刘宗超 Zong-Chao Liu

Ph.D student in epidmeiology and biostatistics

Peking University

Biography

I am a PhD student specializing in Epidemiology and Biostatistics at Peking University School of Oncology. My research is primarily focused on health data science, computational biology, and cancer epidemiology, with a particular emphasis on gastric cancer. Outside of my academic pursuits, I am passionate about bodybuilding and strength training, including powerlifting.

Interests

Biostatistics
Cancer Epidemiology
Health Data Science
Computational Biology

Education

PhD in Epidemiology and Biostatistics, 2022-present
Peking University
MS in Biostatistics, 2021
Columbia University
MBBS (division of Epidemiology and Biostatistics), 2019
Shandong University

Skills

Technical

Linux

Python

Data Science

SQL

Hobbies

Fitness

Cats

Cooking

Experience

Part-time Biostatistician

Beijing Anzhen Hospital

June 2023 – Present Beijing, China

Responsibilities include statistical analysis and modeling for medical research projects.

Full-time Research Assistant (Biostatistician)

Peking University Cancer Hospital & Institute

June 2021 – August 2022 Beijing, China

Conducted biostatistical analysis and research in cancer epidemiology.

Part-time Research Assistant

Department of Mental Health Data Science, New York State Psychiatry Institute

March 2020 – May 2021 New York, USA

Assisted in research projects focusing on mental health data science.

Part-time Research Assistant

Tsinghua University Vanke School of Public Health

March 2020 – May 2021 Beijing, China

Supported research activities in public health and epidemiology.

Staff Intern

Qingdao Center for Disease Control and Prevention

February 2019 – June 2019 Qingdao, China

Interned in the department of epidemiology and public health surveillance.

Intern Staff & Physician

Shandong Qianfoshan Hospital

May 2017 – July 2017 Jinan, China

Gained clinical experience and assisted in medical research projects.

Research

Leveraging Multi-Omics Biomedical Data and Machine Learning Techniques for Improving Gastric Cancer Prevention

Introduction My PhD project focuses on the integration of multi-omics data and machine learning to revolutionize primary prevention of gastric cancer.The project spearheads the fight against gastric cancer by harnessing the power of multi-omics data integration and machine learning techniques.

Jan 1, 0001 2 min read

Leveraging Multi-Omics Biomedical Data and Machine Learning Techniques for Improving Gastric Cancer Prevention

Projects

A Bayesian Model of Hurricane Trajectories

Implemented a Bayesian model using the Metropolis–Hastings algorithm to predict hurricane trajectories.

A Simulation Study to Compare Two Bootstrapping Methods for Propensity-score Matching

Conducted a simulation study to compare the variability of the average treatment effects with true variance using different bootstrapping methods in propensity-score matching.

Analyses of Daily COVID-19 Cases Across Nations

Fitted a logistic growth curve to model the COVID-19 cases data and implemented clustering algorithms to investigate the association between growth modes and countries.