The LearnData Lab develops machine learning and data mining methods for complex relational data—graphs, knowledge bases, and multi-modal streams. We pursue two interconnected research thrusts: advancing graph-structured learning with scalable, principled models, and building trustworthy, knowledge-enhanced AI that is explainable, robust, and grounded in structured knowledge. Our work has been published at top venues including ICML, ICLR, KDD, WWW, CVPR, ACL, and AAAI.
We develop novel graph neural network/transformer architectures, self-supervised graph representation methods, and federated graph learning frameworks. Our research addresses fundamental challenges in knowledge graph reasoning, recommendation systems, and node/graph-level representation learning, with methods published at KDD, WWW, ICLR, WSDM, and CIKM. We focus on principled approaches—information-theoretic decomposition, conditional independence, and curriculum-guided optimization—that deliver both strong empirical performance and theoretical grounding.
We build AI systems that are not only powerful but also transparent, robust, and grounded in structured knowledge. Our research develops explainability methods for graph neural networks and other generative models, knowledge-based LLM steering, adversarial robustness techniques, and watermarking methods. We also investigate LLM reasoning, multi-modal question answering, and AI for scientific discovery—pushing AI toward systems humans can understand and trust.
We are always looking for motivated students and researchers to join our lab. Explore open positions or reach out to discuss potential collaborations.
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