Teaching

Education is a cornerstone for nurturing curiosity, disseminating knowledge and equipping the forthcoming generation with the indispensable tools for innovation and leadership. My teaching approach fosters a stimulating and inclusive ambiance that encourages inquiry, discussions, and the exchange of diverse perspectives. Below are the details of the courses I am currently instructing, along with relevant course materials and contact information.

BMI 5780 – Programming for Biomedical Informatics (The Second Half: Machine Learning Sections)

In the latter half of this course, students are immersed in the essential machine-learning methodologies pertinent to Biomedical Informatics and big data analysis. The segment I instruct delves into various machine learning algorithms crucial for analyzing and deriving insights from complex biomedical data.

    • Acquire a solid understanding of machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, gradient boosting, and neural networks.
    • Apply these algorithms to real-world biomedical data, developing practical skills in data preprocessing, model training, evaluation, and interpretation.
    • Foster the ability to compare machine learning algorithms and select the appropriate algorithm for a biomedical informatics problem.
  • Teaching Methods: The instruction is delivered through lectures, hands-on lab exercises, and project-based assessments to ensure a thorough understanding and practical experience with the machine learning algorithms covered.
  • Office Hours:  By appointment
  • Contact: qing.wu@osumc.edu |

Meta-Analysis in Health Science Research

This course elucidates the principles, methods, and applications of meta-analysis in health science research. Through a mixture of theoretical and practical examples, students are equipped with the skills to conduct and evaluate meta-analyses, enriching their research toolkit for evidence-based biomedical science and health research.

  • Syllabus: Download Syllabus (PDF) — to be completed
  • Learning Objectives:
    • Grasp the fundamental steps of conducting a meta-analysis, including study identification, data extraction, quality assessment, statistical analysis, and publication bias analysis.
    • Gain hands-on experience with real-world examples and practical applications using Covidence, R, and STATA statistical software.
    • Develop skills to conduct genome-wide meta-analysis and meta-learner of various machine-learning models.
  • Teaching Methods: Engaging lectures, interactive discussions, group projects, and software tutorials are employed to foster a conducive learning environment for both theoretical comprehension and practical application.
  • Office Hours: by appointment

Contact: qing.wu@osumc.edu