Computational Medicine and Health
Faculty in this group leverage advances in computing to enable scientific discovery in the biomedical data sciences and their application to patient care and health. Meet some of the faculty active in this group below.
Associate Professor of Clinical Pharmacy and Director of the Quantitative Drug and Disease Modeling Center
Titus Family Department of Clinical Pharmacy
“Computing is essential in all aspects of medicine, starting with drug development. Various computational approaches are used for computer-aided drug design and in silico mechanism-based modeling, from the preclinical to clinical phases. These methods provide better insights into pharmacokinetics (how the body processes the drug) and pharmacodynamics (how the drug affects the body once it reaches its target site), helping to design optimized dosing regimens for translation to the clinic. Next, population modeling using clinical trial data helps us gain insights into inter-individual variability in drug exposure and activity, acknowledging that one dose does not fit all.”
As a clinically oriented researcher with an engineering background, she has developed a research program focused on addressing antimicrobial resistance (AMR). By integrating in vitro and in vivo infection models with in silico mathematical models, this approach aims to enhance our understanding of the interactions between bacterial pathogens, the host and antibiotic treatments throughout the infection process. Leveraging clinical insights about infections, this systems-based approach has enabled Rao’s lab to discover two promising drugs that could significantly aid in combating AMR.
Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science; Founding Director of the USC Center for Neurotechnology.
“In my research, we need to decode internal brains states such as mood or behavioral states, such as movements from complex brain signals. Modeling and decoding these complex brain signals require significant innovation in computing to develop novel AI/ML algorithms that can describe these complex signals and extract hidden information from them. This can lead to transformative brain-computer interfaces. We have also modeled the effect of deep brain stimulation on brain activity and mental states by developing new computing algorithms.”
Shanechi and her group are developing an entirely new generation of brain-computer interfaces that can transform treatments for diverse brain disorders, such as major
depression, which are a leading cause of disability worldwide. They do so by modeling, decoding and regulating abnormal brain activity patterns in these disorders. They have made significant progress by achieving the first decoding of mood from human brain activity, by modeling the effect of brain stimulation therapy on brain activity, and by developing AI algorithm that can substantially improve the modeling and decoding of diverse brain states.
Professor of Ophthalmology, Pediatrics, Neurology, Psychiatry and the Behavioral Sciences, Radiology, Biomedical Engineering and Electrical Engineering
“Deep learning methods allow us to predict clinical decline and discover genomic markers associated with Alzheimer’s. These AI models are over 90% accurate in detecting Alzheimer’s from brain scans, a significant improvement from traditional methods.”
In 2019, Thompson and his team focused on identifying potential blood-based markers for early Alzheimer’s detection by using machine learning. These methods can analyze vast amounts of neuroimaging data, genetic information and other biomarkers to predict disease progression and identify early signs of Alzheimer’s disease with unprecedented accuracy. According to Thompson, one algorithm learned from reviewing more than 85,721 MRI scans from 50,876 patients, while another learned from poring over the 3 billion letters of the human genome to find signs of Alzheimer’s.
Thompson is the co-founder and director of the ENIGMA Consortium, a group of over 2000 researchers in 45 countries, dedicated to understanding brain structure and function, based on MRI, DTI, fMRI, genetic data and many patient populations.