2022年天大经管线上研究生暑期学校报名通知

为促进高校优秀研究生之间的学术交流，充分利用研究生教育的优质资源，开拓优秀博士生学术视野，并提升其理论功底与写作技能，天津大学管理与经济学部将于2022年7月1日—7月14日举办线上全国研究生暑期学校活动。

时间：2022年7月1日—7月14日

地点：中国 · 天津

开课形式：网络远程

01

课程设置

本期暑期学校拟开设以下9门课程（周日不开课），具体授课平台信息将于开课前发布。

02

招生对象

本届暑期学校将面向全国招收在读博士研究生，以及少量海外博士研究生和有志于攻读天津大学管理学博士学位的硕士研究生。

为了保证教学质量，研究生暑期学校的将采取限额申报，感兴趣的同学请抓紧报名。

03

报名方式

1. 研究生暑期学校即日起接受报名，报名截止时间为6月22日0:00。

2. 请将《附件2：暑期学校报名表》及《附件3：暑期学校报名信息统计表》发送至[e-mail]，附件2需要请导师电子签名，报名文档及邮件命名为“暑期学校报名-姓名-所在学校”。录取情况将于6月23日前以电子邮件或电话形式通知被录取学员，未接到录取通知的同学皆视为未录取，不另行通知。

04

其他说明

1.暑期学校结束后，将对合格学员颁发天津大学管理与经济学部2022年研究生暑期学校结业证书。

2. 本次暑期学校免收学杂费、报名费。

3. 无特殊情况，学员须全程参加暑期学校活动，不得中途或提前离开。

4. 联系方式：

办公电话：[phone]

邮箱: [e-mail]

5. 暑期学校相关工作的解释权在天津大学管理与经济学部。

授课教师简介

课程Ⅰ：

Course Title: Causal Models for Business Research

12

Instructor: 

Prof. Yong Tan

University of Washington Tsinghua University

Instructor Bio: Yong Tan is the Michael G. Foster Endowed Professor of Information Systems and Chair of Department of Information Systems and Operations Management at the Michael G. Foster School of Business, University of Washington, the Chang Jiang Scholar Visiting Chair Professor at the School of Economics and Management, Tsinghua University, and a Distinguished Fellow of the INFORMS Information Systems Society. He was the founding Associate Director of the USTC-UW Institute for Global Business and Finance Innovation. He received his Ph.D. in Physics, advised by 2016 Nobel Laureate Professor David J. Thouless, and Ph.D. in Business Administration, both from the University of Washington. His research interests include electronic, mobile and social commerce, big data, AI, economics of information systems, social and economic networks, sharing economy, fintech, and health IT. He has published over 80 papers in Management Science, Information Systems Research, Operations Research, Management Information Systems Quarterly, INFORMS Journal on Computing, IEEE/ACM Transactions on Networking, IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, among others. He served as an associate editor of Information Systems Research and Management Science, and is now a senior editor of Information Systems Research. He was a co-chair of Conference on Information Systems and Technology (CIST 2010), the cluster chair of 2012 INFORMS Information Systems Cluster, a track co-chair of International Conference on Information Systems (ICIS 2013, 2021), a co-chair of Workshop on Information Technologies and Systems (WITS 2014), and a co-chair of INFORMS Workshop on Data Science (2019). He received 2017 Management Science Best Paper Award in Information Systems, Association for Information Systems (AIS) Best Publication of 2012 Award, and 2012 Information Systems Research Best Paper Runner-Up Award. He has placed his doctoral students in top information systems programs such as Carnegie Mellon University, Purdue University, Indiana University, University of Notre Dame, University of Texas at Dallas, Georgia State University, University of Florida, Arizona State University, and University of California, Irvine.   

Course Description: Empirical research normally requires combining several inputs together: economics or behavioral theories; specific examples and institutional background; datasets and data coding; econometric tools and computation. We will discuss how to combine these inputs together in our research projects. This lecture series aims to introduce advanced empirical methods for causal inferences. You are expected to have a solid and critical understanding of estimation strategies for a number of models, including but not limited to, treatment effects, hierarchical models, and instrument variables approach. We will discuss some common identification challenges for causal relationships and the potential solutions. We will work on computational exercises designed to help you learn empirical estimation tools and verify theoretical understanding. However, the emphasis will primarily on the econometrics. Some level of computer programming with R will be required as well. 

课程Ⅱ：

课程名称: 仿真优化的理论与应用

12

Instructor: 

洪流教授

复旦大学

个人简介: 洪流教授本科毕业于清华大学，博士毕业于美国西北大学。现任复旦大学特聘教授、弘毅讲席教授、大数据学院副院长和管理科学系系主任；曾任香港城市大学管理科学讲座教授，香港科技大学教授和金融工程实验室主任等。洪流教授的研究主要集中在随机运筹学、数据科学、供应链管理、风险管理等领域，在Operations Research和Management Science等UTD期刊上发表论文二十余篇。洪教授目前担任INFORMS仿真分会主席、中国管理现代化研究会风险管理专委会主任、中国运筹学会金融工程和风险管理分会副理事长、Journal of Operations Research Society of China的Associate Editor-in-Chief、Operations Research的Area Editor和Management Science的Associate Editor等。

课程简介: 随机仿真是复杂动态随机系统建模的重要工具，而基于随机仿真模型的优化则称为仿真优化。在本课程中，我们讲简单介绍仿真优化的几个子领域中的一些基础性工作和当前研究前沿，这些子领域包括排序择优、离散仿真优化、梯度估计和连续仿真优化等。课程还将介绍仿真优化和深度学习相结合的一些前沿热点研究。

课程Ⅲ:

Course Title: Applications of Game Theory in Research and Practice

12

Instructor: 

Prof. Subodha Kumar

Fox School of Business

Temple University

Instructor Bio: Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Marketing and Supply Chain Management and the Founding Director of the Center for Business Analytics and Disruptive Technologies at Temple University’s Fox School of Business. He has secondary appointments in Information Systems and Statistical Science Departments. He also serves as the Ph.D. Concentration Advisor for Operations and Supply Chain Management. He is a board member for many organizations. He has been awarded a Changjiang Scholars Chair Professorship by the China’s Ministry of Education. He is also a Visiting Professor at the Indian School of Business (ISB). He has served on the faculty of University of Washington and Texas A&M University. He has been the keynote speaker and track/cluster chairs at leading conferences. He was elected to become a Production and Operations Management Society (POMS) Fellow in 2019. He has received numerous other research and teaching awards. He has published more than 150 papers in reputed journals and refereed conferences. He was ranked #1 worldwide for publishing in Information Systems Research. In addition, he has authored a book, book chapters, Harvard Business School cases, and Ivey Business School cases. He also holds a robotics patent. He is routinely cited in different media outlets including NBC, CBS, Fox, Business Week, and New York Post. He is the Deputy Editor of Production and Operations Management Journal and the Founding Executive Editor of Management and Business Review (MBR). He also serves on other editorial boards. He was the conference chair for POMS 2018 and DSI 2018, and has co-chaired several other conferences.

Course Description: This course will begin with an introduction to the basics of game-theoretic models. The goal is that all the students should be able to apply these methods to different problems. To achieve this goal, first, they should be able to model the problem using these methods. Second, they should be able to solve the problem to get useful analytical insights. In summary, the goal is to ensure that the students should be able to see the bigger picture when they work on a problem. After some introductory lectures, students will present the related papers on various topics.

课程Ⅳ：

课程名称：

国际顶级期刊论文发表的四大败因及对策：以个人—环境匹配研究为例

12

Instructor: 

管延军教授

杜伦大学 

香港中文大学

个人简介: 管延军，本科和硕士毕业于北京大学，后在香港中文大学获得心理学博士，在中国人民大学、英国杜伦大学（Durham University）等知名高校积累了丰富工作经验，2017年成为杜伦大学商学院首位华人正教授；2021年被香港中文大学深圳校区聘为组织管理与职业心理学教授。目前已有60余篇论文发表于SSCI国际学术期刊，包括SSCI 一区论文40余篇。2018 年4 月至今担任职业心理学重要国际期刊Journal of Vocational Behavior副主编 （Associate Editor）。在组织管理心理学领域的研究发表于Journal of Management、Personnel Psychology、Journal of Organizational Behavior等知名期刊。 在职业生涯心理学顶级期刊Journal of Vocational Behavior发表论文20篇，在全球同时代研究者中居领先地位；在跨文化研究领域的合作成果发表于Journal of Personality and Social Psychology、Psychological Science、Social Psychological and Personality Science、Personality and Social Psychology Bulletin、Journal of Cross-cultural Psychology等社会及文化心理学顶级期刊，以及影响因子高达21 的Nature 子刊Nature Climate Change 等跨学科期刊。

课程简介：本工作坊将会结合主讲人在管理学国际学术期刊的论文发表以及评审经历，就管理学研究的构思设计、英文论文的写作发表等问题进行简要讲解。主题将围绕论文发表的几大难点展开：（1）如何从主编和评审者的角度来审视自己论文的价值，提升研究设计的质量？（2）开启论文前言有哪几种有效的模式？（3）如何丰富论文的讨论部分？（4）如何应对苛刻的评审意见？ 

课程Ⅴ:

Course Title:

Ⅰ：Meta-Analysis: Benefits, Versatility, and Next-Gen Possibilities

Ⅱ: The Future of Teams and Leadership Research: Where We’ve Been and We’re Going 

12

Instructor: 

Bart de Jong

The University 

The University of California, Berkeley

Instructor Bio: Bart (PhD 2010) is an Associate of OB/HRM at Australian Catholic University (ACU). His research focuses on organizational trust, teams, leadership, and creativity and innovation, which he examines through primary studies and – increasingly – research syntheses (meta-analyses, reviews). He has a highly consistent track record of publishing in top-tier Management journals (e.g., AMJ, JAP, AMA, AROPOB, JOM) and serves on review boards and as an associate editor of these journals (AMJ, JAP). His research excellence and external service has been recognized through multiple best paper and reviewer awards, as well as through invitations to advisory boards and learned societies. Bart has successfully attracted competitive research income, including from the Dutch Research Council (€250k) and Australian Department of Defence ($250k). He has a demonstrable track record of internal research leadership through various roles at the university, faculty, and school level. Bart's teaching portfolio includes both substantive and methodological courses, ranges from the Bachelor to the PhD level, and includes invited teaching at the triple-accredited (AACSB, EQUIS, AMBA) Erasmus University Rotterdam. Prior to joining ACU, Bart worked as an Associate Professor at the VU University Amsterdam (the Netherlands).

Course Description:

Course Title:

Ⅰ: Meta-Analysis: Benefits, Versatility, and Next-Gen Possibilities

Meta-analysis is a powerful and increasingly popular tool for synthesizing evidence on substantive topics of interest. The aim of my talk is to provide you with an introductory, non-statistical, high-level overview of the key benefits, versatility, and next-generation possibilities of meta-analyses (over primary studies and narrative reviews), as well as rebuttals to common criticisms of meta-analysis. In doing so, I will provide illustrations from my own and others' meta-analytic applications within the field of Management. Finally, I will direct you to some useful resources for getting started on either your first-ever meta.

Ⅱ: The Future of Teams and Leadership Research: Where We’ve Been and We’re Going

Teams and leadership are two of the most fundamental topics within the field of Management in general, and Organizational Behavior in particular. My talk adopts a field-evolutionary perspective of these two research fields, and provides you with a holistic understanding of their evolutionary trajectories, including what research has been done on these topics, how they have evolved in recent times, and how what directions they might develop into in the future. In doing so, I will provide illustrations from my own and others’ research in these areas.

课程Ⅵ：

课程名称：Data Analytics and Machine Learning with Python

12

Instructor: 

Prof. Tao Li

The Leavey School of Business

Santa Clara University

Instructor Bio：Tao Li is the director of MS Program in Business Analytics and an associate professor of Information Systems & Analytics in the Leavey School of Business at Santa Clara University. He joined the Business School in Fall 2012 as an assistant professor after graduating with his Ph.D. from The University of Texas at Dallas.

Professor Li’s research interests include sharing economy, crowdfunding, strategic sourcing, supply chain coordination, operations-marketing interface, sustainable operations management, and behavioral operations management. His scholarship has appeared in leading academic journals such as Production and Operations Management, Manufacturing & Service Operations Management, European Journal of Operational Research. His scholarship has been supported by the Santa Clara University Research Grant and the Leavey Research Grant. He is the recipient of the Leavey School of Business Extraordinary Research Award multiple times.

Professor Li teaches Machine Learning with Python, Data Analytics with Python, and Prescriptive Analytics for MS programs in Business Analytics, Information Systems, and Finance. He also teaches Business Analytics, Predictive Analytics, Computer Based Decision Models, Operations Management, and Analytical Decision Making for the Business School's accelerated and evening MBA program and undergraduate program. He is the recipient of the ACE (Accelerated Cooperative Education Leadership Program) Outstanding Faculty Award in 2016 and 2019, and the Leavey School of Business Extraordinary Teaching Award multiple times.

Professor Li supervised projects with companies such as Amazon Web Services (AWS), Adobe, Rubrik, Cloudera, Nuveen, and Atollogy.

Professor Li serves as Senior Editor for Production and Operations Management, Associate Editor for Transportation Research Part E: Logistics and Transportation Review, and Guest Associate Editor for Naval Research Logistics.  He has been a regular reviewer for top journals including Management Science, Operations Research, and Manufacturing & Service Operations Management.

Course Description: The objective of this course is to teach the analytical mindset & programming skills relevant to data science. Students will learn the Python programming language, along with a set of tools for data science in Python, including the Jupyter (IPython) Notebook, NumPy, Pandas, Seaborn, and Scikit-learn. Students will learn skills that cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping and aggregation, visualization, time series, statistical modeling/prediction and communication of results. This class also offers a hands-on approach to machine learning and data science. The class discusses the application of machine learning methods like SVMs, Random Forests, Gradient Boosting and neural networks on real world dataset, including data preparation, model selection and evaluation. 

课程VⅡ：

课程名称：Interplay between Derivatives and Stocks Markets

12

Instructor: 

韩冰教授

加拿大多伦多大学罗特曼管理学院

个人简介: 韩冰，芝加哥大学数学博士及UCLA金融学博士，加拿大多伦多大学罗特曼管理学院金融学教授，多伦多证券交易所资本市场讲座教授. 韩冰教授的主要研究领域是资产定价(理论和实证), 投资,行为金融学,风险管理及房地产金融。他的多篇论文发表在顶级经济，金融和管理学学术杂志上，包括Journal of Finance, Journal of Financial Economics，Review of Financial Studies， Review of Economic Studies，International Economic Review, Journal of Economic Theory，Management Science等。他的研究成果受到《纽约时报》、《华尔街日报》、《华盛顿邮报》、《经济学人》等媒体的专访和报导。韩冰教授获得了众多国际知名学术奖项，包括欧洲金融协会最佳论文奖，中国金融协会会议最佳论文奖，美国个人投资者协会在资产定价研究中获优秀论文奖，上海风险论坛最佳论文奖, 中国国际金融与政策论坛杰出论文奖, 全球金融专业人士协会终身成就奖。韩冰教授现任Financial Management，Journal of Economic Dynamics and Control，Journal of Empirical Finance，International Review of Finance和Pacific-Basin Finance Journal主编和副主编。

课程简介: 本短期课程从风险、信息和错误定价的角度介绍衍生品与基础市场之间的各种相互作用。我将展示研究股票收益的横截面以及预测时间序列中的股票市场收益的新角度。我还将讨论一个新兴的资产定价领域，该领域检查不受股票收益可预测性驱动的期权收益可预测性。此外，我将根据我作为作者和主编的经验分享对学术出版的建议。学员们通过本课程对于如何做好研究和发表会有更进一步的认识。

课程VⅢ:

课程名称：行为金融和投资者行为

12

Instructor: 

路磊教授

加拿大曼尼托巴大学阿斯博商学院

个人简介：路磊，加拿大曼尼托巴大学阿斯博商学院金融学(终身)正教授，Bryce Douglas讲席教授，博士生导师。2007年毕业于加拿大麦吉尔大学，获得金融学博士学位。2007至2011年任教于上海财经大学金融学院，2011至2016年任教于北京大学光华管理学院。

他的研究方向包括资产定价，行为金融和国际金融。他的研究发表在Management Science, Journal of Financial and Quantitative Analysis, Journal of Corporate Finance, Financial Management, Journal of Economic Dynamics and Control, Economic Theory等金融学和经济学英文期刊。他的研究也发表在《管理科学学报》和《金融研究》等中文期刊。

他曾经主持过加拿大社会科学和人文研究理事会 (SSHRC)基金项目，国家自然科学基金面上项目，上海市浦江人才计划项目，以及中国金融期货交易所的研究项目。他目前担任Accounting and Finance, China Finance Review International, Journal of Management Science and Engineering 杂志副主编。

课程简介：本课程主要介绍投资者异质信念与股票市场的理论和实证，以及投资者情绪与股票横截面收益。

课程Ⅸ：

课程名称：Bubbles and Sentiments

12

Instructor: 

王鹏飞教授

北京大学

个人简介：王鹏飞教授2000年毕业于吉林大学，获经济学学士学位，2003年毕业于北京大学，获经济学硕士学位，2007年毕业于美国康奈尔大，获哲学博士学位（Ph.D. in Economics）。2007年加入香港科技大学经济系，2013年晋升副教授（with tenure），2016年晋升教授。2020年从香港科技大学离职，加入北京大学汇丰商学院担任经济学讲席教授, 现为北京大学深圳研究生院副院长、北京大学汇丰商学院院长。主要研究宏观经济学和金融经济学，是全球经济学界中具有高影响力的华人经济学家，在国际一流期刊包括American Economic Review, Econometrica, Journal of Finance， 经济研究等杂志发表论文40多篇并主持多项重大项目。2020年与香港大学林晨教授等共同主持项目“金融科技、金融稳定和普惠金融”，获选香港研资局第十轮主题研究计划， 2021年先后获得国家自然科学基金首届原创项目和国家杰出青年项目的资助。

课程简介：The objective of this short course is to study the implications of financial market imperfections for macroeconomics and finance. The course will focus information and contract frictions in generating asset bubbles, sentiment-driven fluctuations both in theories and quantitative applications.

竭诚欢迎全国高校研究生踊跃报名参加！

点击“阅读原文”下载附件2、3

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信息来源 / 路欣雅

封面来源 / 谭清远

底图制作 / 张诗萌

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责任编辑 / 李   庚