Computational Precision Health

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Transforming Personal and Public Health through Computation
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A new joint PhD program

Training the next generation of talent at the intersection of computation and health
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Applying computation to real-world settings to improve the quality, efficiency, and equity of medicine and public health

The UCSF UC Berkeley Joint Program in Computational Precision Health (CPH) leverages the world leadership in computer science, engineering and statistics at UC Berkeley, clinical care, research and informatics at UCSF, and population health at both institutions to transform personal and public health through computation.

CPH exemplifies a new paradigm for using computation to improve health and healthcare through adaptive precision interventions that diagnose, treat, or prevent disease at the individual or community level  in complex real-world settings. CPH impact programs will comprise precision problem formulation, computational precision solutions that are adaptive to time, place, context, and data streams, and precision deployments that are fair, equitable, and scalable. 

CPH offers an exceptional research and training environment that includes unmatched access to high performance computing, clinical data from over 7 million patients from all five University of California medical centers, and a partnership with UCSF Health for testing and deploying AI and machine learning tools in real-world clinical practice. Our diverse and multi-disciplinary intellectual community is embedded within clinical and public health practice settings that reflect the diversity of the San Francisco Bay Area and beyond.

CPH is creating a new class of talent that will lead the world in novel algorithms and analytics to improve the quality, efficiency, and equity of medicine and public health.

ANNOUNCEMENTS: Apply to the PhD Program | Apply for the Faculty Position | Apply to Join the Graduate Group

Research Highlights

Personalized cancer screeningPersonalized Cancer Screenings
Machine learning on mammograms and clinical data to customize screening and allow for earlier detection of cancer. Data from three hospital systems show how this approach clearly outperforms current standard-of-care for breast cancer detection.
Detecting domestic violenceDetecting Domestic Violence
Combining imaging, electronic health records, and free text data to recognize injuries from domestic violence.
Transforming the computational health toolboxTransforming the Computational Health Toolbox
Integrating causal, statistical and machine learning frameworks to uncover factors driving individual differences in disease progression and response to interventions, and inform tailored health screening, prevention, and treatment.

Research

Computational Precision Health is a new paradigm that uses computation to develop and deploy adaptive precision interventions for real-world impact. CPH uses novel computational algorithms, tools, and infrastructures to formulate precision problems, develop precision solutions that adapt to time, place, and context, and deploy precision solutions more effectively.

Graduate Group research areas intersect computational and applied health domains, ranging from Artificial Intelligence/Machine Learning and Causal Inference to applications in Cancer, Digital Health, Pandemic Surveillance and Control, and many other areas.

Learn more about the: Research

Research

Training

Training

The UCSF UC Berkeley Joint Program in Computational Precision Health grants PhD degrees, and offers a Designated Emphasis for currently matriculated PhD students at UCSF or UC Berkeley.

CPH programs train students to rigorously formulate problems with direct impact on individual and population health, and to develop new computational methods to address these problems in the complexity of the real world.

Students will advance the computational precision health frontier with attention to fairness, accountability, transparency, and ethics to positively impact the future of health and healthcare.

Learn more about the: PhD Program  Designated Emphasis