Yasui works in the intersection of Machine Learning, Statistics, and Biology/Genetics and aims to discover new knowledge from complex high-dimensional biological data generated by the cutting-edge biotechnology to be translated into disease prevention, detection and treatment. He has published over 400 peer-reviewed research papers including those in leading clinical, biological, and methodological journals with over 37,000 citations (h-index of 100 and i10-index of 322: Google Scholar 04/01/2019). He has been involved in more than 80 peer-review-funded projects as the Principle Investigator or a key Co-Investigator, mainly from CIHR and US NIH. He has collaborated extensively with scientists in Canada, USA, Europe and Asia and has trained over 40 graduate-level/postdoctoral trainees from diverse backgrounds (clinical, biological, and social sciences as well as computational and mathematical/statistical sciences). An example of translational products from Yasui’s research is the development of AutoMODS, an inexpensive, faster, safer system for detection of tuberculosis (TB) for use in resource-limited settings, in collaboration with TB/HIV Research Foundation, an NGO in Chiang Rai, Thailand, using automated image capture/analysis approaches his team developed. AutoMODS has received the First Prize in the 2013 National Competition for Innovation in Thailand and the NGO has applied for patents and is disseminating its utilization nationwide.