Chakkrit Tantithamthavorn

Biography

He is a lecturer in the School of Computer Science, the University of Adelaide. He was a postdoctoral research fellow at Queen’s University, Canada. He holds one of the most prestigious and selective sources of national funding in Japan, i.e., a JSPS Research Fellowship for Young Researchers and a Grants-in-Aid for JSPS Fellow. He won the "Best Ph.D. Student Award" for his Ph.D. study at Nara Institute of Science and Technology, Japan. During his Ph.D. study, he also spent two years as a visiting researcher at Queen’s University, Canada. His research has been published at top-tier software engineering venues, such as, IEEE Transactions on Software Engineering (TSE) and the International Conference on Software Engineering (ICSE). His research has been recognized as a TSE Journal-First invited paper at ICSE 2017 and an Outstanding Paper Award for Young C&C Researchers of NEC C&C Foundation, Japan for his ICSE 2015 paper. He was invited to present his research at world-class universities, such as, University College London (UK), McGill University (Canada), École Polytechnique de Montréal (Canada), as well as, academic conferences in the U.S., Canada, UK, Italy, New Zealand, Japan, Thailand, and Argentina. He served a referee of flagship software engineering journals, such as, Applied Soft Computing, IEEE Transactions on Software Engineering, as well as, a program committee member and an additional reviewer of ICSME, MSR, ISEC, SCAM, and SANER. To date, he acquired a total of A$140,000 prestigious research grants for his research projects that are funded by JSPS, NEC C&C Foundation, Queen’s University, MEXT, and NAIST. His current research lies in software analytics, i.e., the intersection of data science and software engineering, with a specific focus on advancing the fundamentals of predictive and statistical modelling for software engineering (e.g., software analytics) in order to produce more accurate predictions and reliable actionable insights to support software engineers, software managers, data scientists, and researchers. His Ph.D. thesis shows that the experimental components (e.g., the choice of metrics, the quality of dataset) of software analytics modelling substantially impact the predictions and associated insights, suggesting that empirical investigations on the impact of overlooked experimental components are needed to derive practical guidelines.

Research Interest

Medicine,Medical Education,Research,etc