Large Language Models (LLMs) have revolutionized automated data analytics and machine learning by enabling dynamic reasoning and adaptability. While recent approaches have advanced multi-stage pipelines through multi-agent systems, they typically rely on rigid, single-path workflows that limit the exploration and integration of diverse strategies, often resulting in suboptimal predictions. To address these challenges, we propose SPIO (Sequential Plan Integration and Optimization), a novel framework that leverages LLM-driven decision-making to orchestrate multi-agent planning across four key modules: data preprocessing, feature engineering, modeling, and hyperparameter tuning. In each module, dedicated planning agents independently generate candidate strategies that cascade into subsequent stages, fostering comprehensive exploration. A plan optimization agent refines these strategies by suggesting several optimized plans. We further introduce two variants: SPIO-S, which selects a single best solution path as determined by the LLM, and SPIO-E, which selects the top k candidate plans and ensembles them to maximize predictive performance. Extensive experiments on Kaggle and OpenML datasets demonstrate that SPIO significantly outperforms state-of-the-art methods, providing a robust and scalable solution for automated data science task.

Cultural values alignment in Large Language Models (LLMs) is a critical challenge due to their tendency to embed Western-centric biases from training data, leading to misrepresentations and fairness issues in cross-cultural contexts. Recent approaches, such as role-assignment and few-shot learning, often struggle with reliable cultural alignment as they heavily rely on pre-trained knowledge, lack scalability, and fail to capture nuanced cultural values effectively. To address these issues, we propose ValuesRAG, a novel and effective framework that applies Retrieval-Augmented Generation (RAG) with in-context learning to integrate cultural and demographic knowledge dynamically during text generation. Leveraging the World Values Survey (WVS) dataset, ValuesRAG first generates summaries of values for each individual. Subsequently, we curated several representative regional datasets to serve as test datasets and retrieve relevant summaries of values based on demographic features, followed by a reranking step to select the top-k relevant summaries. ValuesRAG consistently outperforms baseline methods, both in the main experiment and in the ablation study where only the values summary was provided, highlighting ValuesRAG's potential to foster culturally aligned AI systems and enhance the inclusivity of AI-driven applications.

Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China. In this paper, we examine China's AI development process, demonstrating that it is characterized by rapid learning and differentiation, surpassing the export-oriented growth propelled by Foreign Direct Investment seen in earlier Asian industrializers. Our data indicates that China currently leads the USA in the volume of AI-related research papers. However, when we delve into the quality of these papers based on specific metrics, the USA retains a slight edge. Nevertheless, the pace and scale of China's AI development remain noteworthy. We attribute China's accelerated AI progress to several factors, including global trends favoring open access to algorithms and research papers, contributions from China's broad diaspora and returnees, and relatively lax data protection policies. In the vein of our research, we have developed a novel measure for gauging China's imitation of US research. Our analysis shows that by 2018, the time lag between China and the USA in addressing AI research topics had evaporated. This finding suggests that China has effectively bridged a significant knowledge gap and could potentially be setting out on an independent research trajectory. While this study compares China and the USA exclusively, it's important to note that research collaborations between these two nations have resulted in more highly cited work than those produced by either country independently. This underscores the power of international cooperation in driving scientific progress in AI.

This paper elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, and discovering potential cooperators etc. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard working. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.

Teamwork is cooperative, participative and power sharing. In science of science, few studies have looked at the impact of team collaboration from the perspective of team power and hierarchy. This research examines in depth the relationships between team power and team success in the field of Computer Science (CS) using the DBLP dataset. Team power and hierarchy are measured using academic age and team success is quantified by citation. By analyzing 4,106,995 CS teams, we find that high power teams with flat structure have the best performance. On the contrary, low-power teams with hierarchical structure is a facilitator of team performance. These results are consistent across different time periods and team sizes.

The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months. The virus's rapid widespread has led to a proliferation in biomedical research addressing the pandemic and its related topics. One of the essential Knowledge Discovery tools that could help the biomedical research community understand and eventually find a cure for COVID-19 are Knowledge Graphs. The CORD-19 dataset is a collection of publicly available full-text research articles that have been recently published on COVID-19 and coronavirus topics. Here, we use several Machine Learning, Deep Learning, and Knowledge Graph construction and mining techniques to formalize and extract insights from the PubMed dataset and the CORD-19 dataset to identify COVID-19 related experts and bio-entities. Besides, we suggest possible techniques to predict related diseases, drug candidates, gene, gene mutations, and related compounds as part of a systematic effort to apply Knowledge Discovery methods to help biomedical researchers tackle the pandemic.