The rapidly growing Natural Language Processing community at the University of Southern California. Our labs span a wide range of research areas. We invite you to explore the different labs to learn about us, our work, and how you can get involved.
Intelligence and Knowledge Discovery Lab is a group of reseachers working on next-generation machine intelligence techniques for label-efficient machine learning, knowledge-guided natural language processing and knowledge reasoning. Our research spans across machine learning, natural language processing and data mining, with a focus on weak-supervision methods for modeling natural-language text data and graph-structured data.
The AI, Language, Learning, Generalization, and Robustness (ALLeGRo) Lab studies natural language processing and machine learning with a focus building reliable NLP systems for a wide range of scenarios. We aim for a deeper understanding of how NLP systems work, when they fail, and how they can be improved.
DILL is a lab dedicated to the study of Data, Interpretability, Language and Learning. DILL is focused on automatically estimating the difficulty of datasets for models, efficient pretraining, and semi-automatically building datasets that help models learn better. We also emphasize on measuring how interpretable model decisions are to human users.
Our lab studies machine learning approaches to advance the frontiers of artificial intelligence. We use techniques from deep learning, reinforcement learning, probabilistic graphical models, information theory and (sometimes) take inspirations from cognitive science and neuroscience to achieve this goal.