NCKH Giảng viên

Abstract: In this talk, we would like to briefly review some real-life applications of Machine Learning for E-commerce platforms. Next, we will discuss in detail our recent approaches and results for Product Identification Problem in E-commerce

Abstract: Customer Segmentation is one of the most important topics in the marketing field. Traditional customer segmentation methods such as RFM (Recency, Frequency, and Monetary) and its variants have a major drawback of segmenting customers based only on some dimensions of customer behavior. Our research proposes a novel approach to overcome this issue. In the first step, we create the maximum possible features from the transactional dataset that can simulate customer consumption behavior. In the second step, we use GMM(Gaussian Mixture Model) combined with the feature selection method to segment our customer base. We also compare the traditional approach with a novel method using some business metrics. We show that 91% of customers in the golden group found by the novel approach making at least one transaction for six continuously consecutive months in the future, 84% of them is in the top 10% of highest monetary value customer in 2011. These number is only 76% and 74%, corresponding to the traditional method.

Abstract: In this work, we study a stochastic spatial epidemic model where the N individuals are characterized by their position and infection state. We begin with a microscopic description in which the displacement of individuals is driven by mean-field interactions, a state-dependent diffusion, and a common environmental noise. The evolution of epidemiological states is described by Poisson point processes with an infection rate depending on the distribution of other nearby individuals, also of the mean-field type. Then, we study the behavior of this system at the macroscopic level when the population size is large.

Abstract: Định lý thứ nhất của phúc lợi kinh tế trong mô hình kinh tế cổ điển là phân bổ nguồn lực ở trạng thái cân bằng sẽ là hiệu quả. Tuy nhiên, điều này không đúng khi hành vi của người tiêu dùng bị ảnh hưởng bởi các yếu tố ngoại lai. Trong nghiên cứu này, chúng tôi chứng minh là có thể dùng thuế VAT và các khoản trợ cấp để cải thiện tính hiệu quả của điểm cân bằng trong mô hình có yếu tố ngoại lai.

ChatGPT là công nghệ AI được lan truyền rộng rãi với khả năng đáng kinh ngạc trong việc trả lời các câu hỏi trong nhiều lĩnh vực khác nhau, cũng như giao tiếp rất tự nhiên với con người trong cuộc đối thoại. Tuy nhiên nền tảng công vệ về xử lý ngôn ngữ tự nhiên, học tăng cường đằng sau ChatGPT ít được đề cập tới. Thế nên trong seminar này, tôi sẽ trình bày về các kĩ thuật để ra được mô hình ChatGPT. Thêm vào đó, tôi sẽ thảo luận về các ứng dụng thiết thực của ChatGPT, cũng như những giới hạn hiện tại của mô hình này.

The co-existence of external referencing pricing (ERP) and reimbursement policy is common in many countries. Thus, this research examines whether or not the imposition of ERP is socially desirable in the presence of the reimbursement policy. For the direct sales channel, we find that the home social welfare is worse off with ERP if the home copayment rate is too high. Our main results are robust under indirect sales channels. Moreover, the home social welfare under the pharmacy purchasing-price (PPP) ERP is larger than that under the ex-factory-price (EFP) ERP if the home copayment rate is high enough. Finally, the profit of the brand-name firm under indirect sales channels is higher than that under direct sales channels if the home copayment rate is too high.

Portfolio optimization is one of the most basic skills you’ll need to acquire when actively managing your investments. In this work, the problem of choosing a portfolio that maximizes a certain utility function of asset returns is presented from the academic work to the financial industry. The utility function is a convex function of the portfolio weights, and the portfolio optimization problem can be formulated as a convex optimization problem. Then, we introduce a well-known and effective toolbox to solve this problem in the financial industry.

This talk discusses Graph Analysis and its applications in the banking industry. Graph Analysis provides a powerful tool for data analysis to understand complex relationships and patterns in the data, allowing us to make more informed decisions and improve overall performance. This talk is threefold, and we will first summarize some key notations of graphs. Secondly, we review some important algorithms in graph theory and applications. Finally, we explore real applications of the graph in the bank area.

Many random quantities in finance can be modeled as solutions of stochastic differential equations. Some well-known models are the Black-Schole model for stock prices, the Cox-Ingersoll-Ross model for spot interest rates… In many applications, stochastic differential equations do not have a closed-form solution. Therefore, it can only be solved by numerical methods. This talk presents some recent numerical approximation methods for stochastic differential equations with irregular coefficients.

We consider a continuous Gaussian random field living on a compact set T. We are interested in designing an asymptotically efficient estimator of the probability that the integral of the exponential of the Gaussian process over T exceeds a large threshold u. We propose an Asmussen–Kroese conditional Monte Carlo type estimator and discuss its asymptotic properties according to the assumptions on the first and second moments of the Gaussian random field. We also provide a simulation study to illustrate its effectiveness and compare its performance with the importance sampling type estimator of Liu and Xu (2014a)