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捕鱼能上下分24小时兑现:?Innovation Laboratory of Mingli College | Big data people's livelihood governance cross-research innovation team

The rapid development of artificial intelligence and big data technology has promoted the transformation of the research paradigm of people's livelihood governance, providing new perspectives, new materials, new methods, and new challenges for the research of people's livelihood governance. On the one hand, the digitization of public services and the construction of digital government have reshaped the operation mechanism of previous livelihood protection projects, and the people have increased their demands for digitization, convenience and humanization of public services. On the other hand, artificial intelligence and big data technology have improved the level and efficiency of people's livelihood governance, significantly promoted the scientific decision-making of people's livelihood governance, the agile response, and the precision of the results, and also deeply promoted the research and policy innovation related to people's livelihood governance.

Combining teaching and research teams in the fields of public administration, statistics, sociology, and computer science, the team focuses on people's livelihood governance and people's well-being, explores interdisciplinary innovative research based on artificial intelligence and big data, responds to the new proposition of people's livelihood governance in the era of big data with new thinking concepts and new research methods, and promotes the high-quality development of people's livelihood governance practices.


Innovation team mentor group

Hu Hongwei

Professor, School of Public Administration

Vice President of Qiushi Academy

Research interests: Medical insurance and health security, health system reform, policy analysis and evaluation of the elderly, poverty, assistance and welfare

Cheng Yonghong

Associate Professor, School of Public Administration

Research interests: Income distribution and social security, pension security reform, employment issues

Zhang Qiong

Associate Professor, School of Public Administration

Research interests: Population and Labor economics, public policy evaluation, behavioral public management

Chen He

Associate Professor, School of Public Administration

Research interests: Population aging and social security, long-term care system development, aging on the impact of basic medical insurance system

Zhou Qin

Associate Professor, School of Public Administration

Research interests: Medical security and Social insurance, health policy evaluation, socio-economic factors affecting health, health economics

Wang Wen

Associate Professor, Beijing Technology and Business University

Research direction: International comparison of old-age security and social security

Wang Yu

Associate professor, School of Statistics

Research interests: Medical security, aging, health survey, health index construction, network public opinion index construction

Sun Tao

Associate professor, School of Statistics

Research interests: Complex survival data model, machine learning model

Yu Yuehui

Lecturer, School of Public Administration

Research interests: Medical security, health promotion, mental health, social poverty reduction, family policy


Innovative content

Combining the teaching and research teams in the fields of management and statistics, the team is committed to exploring the application of artificial intelligence, big data, statistics, and econometrics to the research of people's livelihood issues and governance, and promoting the multi-disciplinary exploration and innovative research of artificial intelligence and big data in people's livelihood governance. The following are research directions and topics that the tutor team is interested in, but not limited to, exploring with the students in the Innovation team.

1. Big data and pension and medical policy innovation

2. Application of statistical mechanics methods in the study of income distribution function

3. NOBEL, Neo Organizational & Behavioral Economics Lab. 3. Nobel, neo Organizational & Behavioral Economics Lab

4. Big data and long-term care system construction

5. Reform of the national basic medical insurance system at the provincial level

6. National mental health development and utilization of mental health services

7. Integrated care and family support for the elderly

8. Complementary reforms to delay retirement

9. Comorbidity management of chronic disease patients in community

10. Children's health protection is coordinated by three doctors

11. Construction and evaluation of Healthy China Construction index

12. Research on the construction of mental health network public opinion index

13. Health life expectancy measurement and decomposition method based on longitudinal cohort data


Currently some team members

He Haotian

Class of 2023

PhD student of Social security major

research direction: pension security, social assistance

Zhang Qian

PhD student of Social security major in 2023

research direction: Medical security

Shi Jianqun

Class of 2023 social security doctoral candidate

research direction: long-term care insurance, medical security

Wang Xiaojun

Class of 2023

PhD student of Social Security major

research direction: Elderly care services

Deng Heming

Class of 2022

Master's degree in Epidemiology and Health statistics

research direction: network public opinion index construction, health survey

Gao Sunan

Class of 2022

Master's degree in Epidemiology and Health statistics

research direction: causality inference and construction of online public opinion index

Yanzhao Wang

Class of 2022

Master's degree in Epidemiology and Health statistics

research direction: multimodal information fusion and prognostic risk


Interested students Welcome to our team

Students who are interested in the interdisciplinary research of artificial intelligence and big Data people's livelihood governance innovation are welcome to join our team. In our team, you will have the opportunity to learn the basic knowledge of related fields under the guidance and cooperation of the supervisor group and doctoral seniors, improve the comprehensive research ability, break through the bottleneck of cross-disciplinary research under the team cooperation, and promote the high-quality development of the national people's livelihood governance practice.

The team welcomes students with backgrounds in statistics, econometrics, machine learning and big data, as well as students interested in empirical research. As long as you are interested in people's livelihood governance issues and do not reject quantitative research, you are welcome to come to our team. Let us explore and feel the charm and significance of interdisciplinary research on artificial intelligence and big data people's livelihood governance innovation.


Registration method: Questionnaire star registration

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