General AI Applications

AI is changing how we live. I am lucky to be involved and contributing to many interesting projects orbiting applications of AI in various domains. Here are some examples.

User Experience (UI/UX, VR/AR/MR)

Multi-modal foundation models and agent-based software engineering tools have enabled numerous tasks that were deemed too complex in the past. Particularly, I am highly interested in and actively investigating opportunities in multi-disciplinary applications of HCI spanning various areas of natural science. I am looking for answers to quesitons, e.g.: What is the most intuitive and effective user interface design for natural scientists to conduct their research?

Microarray analysis

DNA Microarrays, is a way of genetic information analysis. Statistical methods have been applied in this domain to figure out which (set of) genes are important to discriminate the cancerous and non-cancerous genes. The problem is very challenging as there are so many genes (features) compared to relatively small number of patient data (samples). Thus, many feature selection methods in machine learning were developed and applied for this domain. The problem is still open and many other applications can be possible.

Mammogram analysis

Breast cancer is one of the most fatal type of cancer in women as reported by American Cancer Society. Like other types of cancers, early diagnosis of malicious tumors is the critical factor in overcoming the breast cancer. Breast cancer screening, has been used for an attempt to accomplish the early detection by periodic check up using mammogram (x-ray), ultrasound, or MRI. With the recent advances in digital mammography devices, several key issues have arisen for improving the early breast cancer detection using the digital mammograms:

First, with the massive amount of women in need of annual or bi-annual check up, how can computational image processing and machine learning techniques can help detect suspicious regions of massive amount of high resolution mammogram data, accurately?

Second, growing number of mammogram testing and false positive detections of suspicious regions increase the healthcare cost both for patients and the insurance plans. Can computational approaches reduce these false positive rates and reduce the unnecessary medical expenditures?

Third, there are different views (CC, MLO) and different devices (X-ray, sonar, MRI) that can achieve diagnostic imaging for breasts. Can we utilize multiple sources of information to improve performance or enhance the understanding of the images?