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Monday 09:00 - 18:00 Tuesday 09:00 - 18:00 Wednesday 09:00 - 18:00 Thursday 09:00 - 18:00 Friday 09:00 - 18:00

College of Economics and Management of NUAA, College of Economics and Management(CEM), No. 29, Jiangjun Avenue, Jiangning Dist, Nanjing (2026)

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College Of Economics And Management(CEM), No. 29, Jiangjun Avenue, Jiangning Dist

16/12/2024

[Report Title]: Three Laws of Cyber-Physical Digitization for Industry 4.0 Smart Manufacturing
[Subject of Report]: Management Science and Engineering
[Speaker]: Professor George Q. Huang (Hong Kong Polytechnic University)
[Report Time]: 14:30-16:30 December 20, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
To automate or to digitize, thit is no longer a question for industrial transformation. While automation is situational, digitization is imperative to develop Industry 4.0 smart manufacturing cyber-physical systems. Both strategies share the same aim at creating values, critical masses and interoperability. But they lead to differing cost and impacts when operational settings vary. If industrial operations are repetitive and the repetition is substantial with prevailing certainty, then automation is an appropriate strategy for most cost-effective industrial transformation. However, significant proportion of industrial operations are still human-centric and cannot be fully automated. Digitization is often associated with so-called “Three High Problems”, namely high cost, high risk and high technical threshold. This talk presents three laws for tackling the Three High Problems. The Three Laws are (i) The First Law of Universal Value – The Value Law; (ii) The Second Law of Sharing Equilibrium – The Sharing Law; and (iii) The Third Law of Cyber-Physical Entanglement – The Entanglement / Coupling Law. The talk will outline the principles and properties of the three laws and demonstrate how they can be utilized for cost-effective digitization.

About the speaker:
Professor Huang Guoquan joined the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University in December 2022 and became a chair professor of intelligent manufacturing and deputy director of the Advanced Manufacturing Institute of the Hong Kong Polytechnic University. Prior to this, Professor Huang served as Chair Professor and Head of the Department of Industrial and Manufacturing Systems Engineering at the University of Hong Kong. Professor Huang graduated from the Department of Mechanical Engineering of Nanjing Institute of Technology (Southeast University) and received a PhD from Cardiff University in the UK. Professor Huang started studying for a doctorate in the mid-1980s and has been engaged in research on intelligent manufacturing, intelligent logistics and intelligent buildings for a long time. He has hosted nearly HK$150 million in R&D funding from government research funds, universities and enterprises, and has participated in more than one project. 150 million Hong Kong dollars, he has published a series of academic papers, monographs, and special issues, and has been widely cited by his peers, becoming the 1% most widely cited scholar in the world. Professor Huang has served as an editorial board member or deputy editor for more than a dozen international magazines. He is a fellow of the American Institute of Electrical and Electronics Engineers (FIEEE), a fellow of the American Society of Mechanical Engineers (FASME), a fellow of the British Institute of Logistics and Transport (FCILT), and a fellow of the British Institute of Logistics and Transport (FCILT). Fellow of the Institute of Engineering and Technology (FIET), Fellow of the Hong Kong Institution of Engineers (FHKIE), and Fellow of the American Institute of Industrial Engineers (FIISE). Professor Huang has been selected as the Outstanding Youth of the National Foundation of China, the National Talent Plan, the Guangdong Province Science and Technology Cooperation Award, the First Prize of the Natural Science Award of the Ministry of Education, and the First Prize of the Guangdong Province Science and Technology Progress Award. Recently, he has presided over a number of major projects of the Hong Kong Research Foundation and Innovation Technology around the themes of smart manufacturing, smart logistics and smart construction of Yuanwang.

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19/11/2024

[Report Title]: Emerging trends and future directions in sustainability and global value chain research
[Subject of Report]: Applied Economics
[Speaker]: Aymen Sajjad（Massey University New Zealand）
[Report Time]: 15:00-18:00 December 5, 2024
[Report Place]: Room 708, College of Economics and Management

Summary of the report:
The importance of research on sustainability in global value chains lies in its potential to address pressing global challenges, including climate change, resource scarcity, and social inequality. Sustainable global value chain practices are instrumental in reducing emissions, conserving resources, and promoting fair labor practices, contributing to the broader goals of sustainable development. Against this background, this talk discusses the research findings that explore the underlying motivators and barriers to global value chain adoption. Further, emerging trends and future directions in global value chains will be presented.

About the speaker:
Dr Sajjad is a Senior Lecturer (Associate Professor) in Sustainability and Business Management at Massey University (QS rank-239). He holds a PhD in Sustainable Supply Chain Management from Massey University, New Zealand. His research focuses on sustainability issues that confront organizations and societies. Dr Sajjad is an Associate Editor of Business Strategy and the Environment, Corporate Governance: The International Journal of Business in Society, Journal of Management & Organization, FIIB Business Review Journal, and the International Journal of Sustainable Society. He is also an Editorial Advisory Board Member for the Review of International Business and Strategy, Management & Sustainability: An Arab Review and an Editorial Review Board Member for the Journal of Global Responsibility. Dr Sajjad has published in top-tier business management and sustainability journals (e.g., Journal of Economic Behavior and Organization, International Journal of Management Reviews, Business Strategy and the Environment, International Journal of Operations and Production Management, Journal of Manufacturing Technology Management, Social Indicators Research, Resources Policy, Sustainable Development, Corporate Social Responsibility and Environmental Management, and Corporate Governance: the International Journal of Business in Society). In addition, he has authored five book chapters on corporate social responsibility and sustainability issues and led several special issues in top-ranked journals. He is also Deputy Director of the Sustainability and Corporate Social Responsibility Research Hub at Massey Business School.

19/09/2024

[Report Title]: Some Statistical Models and Inference for Functional Data Analysis
[Subject of Report]: Management Science and Engineering
[Speaker]: Ling Nengxiang (Hefei University of Technology)
[Report Time]: 10:00-12:00 September 21, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
Functional data analysis (FDA) is one of the important fields of the modern statistics. In this talk, we first provide an introduction to FDA, including a description of the common statistical analysis techniques. Second, some regression modeling for FDA and its statistical inference are also presented, including our recent works. Finally, some possible research directions and applications are given.

About the speaker:
Ling Nengxiang, professor and doctoral supervisor of the School of Mathematics, Hefei University of Technology. His main research areas are the theory and methods of functional data analysis and non-parametric/semi-parametric statistical modeling. He has presided over a number of scientific research projects at the provincial and ministerial levels, including the National Social Science Foundation Key Project, the National Natural Science Foundation General Project, the Ministry of Education Humanities and Social Sciences Foundation Project, the Anhui Provincial Natural Science Foundation Project, and the National Bureau of Statistics Research Project. He has achieved many research results in the fields of functional data analysis, high-dimensional factor models, quantile regression, etc. His research papers have been published in important international statistics journals such as JRSSB and JMVA, as well as related international academic conferences. Many of his papers have been positively cited and evaluated by others.

05/09/2024

[Report Title]: Machine Learning and Dynamical Systems meet in Reproducing Kernel Hilbert Spaces
[Subject of Report]: Management Science and Engineering
[Speaker]: Boumediene Hamzi (California Institute of Technology)
[Report Time]: 14:00-16:00 September 18, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
The theory of dynamical systems, developed by Poincare and Lyapunov in the 19th century, studies the qualitative behaviour of systems through models, often involving differential equations. These models can be complex to develop for challenging systems like climate, brain, biological, and financial dynamics. In contrast, machine learning focuses on algorithms that improve with more data, applicable in areas like computer vision, stock market analysis, and social media sentiment analysis. Machine learning excels in scenarios where explicit models are absent but data is available. This talk explores how reproducing kernel Hilbert spaces can bridge dynamical systems theory and machine learning. We introduce methods for learning surrogate models, including parametric and nonparametric kernel flows for chaotic systems, and techniques like Sparse Kernel Flows and Hausdorff-metric based Kernel Flows. We also present a data-based approach for estimating key quantities in nonlinear systems, leveraging kernel methods to approximate controllability and observability energies, model reduction, invariant measures, and Lyapunov functions.

About the speaker:
Boumediene Hamzi is currently a Senior Scientist at the Department of Computing and Mathematical Sciences, Caltech, an Affiliate Fellow of the Data Science Institute at Imperial College London, and a visiting professor at Johns Hopkins University. He is also co-leading the Research Interest Group on Machine Learning and Dynamical Systems at the Alan Turing Institute (London, UK). He has been honoured twice as a Marie Curie Fellow and published more than 100 papers in academic journals including Automatica, SIAM Journal on Control and Optimization, etc. Broadly speaking, his research is at the interface of Machine Learning and Dynamical Systems.

27/06/2024

[Report Title]: Disagreement and the Macro Announcement Return
[Subject of Report]: Applied Economics
[Speaker]: Zhenzhen Fan（University of Guelph (Canada)）
[Report Time]: 11:00-13:00 July 1, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
Announcement day returns exhibit substantial variability across different macro variables. This paper argues that the heterogeneity in announcement day returns can be attributed to shifts in investor disagreement of stock returns on macro announcement days. To quantify this disagreement, we introduce a novel measure by examining the distribution function of investor beliefs on S\&P 500 returns using daily index option order imbalances. We then decompose this total disagreement into two components: uncertainty and differential interpretation. The former measures the dispersion in pre-announcement forecasts of macroeconomic news, while the latter captures disparities among investors in their perceptions of how the release of such news influences future stock returns. Our empirical analysis indicates that this decomposition of disagreement effectively accounts for the heterogeneity in announcement-day returns across different macro variables. Finally, we incorporate heterogeneous beliefs into an asset pricing model. Our model suggests that both the resolution of uncertainty and variations in the interpretation of macroeconomic announcements play significant roles in understanding announcement-day return.

07/06/2024

[Report Title]: Why Are There Six Degrees of Separation in a Social Network?
[Subject of Report]: Management Science and Engineering
[Speaker]: Stefano Boccaletti (Institute of Complex Systems, Italian National Academy of Sciences)
[Report Time]: 14:00-15:00 June 8, 2024
[Report Place]: Room 207, College of Economics and Management

Summary of the report:
A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. I will show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. I will show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

15/05/2024

[Report Title]: Climate Change Concerns, Firm ESG Performance, and Pricing of Weather-Related Natural Catastrophes: Evidence from the Catastrophe Bond Market
[Subject of Report]: Applied Economics
[Speaker]: Sun Tao (Department of Finance and Insurance, Lingnan University, Hong Kong)
[Report Time]: 10:00-12:30 May 16, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
We examine the impacts of media climate change concerns and sponsors’ ESG performance on the pricing of weather-related catastrophe bonds. We find that unexpected changes in media climate change concerns have a substantial impact on catastrophe bond secondary market spread. Moreover, investors demand a lower spread for catastrophe bonds sponsored by insurers with better ESG performance. A one-standard-deviation increase in sponsor ESG score will lead to a decrease in catastrophe bond spread by 107.0 basis points, or 18.0% of the average bond spread. More importantly, sponsor ESG performance mitigates the adverse impact of unexpected change in media climate change concern on catastrophe bond spread. Using the 2008 Great Financial Crisis as an exogenous shock, we show that the effect of sponsor ESG performance on catastrophe bond spread is casual because catastrophe bonds with observable sponsor ESG performance traded 116.5 basis points lower than those without observable sponsor ESG scores. Our findings have important implications for financing climate risk using insurance-linked securities.

About the speaker:
Prof. Tao Sun is currently a faculty member in the Department of Finance and Insurance at Lingnan University. He received his Ph.D. degree in Risk Management and Insurance from Temple University. His current research interests include systemic risk and financial stability, insurance economics, risk modeling, mortality/longevity risk management, and corporate risk management. Prof. Sun has publications in top tier journals in risk management, insurance and actuarial science, including the Journal of Risk and Insurance, and Insurance: Mathematics and Economics. He serves as referee for the Journal of Risk and Insurance, Insurance: Mathematics and Economics, Risk Management and Insurance Review, the North American Actuarial Journal, and the Journal of Insurance Issues. He was the principal investigator of Hong Kong Research Grants Council General Research Funds.

14/05/2024

[Report Title]: Transmission Effect of Insurers' Climate Risk Disclosures on Their Corporate Bond Investees' Environmental Friendliness
[Subject of Report]: Applied Economics
[Speaker]: Cheng Jiang (Department of Finance and Insurance, Lingnan University, Hong Kong)
[Report Time]: 10:00-12:30 May 16, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
We investigate how insurers’ mandatory climate risk disclosure affect their corporate bond investees’ environmental friendliness by relying on the adoption of the Climate Risk Disclosure Survey (CRDS) by the U.S. insurance industry and a difference-in-differences research design. We find that the adoption reduces carbon emissions by treated insurers’ investees, consistent with investors’ climate risk disclosure having transmission effects on improving investees’ environmental performance. This reduction is more pronounced when there is more public pressure on the insurers and/or their investees to be more climate friendly, when the insurers are likely to monitor their investees more closely, when the investees are more dependent on financing from the insurers, and when the insurers face less competition in their underwriting business.

About the speaker:
Professor CHENG, Jiang is currently a faculty member at Lingnan University in Hong Kong SAR. Prior to joining Lingnan, he taught at Shanghai Jiao Tong University and Shanghai University of Finance and Economics. He has also served as a visiting professor at National Taiwan University and Indiana State University. With over 20 academic papers published in leading finance, insurance, and actuarial science journals such as JFQA, JRI, Geneva Review, and NAAJ, he is a highly accomplished researcher. He was the primary investigator for the general project of the National Natural Science Foundation of China, as well as numerous provincial and ministerial level projects. He is currently the principal investigator for two Hong Kong Research Grants Council General Research Funds. Professor CHENG has received numerous academic awards, including best paper awards in international annual conferences such as the Asia Pacific Risk and Insurance Association.

08/05/2024

[Report Title]: Quest for Talents: Attraction and Retention of Highly- Skilled Overseas Chinese in the US and Canada
[Subject of Report]: Business Administration
[Speaker]: Fang Tao (Memorial University of Newfoundland, Canada)
[Report Time]: 09:00-12:00 May 12, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
Growing numbers of Chinese living overseas have returned to China recently, partly because of discrimination faced abroad and the promise of talent policies at home. Using survey data, this article examines the factors associated with the successful economic integration of Chinese returnees, as indicated by their career and income satisfaction. Those motivated to return by talent policy are substantially more likely to be economically satisfied and satisfied with career. The perception of economic prosperity in China, the desire to find a marriage partner, and being a male also positively correlate with satisfaction, while the importance of Chinese food in the return decision negatively correlates.

17/04/2024

[Report Title]: Managing Emergency Logistics for Hazardous Materials
[Subject of Report]: Management Science and Engineering
[Speaker]: Dr. Ginger Y. Ke (School of Business, University of Newfoundland, Canada)
[Report Time]: 14:30-18:30 Apr 19, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
This presentation discusses the management of emergency logistics for hazardous materials, with a special focus on the impact of possible system disruptions on system performance. More particularly, we first present a time-varying risk assessment method that takes into consideration the population dynamism. Then the two-stage robust optimization approach is employed to formulate two mixed-integer programming models, namely the basic and expanded unit commitment models, for managing a reliable emergency response system. The locations of emergency facilities are determined in the first stage, and recourse decisions are made in the second stage after the uncertain disruptions are realized. A column-and-constraint-generation algorithm is used to solve the proposed models exactly and tested on various-sized random instances. A real-world case study with a series of numerical analyses reveals managerial insights that can be applied to facilitate more effective and efficient emergency responses. The presentation concludes with a brief discussion of possible future research directions.

16/04/2024

[Report Title]: Data driven, two stage machine learning algorithm based prediction scheme for assessing 1 year and 3 year mortality risk in chronic hemodialysis patients
[Subject of Report]: Management Science and Engineering
[Speaker]: Chen Mingzhi (Fu Jen Catholic University)
[Report Time]: 10:00-12:00 Apr 17, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

27/03/2024

[Report Title]: Revolutionizing Aircraft Recovery: Graph Attention Networks for Efficient Transportation Network Modeling and Supervised Machine Learning Approach for Solution Space Reduction
[Subject of Report]: Management Science and Engineering
[Speaker]: Wei Keji (Tongji University)
[Report Time]: 14:00-16:00 Apr 1, 2024
[Report Place]: Room 702, College of Economics and Management

Summary of the report:
After the COVID-19 pandemic’s disruptive impact on operation, the aviation sector faces a pressing need for agile operational solutions that still maintain optimal performance. To meet this challenge, we have harnessed machine learning techniques to enhance both the construction and the solving of operational models, enabling the rapid finding of solutions that are still provably optimal.

In the model construction, we have redefined the challenge as a Precedence Sequencing Problem with Precedence Costs (PSPPC). Our innovative approach utilizes graph attention networks (GATs) to significantly accelerate the construction of these networks, thereby expediting the overall solution process. On the model-solving, we introduce a supervised machine learning approach by pruning the solution space of the optimization problem. Together, these advanced methods yield exact and optimal solutions more rapidly than conventional benchmarks, demonstrating considerable promise for rapid decision-making in practice.

About the speaker:
Keji Wei current is an assistant professor at the School of Economics and Management , Tongji University. Prior to this academic role, he worked as senior operations research at Sabre Corporation. Dr.Wei obtained his PhD in Operations Research from the Dartmouth College. His research interest is large scale operations optimization, with an emphasis on aviation area. Particularly, he has designed and implemented the operations research and machine learning techniques to improve the solution quality to serve more than +20 airlines in the world.

Keji has published many refereed papers in international journals or conference proceedings, including Transportations Science, EJOR, etc. He has gotten several awards like Transportation Science Meritorious Service Awards and Agifors Anna Valicek Award. His PhD dissertation has won Best Dissertation Award at Informs AAS 2020. Also, he has served as a reviewer for numerous international journals such as Transportation Science, Transportation Research Part B,C,E, etc.

College of Economics and Management of NUAA

16/12/2024

19/11/2024

19/09/2024

05/09/2024

27/06/2024

07/06/2024

15/05/2024

14/05/2024

08/05/2024

17/04/2024

16/04/2024

27/03/2024

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Monday	09:00 - 18:00
Tuesday	09:00 - 18:00
Wednesday	09:00 - 18:00
Thursday	09:00 - 18:00
Friday	09:00 - 18:00