Instructor(s): Adam Yala

CPH students will develop skills and expertise in both the computational sciences and health sciences. In this three-part course series, students learn to integrate core foundations of computational precision health in small multidisciplinary teams using a Problem Based Learning pedagogical approach. Students will learn core computational foundations including in machine learning and causal inference and they will obtain deep exposure to multiple clinical areas including cancer and cardiology.

Instructor(s): Ahmed Alaa

CPH students will develop skills and expertise in both the computational sciences and health sciences. In this three-part course series, students learn to integrate core foundations of computational precision health in small multidisciplinary teams using a Problem-Based Learning pedagogical approach. Students will learn core computational foundations including in machine learning and causal inference and they will obtain deep exposure to multiple clinical areas including cancer and cardiology.

Instructor(s): Irene Chen

In part 3 of the three-part course series, students will focus on diabetes and ethical machine learning. The students will first learn about modeling chronic diseases including working with time-series data, treatment management, and sequential decision making. Along the way, we'll examine questions of equity and fairness within these questions including access to healthcare, social determinants of health, and trust in the healthcare system.

Instructor(s): Ida Sim

CPH 201a provides the foundations for understanding and engaging with inpatient and outpatient clinical care. Student will gain deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.

Instructor(s): Irene Chen

A 2-semester course series taken during the second year of Computational Precision Health, augmenting the Cornerstone course to provide deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.

Instructor(s): Staff

For first-year CPH graduate students, this course will provide an introduction to experimental methods and research approaches in the different areas of Computational Precision Health. Ten week laboratory rotations spread out over the fall and spring quarters, summer on a needed basis. Research is conducted under the direction of an individual faculty member.

Instructor(s): Staff

Individual research under the supervision of a faculty member.

Instructor(s): Ahmed Alaa, Irene Chen, Adam Yala

Computational precision health is a rapidly evolving field at the intersection of computational (computer science, data science, statistics) and health sciences (clinical medicine, population health, clinical research). The seminar consists of discussion of recent literature in CPH, guest speakers from across the programs faculty and beyond, presentations by second-year students on work completed during lab rotations, and presentations by third-year students on active dissertation research.