	Organizer: Zhimei Ren

	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:
	Danyang Huang	Ching-Kang Ing	Feiyu Jiang	Hulin Wu	Jiaqi Zhang	Shuangning Li
	Tianxi Li	Han Xiao	Guanghui Wang	Ricardo Henao	Hengrui Cai	Ying Jin
	Xiaoyue Niu	Dan Yang	Weichi Wu	Yucong Lin	Xin Bing	Yu Gui
	Wanjie Wang	Ting Zhang	Xuehu Zhu	Chuan Hong	Gemma Moran	Cong Ma
Lunch
13:30 - 15:10	Scalable and Advanced Techniques in Statistics	Statistical and Machine Learning Inference	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: Jia Gu	Chair: Matteo Bonvini	Chair: Rui Duan	Chair: Ruoqing Zhu	Chair: Zijian Guo
	Organizer: Xudong Li	Organizer: Jinchi Lv	Organizer: Matteo Bonvini	Organizer: Rui Duan	Organizer: Yifan Cui	Organizer: Zijian Guo

	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:	Speakers:
	Yangjing Zhang	Lan Gao	Yuming Zhang	Liyang Song	Xinyi Li	Xin Xiong
	Xiaojun Mao	Zhao Ren	Wei Huang	Zilin Li	Yiyun Luo	Nian Si
	Yuling Jiao	Songshan Yang	Lin Liu	Tian Ge	Ruoqing Zhu	Juntong Chen
		Zemin Zheng	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	Zhenyu Wang	Tao Luo	Siyuan Ma	Tim CD Lucas	Lequan Yu
			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.