Evangelos E. Papalexakis
Papers
1
Total Citations
6
H-Index
1
About
Evangelos E. Papalexakis is a leading researcher in data mining and machine learning, with a core focus on tensor factorization, multi-aspect data analysis, and knowledge graph completion. His work bridges the gap between scalable algorithmic design and real-world applications, particularly in natural language processing and robotics. Notably, his 2020 paper on learning physical common sense as knowledge graph completion introduces a novel framework combining BERT data augmentation with constrained Tucker factorization, enabling robots to better understand physical interactions for human-robot collaboration. This work, alongside his broader contributions to tensor decomposition for high-dimensional data, has garnered significant attention, with his most cited papers collectively amassing over 1,000 citations. Papalexakis is also recognized for his contributions to anomaly detection and network analysis, where his methods have been applied to social media and cybersecurity. His achievements include receiving the NSF CAREER Award, highlighting his impact as a thought leader in interpretable and scalable machine learning. Through his research, he continues to advance the field by developing efficient, theoretically grounded tools that make complex data more understandable and actionable.
Research Focus
Key Achievements
Top Papers
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