Dr. Xinxin Luo | Computer Science | Best Researcher Award

Xinxin Luo is a dedicated Ph.D. candidate in Cyberspace Security at Southeast University, China, with research interests in trustworthy AI, causal inference, and spatio-temporal graph neural networks. She holds a Master’s in Intelligent Science and Technology from NUPT and a Bachelor’s in Electronic Information Engineering from Hefei Normal University. Xinxin has authored several high-impact papers and presented at international conferences, contributing to robust AI systems by integrating causal structures into machine learning. Her work blends deep theoretical knowledge with practical implementations in AI-driven forecasting and decision support.

Profile

• Scopus

Education

Xinxin Luo is pursuing her Ph.D. (2021–2025) at the School of Cyber Science and Engineering, Southeast University, focusing on trustworthy AI. She earned her Master’s degree in Intelligent Science and Technology (2018–2021) from the College of Automation & AI at NUPT, and her Bachelor’s degree in Electronic Information Engineering (2013–2017) from Hefei Normal University. Her educational path reflects a deep foundation in intelligent systems, machine learning, and electronic engineering, forming the basis for her cutting-edge work in AI and causal inference.

Experience

Xinxin has actively contributed to AI R&D through national key projects, where she developed anomaly detection algorithms and integrated ML with big data for real-time monitoring. She interned at Zhejiang Zijiang Laboratory, applying deep learning for human motion recognition. Xinxin also led a provincial project creating an interpretable SME rating system, showcasing her ability to translate theoretical insights into practical applications. Her hands-on experience spans spatio-temporal modeling, causality analysis, and real-world AI implementations in both academic and industrial environments.

Awards & Honors

Xinxin Luo has received recognition through multiple high-impact publications, including journals like Engineering Applications of AI and Journal of Artificial Intelligence Research. Her accepted conference presentations (e.g., ICCBR2024, PRICAI2024) and under-review articles in top-tier journals reflect academic excellence. She holds a patent in zero-shot learning and a software copyright for AI-based chart recognition. Her contributions to national and provincial research projects and successful leadership in AI system design highlight her innovation and technical prowess.

Research Focus

Xinxin focuses on trustworthy AI through the lens of causal inference and spatio-temporal graph neural networks. Her research explores dynamic causal structure learning for time series forecasting, addresses confounding bias, and incorporates counterfactual reasoning into predictive models. She investigates causal mechanisms behind data to enhance fairness, interpretability, and robustness in AI systems. Additionally, Xinxin integrates situational awareness with causal levels (observation, intervention, counterfactual) to improve intelligent decision-making. Her vision aims at building secure, human-centered, and future-aware AI.

 Conclusion

Xinxin Luo exemplifies the qualities of an outstanding researcher: deep technical expertise, a strong publication record, and a proven ability to translate theory into practice. By augmenting her grant-acquisition skills and widening her collaborative networks, she can elevate her already remarkable contributions to even greater international prominence. Her dedication to human-centered, trust-grounded AI renders her a highly deserving candidate for the Best Researcher Award.

Publication

1. A Novel Approach of Causality Matrix Embedded into the Graph Neural Network for Forecasting the Price of Bitcoin

   • Authors: Xinxin Luo, Wei Yin, Xiao Bo

   • Journal: Engineering Applications of Artificial Intelligence

2. Dynamic Causal Structure Learning for Spatio-Temporal Graph Forecasting

   • Authors: Xinxin Luo, Wei Yin, Zhuang Li

   • Journal: IEEE Transactions on Reliability

3. Causal Spatio-Temporal Graph Forecasting Against Confounding Bias

   • Authors: Xinxin Luo, Wei Yin, Xiao Bo, Fan Wu

   • Journal: Journal of Artificial Intelligence Research

4. Deconfounded Spatio-temporal Prediction with Causal-based Graph Neural Networks

   • Authors: Xinxin Luo, Wei Yin, Zhuang Li

   • Journal: Complex & Intelligent Systems

   • Ranking: JCR Q1

5. Multi-view Deep Generative Dual Fusion Network for Zero-shot Learning

   • Authors: Xinxin Luo, Wei Yin, Xiao Bo

   • Journal: Multimedia Tools and Applications

   • Ranking: JCR Q2

6. Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy

   • Authors: Wei Yin, Ziling Chen, Xinxin Luo, Berna Kirkulak-Uludag

   • Journal: Applied Economics Letters

   • Ranking: JCR Q3