As a graduate research assistant, I am assisting Thurston Arthritis Center. The following are my main collaborative works: ► Developing a deep learning algorithm based on convolutional neural networks to predict pain in patients with arthritis using ultrasound images.► Devised a tree-based methodology for obtaining meaningful biomarkers and optimal treatment rules by leveraging black-box methods with high-dimensional data from clinical trials, suggesting a new approach to interpretable AI ► Designing a methodology for estimating treatment rules and significant biomarkers for survival times of individuals in high-dimensional and longitudinal data.

As a graduate research assistant in Trauma center, I participated in The Advancing Understanding of RecOvery afteR traumA (AURORA) Study. I analyzed weak-signal data to identify meaningful biomarkers for pain-related outcomes using machine learning techniques for censor data.

I worked as an intern in Biogen Digital Health that pursues pioneering personalized medicine with machine learning in neuroscience. ► Participated in developing R-package for a doubly robust estimator of conditional average treatment effects (CATE) and personalized treatment rules based on Yadlowsky et al (2021), which will be published in 2023.► Based on Yadlowsky et al (2021), derived a doubly robust estimator of CATE in the continuous outcome setting and obtained subgroups of patients with multiple sclerosis who are benefited from certain treatments by implementing the derived estimator.

As a student analyst, I analyzed the School’s administrative data using key metrics by SAS and SQL and supported strategic decisions for the effective administration of the Gilling School of Public Health. Also, I performed data visualization using Tableau, and generated infographics from various angles for successful decision making.

National Cancer Center is the national hospital specialized in cancer under the Ministry of Health and Welfare in South Korea. As a statistical researcher in National Cancer Center, I analyzed the national health data which includes all Korean citizens in view of different types of cancer, translated the problem, and provided technical solutions and statistical perspectives. ► Analyzed and designed statistical models for risk prediction of lung cancer to support the lung screening guidelines in Korea.► Analyzed the large-scale longitudinal data on breast cancer and thyroid cancer.► Developed statistical models for smoking cessation and successfully supported the project “The international tobacco control policy evaluation using adult smokers’ cohort in Korea," cosigned by the Ministry of Health and Welfare of the South Korean government.

As an intern at KB Securities (formerly Hyundai Securities), I worked in Smart Marketing Team, supporting systematic marketing strategies.► Conducted data analysis of clients (more than 3,000,000) and automated generating weekly reports on active clients.► Built statistical models for predicting the behavior of dormant clients.