Projects

Culture & Research Philosophy

Our interdisciplinary team works at the cross-section of humans and technologies: understanding humans, human well-being, and technologies for better human lives:

  • Experimental: Conduct reproducible experiments that advance fundamental understanding.
  • Computational: Leverage algorithms, models, and coding expertise to tackle challenging questions.

Lab Entry

We welcome students from all disciplines with strong curiosity and a passion for rigorous, original research. We strive to submit our work to top-tier conferences including ACM CHI, ACL, NIPS, CVPR.

Topics

We focus topics around understanding humans, human well-being, and technologies for better human lives. We are particularly interested in the following research areas:

  1. Mental Health AI Assistant / Trainer
    We are researching how to build AI assistants that can help people with mental health issues. We are particularly interested in understanding the cognitive processes involved in mental health issues, and how to design AI assistants that can provide better support and guidance to users. Our work includes developing user studies, creating datasets for training and evaluation, and ultimately leading to more effective AI assistants that can be used in real-world applications.
  2. Mindfulness Technology
    We are researching how to leverage technology to promote mindfulness and well-being. Our work includes developing apps and tools that can help users cultivate present-moment awareness, reduce stress, and improve their overall mental health.
  3. Understanding Humans in Using AI so to build Better AI Systems
    We are researching how humans interact with AI systems, and how to design better AI systems that can understand and adapt to human needs. We are particularly interested in understanding the cognitive processes involved in using AI systems, and how to design AI systems that can provide better explanations and feedback to users. Our work includes developing user studies, creating datasets for training and evaluation, and ultimately leading to more user-friendly AI systems that can be used in real-world applications.
  4. Medical VQA
    Visual Question Answering (VQA) in the medical domain is a challenging task that requires a deep understanding of both visual data and medical knowledge. We are developing advanced VQA systems that can assist healthcare professionals by providing accurate answers to complex medical questions based on medical images such as X-rays, MRIs, and CT scans. Our research focuses on integrating multimodal data and leveraging domain-specific knowledge to improve the accuracy and reliability of these systems.
  5. BCI spellers
    Our lab has been developing BCI spellers for almost a decade. We worked with both SSVEP paragadigm, P300 paradigm, and hybrid paradigms. We have performed many iterative improvements on the system, including optimizing the visual stimuli, improving the signal processing and classification algorithms, and enhancing the user interface. The ongoing research focus is to improve the information transfer rate (ITR) and usability of the BCI spellers with new technologies such as deep learning and transfer learning.
  6. Raman spectroscopy for non-invasive glucose monitoring
    Diabetes is a major health issue worldwide, and non-invasive glucose monitoring has the potential to greatly improve the quality of life for diabetic patients. Our lab has been working on developing a non-invasive glucose monitoring system using Raman spectroscopy. Our goal is to design and develop a portable Raman system that can help elderly and patients for glucose monitoring.