Shayan Shams, Ph.D. joined the UTHealth School of Biomedical Informatics (SBMI) on September 2019 as an assistant professor and a member of the Center for Secure Artificial intelligence For hEalthcare (SAFE).
Dr. Shams' primary research interests are 1) Artificial Intelligence (AI), and unsupervised and semi-supervised deep learning and machine learning in computer vision with applications in health-care and bioinformatics, 2) statistical analysis of complex data especially within the area of healthcare, and 3) big data, High-Performance Computing (HPC), distributed computing and hardware accelerator devices (GPU, FPGA) programming. He has published in prestigious computer science conferences (such as ICDCS, IPDPS, MICCAI, ACM-BCB) and high-impact journals (such as Concurrency and Computation) He has also served as an external reviewer for several computer science conferences such as(ICDCS, MICCAI, WWW, BigData,...)
As an expert in big data and health data science, Dr. Shams is interested in the integration of AI, HPC and big data techniques to develop actionable health care models, bringing AI models to edge devices for personalized medicine and Knowledge transfer from one domain to another. His extensive experience and background in deep learning and machine learning with expertise in unsupervised, semi-supervised and transfer learning has led to the development of AI models in various domains from Radiology, Pathology, Biology to Social Sciences. He is currently working on multidisciplinary projects as collaborating with various domain experts including Radiology, Pathology, Dentistry, and Neurology.
“Medical error is 3rd most common cause of death in the US and I believe bringing AI to medicine will revolutionize diagnosis and decision-making path in medicine. This will lead to decreasing medical error rate significantly and saves lives.”