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.
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).
Although these topics are slightly different, our lab views them from these shared research challenges:
- Few-shot / semi-supervised learning - contributes to the development of a model that can learn quickly, and when labels are limited.
- Prompt-based learning - contributes to the use of prompt for learning without any fine-tuning
- Human feedback - contributes to the use of human feedback for training neural network
- Low compute - contributes to solving the problem of efficiency of large model, either by distillation, pruning or quantization.
- Robustness - contributes to the use of adversarial or data augmentation for robust performance
- Cross-modal learning - contributes to learning the alignment or mapping function between different modalities. For example, converting fMRI images to face images.
- Explainable AI - contributes to the development of tools/techniques or analysis of model that yield better understanding