Projects

Culture

We strive to build an interdisciplinary team working in the area of understanding humans and improving overall human's wellbeing. I called my area of research Human AI Interaction. It's an interdisciplinary area intersecting technology and 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.

Problems we are interested

We are mostly interested in problems related to modeling human, improving well-being and health, and generally passionate to do something better for the planet.

Modeling humans

  1. Modeling humans through EEG and physiological signals: How does EEG and physiological profile looks like when humans are stressed? engaged? mindful? etc. Then can we create an intervention system to improve these properties?
  2. Modeling humans through language: How can we model beliefs, emotion, cognition, behaviors, culture, and biases, etc. in language?
  3. Understanding humans through voice and face: Can we detect depression, confidence, humours, or risk profiles from a person's voice? What should we ask him/her to speak to detect that? How long should that voice be? Does it work for only certain languages? Does it get better results if we combine with facial features?

Health and wellness

  1. Medical Imaging and IoT: How to incorporate AI to the current workflow? What data interoperability challenges we need to concern? How to build explaninability and trust? How reasoning can be done? Can we incorporate VQA? How does traditional computer vision techniques compared with modern techniques like semi-supervised or self-supervised learning? How to deal with millions of unlabeled data? Can we use diffusion or generative AI to create infinite medical images?
  2. Raman Spectroscopy: How to measure blood glucose non-invasively using Raman spectroscopy? Can we put them into a portable low-cost machine and deploy them into households?
  3. EEG BCI Speller: How to develop an effective and user-friendly BCI speller for locked-in patients using EEG?
  4. Chatbot for emotional support: Can we incorporate CBT/DBT/ACT to chatbot to provide emotional support or assessments to people suffering from stress or depression?
  5. Chatbot for medical imaging: How to answer complex, image-based questions for medical diagnosis and treatment? How to utilize external knowledge (e.g., medical literatures, past clinical trials) to help on the current medical image case? How to combine with structured data like patient data? How to combine different views and modality for more holistic analysis?

Helping the world be a better place

  1. GIS + AI: The region is suffering from a lot of environmental problems from water sanitation, air pollution, and flood. Can we create a GIS-based application, that can track raw information in real-time, and also provide some risks calculations that can be used by government or insurance sectors?
  2. Precision agriculture: How do we use technology like IR sensors or hyperspectral drone imagery to monitor crop health and growth and for general food safety?
  3. Post-Disaster Assessment: How to develop an AI model to analyze the assessment of damage severity using satellite or other forms of imagery? Can we use change detection or segmentation techniques to compare pre- and post-catastrophe aerial images? What are some available datasets (e.g., xBD dataset)?
  4. Urban-planning: How to utilize imagery to analyze urban spaces, plan infrastructure, and monitor critical areas?

Applications

  1. Voice cloning: Thailand still do not have its own technology in terms of voice cloning or avatar generation, which will be useful in the domain of education and tourism.
  2. Car Damage Prediction: Current car damage prediction still suffers from low accuracy, mostly from outliers not existing in the dataset.
  3. ToR Report Generation: Using web + LLM to generate whole ToR report.
  4. AI Tutor: Using web + LLM to reimagine how a AI tutor should look like.