We strive to build an interdisciplinary team working at the domain of deep learning, NLP, and computational neuroscience. Our ultimate goal is to model humans. Our core values are:

  • Experimental:  We value scientific rigor, focusing on researching under strong scientific grounds and conducting sound experiments that provide definitive and repeatable findings.  
  • Computational:  Our scientific nature is to use algorithms, mathematical models, strong theoretical background, and strong coding skills.

Lab Entry

We welcomed students from all disciplines but those with a huge passion for research.  All Master/Ph.D. students are required to publish at top-tier conferences/journals:  HCI (CHI, UIST),  NLP (ACL, EMNLP),  CV (CVPR, ICCV), Brain (Journal of Neural Engineering).

Focus Area

Although these topics are slightly different, our lab views them from these shared research challenges:

  1. Few-shot / semi-supervised learning - contributes to the development of a model that can learn quickly, and when labels are limited.
  2. Prompt-based learning - contributes to the use of prompt for learning without any fine-tuning
  3. Human feedback - contributes to the use of human feedback for training neural network
  4. Low compute - contributes to solving the problem of efficiency of large model, either by distillation, pruning or quantization.
  5. Robustness - contributes to the use of adversarial or data augmentation for robust performance
  6. Cross-modal learning - contributes to learning the alignment or mapping function between different modalities.  For example, converting fMRI images to face images.
  7. Explainable AI - contributes to the development of tools/techniques or analysis of model that yield better understanding