Our Purpose
Computing and data have transformed how we address questions in medicine and health science. We are committed to supporting innovative researchers and thinkers in computational health, as well as push discovery at the intersection of digital transformation, medicine and health sciences.
The Opportunity
We have the unique opportunity to apply data analytics and computational methodologies to important health research questions in whole new ways. This will allow us to revolutionize diagnostic tools, treatments and disease tracking all with the goal of improving human health.
The following are just a few examples of how UC Noyce Initiative researchers are making an impact in computational health:
Advancing the Study of Women's Brains
Most of what we know about health and disease centers on the male body. Neuroscientists overlook aspects of the human condition relevant to half of the global population (e.g. the menstrual cycle, the pill, pregnancy, menopause). The Ann S. Bowers Women's Brain Health Initiative advances the study of women's brain health through deeply collaborative science.
Click this box to learn more about the Ann S. Bowers Women's Brain Health Initiative.
Communication Tools for Mute and Paralyzed
Assistant Professor Gopala Anumanchipalli utilizes computational models of spoken language to develop more effective and personalized communication tools for individuals who are unable to speak, including paralyzed individuals.
Click this box to learn more about this UC Noyce Initiative research project.
Making Prosthetics More Lifelike
UC Noyce Initiative researcher Jonathon Schofield uses bionic engineering and assistive robotics to improve prosthetic limbs. Schofield is part of a team of engineers, scientists and surgeons who working to make life easier for amputees through a combination of surgery, advanced machine learning and smart prosthetics.
Click this box to learn more about this UC Noyce Initiative project.
Algorithms to Prevent Heart Attacks
Associate Professor Ziad Obermeyer utilizes algorithms informed by electrocardiograms to identify patients at high risk for heart attack before one occurs by targeting them for preventative interventions. It is work that put Obermeyer on the TIME100 AI List as a global leader in AI.
Click this box to learn more about Professor Obermeyer.
Deep Learning of Alzheimers and Stroke
This multi-campus collaborative team plans to develop an accessible deep learning framework for neuropathology and neuroradiology image analysis to aid deeper phenotyping of neurodegenerative (e.g. Alzheimer’s and dementia) and cerebrovascular (e.g. stroke) diseases.
Click this box to learn more about one of the PIs behind this project.