Project Description/Goals: To develop and implement rapid computational and molecular toxicology approaches for identifying toxic chemicals and safer alternatives.
Project Description/Goals: A collaboration between research scientists at Kaiser Permanente Southern California (KPSC) and academic modeling team based in University of California, Berkeley and University of California, San Francisco as part of the Outbreak Analytics and Disease Modeling (OADM) Network to improve existing pathogen surveillance toolkit (i.e., computational intractability of existing high-dimensional models and the need for enhanced nowcasting) and methodological innovations will be conducted in parallel with assessment of STLT public health department needs.
CTML Faculty Involved: Alan Hubbard Ph.D. and Alejandro Schuler Ph.D.
Project Description/Goals: This project aims to develop a regularized halfspace depth for functional data, providing the first solution to a long-existing problem of degeneracy. Building on this foundation, the next phase will focus on extending the method to account for uncertainties in sparse and noisy longitudinal observations, expanding the few depth notions designed for this type of object. Additionally, a restricted metric halfspace depth will be explored to enable the detection of shape outliers with distinct features. The project will also propose practical graphical tools for outlier detection.
Description: In the current HIV epidemic response, high variability in implementing contexts and epidemic settings demands epidemiological designs and analytic methods that are able to detect and respond to heterogeneity effectively and efficiently. This project leverages the rich data increasingly generated in the course of the HIV epidemic response and applies targeted machine learning to advance adaptive design and analytic approaches.
CTML Faculty Involved: Mark Van Der Laan Ph.D. | Maya Petersen M.D. Ph.D.
The NIH-funded Biomedical Big Data Training Program at UC Berkeley responds to the urgent need for advances in data science so that the next generation of scientists has the necessary skills for leveraging the unprecedented and ever-increasing quantity and speed of biomedical information. Big data hold the promise for achieving new understandings of the mechanisms of health and disease, revolutionizing the biomedical sciences, making the grand challenge of Precision Medicine a reality, and paving the way for more effective policies and interventions at the community and population levels. These breakthroughs require highly trained researchers who are proficient in biomedical big data science and have advanced skills at collaborating effectively across traditional disciplinary boundaries.
