Yicong Liu

I am a PhD student in biomedical and public health data science with a clinical background in medicine and advanced training in epidemiology and biostatistics. I hold a Bachelor’s degree in Medicine and a Master’s degree in Public Health (MPH), and my research focuses on the prediction of multiple sclerosis (MS) using machine learning and genetic data.

My work integrates:

- Statistical and machine learning modeling

- Genetic and polygenic risk analysis

- Epidemiological study design

- Big health data analytics

I aim to develop robust, interpretable predictive models that can support early MS risk detection, disease stratification, and precision public health approaches. My long-term goal is to bridge clinical neurology, genomics, and artificial intelligence to improve outcomes for people living with complex neurological diseases.

My research interests focus on neurological autoimmune diseases, particularly multiple sclerosis (MS). I am interested in understanding the genetic and biological mechanisms that underlie disease susceptibility and progression by integrating genetic data with computational approaches. My work centers on the use of polygenic risk scores and machine learning methods to develop predictive models that can identify individuals at high risk and support early disease detection.