Presented by Shayan Shams, Department of Data Science 

REGISTER HERE

While the ubiquitous utilization of Artificial intelligence in healthcare applications has been expected for half a century, most of the dominating approaches of AI in healthcare have relied on expert knowledge, handcrafted feature selection, and rule fit algorithms. The emergence of the Data Science field and advances in Deep Learning and their impressive success in various fields such as speech recognition, image interpretation, and language translation have consequently led to a radical shift in the utilization of AI in healthcare.

Furthermore, with the adoption of Electronic Health Records (EHR), it is feasible to access and analyze data from millions of patients, detect patterns, and provide smarter healthcare. Advances in computational power paired with massive amounts of data make many clinical problems great candidates for developing innovative Deep Learning models to solve real-world problems in medicine. In this presentation, Dr. Shams will go over some of his current projects involving the utilization of Deep Learning techniques in various healthcare domains and demonstrate how Data Science and AI can improve patient care and healthcare delivery in rehabilitation and cancer prevention and therapy.

Dr. Shams is an Assistant Professor at the Department of Data Science, at San José State University. Dr. Shams is a computer scientist with extensive experience in Artificial Intelligence, and image and signal processing. His group has developed new machine learning-based methodologies to discover computational biomarkers from patterns in biomedical data.

Dr. Shams has successfully led multidisciplinary grant applications in developing new biomedical technologies with multiple collaborators. Currently, Dr. Shams is serving as the PI on a $900,000 multidisciplinary grant from the Ovarian Cancer Research Alliance to develop an AI framework for predicting efficacy of therapy in ovarian cancer.