Seminar Series

Our seminar series features talks from innovators from academia, industry, and national labs. These talks provide a forum for thought leaders to share their work, discuss trends, and stimulate collaboration. These monthly seminars are held onsite and virtually. Recordings are posted to a YouTube playlist.

GeoAI: Past, Present, and Future

Shawn Newsam
Shawn Newsam | UC Merced

This talk will focus on GeoAI which is the application of artificial intelligence (AI) to geographic data. First, I will briefly describe some of my work in this area over the last 25 years which has been driven largely by two themes. One theme is that spatial data is special in that space (and time) provides a rich context in which to analyze it. The challenge is how to incorporate spatial context into AI methods when adapting or developing them for geographic data—that is, to make them spatially explicit. A second theme is that location is a powerful key (in the database sense) that allows us to associate large amounts of different kinds of data. This can be especially useful, for example, for generating large collections of weakly labelled data when training machine learning models. In the second part of my talk, I’ll discuss near-term opportunities in GeoAI related to foundation models particularly for multi-modal data. Finally, I’ll point out some anticipated challenges in GeoAI as generative models like OpenAI’s generative pre-trained transformer (GPT) become pervasive.

Dr. Shawn Newsam is a Professor of Computer Science and Engineering and Founding Faculty at the University of California, Merced. He has degrees from UC Berkeley, UC Davis, and UC Santa Barbara, and did a postdoc in the Sapphire Scientific Data Mining group in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory from 2003 to 2005. (So, UC Merced is his 5th UC institution!) Dr. Newsam is the recipient of a U.S. Department of Energy Early Career Scientist and Engineer Award, a U.S. National Science Foundation Faculty Early Career Development (CAREER) Award, and a U.S. Office of Science and Technology Policy Presidential Early Career Award for Scientists and Engineers (PECASE). He has held leadership positions in SIGSPATIAL, the ACM special interest group on the acquisition, management, and processing of spatially-related information, including serving as the general and program chair of its flagship conference and as the chair of the SIG. His research interests include computer vision and machine learning particularly applied to geographic data.

Using AI to Expand What Is Possible in Cardiovascular Medicine

Geoffrey Tison
Geoffrey H. Tison | UCSF

Machine learning and artificial intelligence (ML/AI) methods have shown great promise across various industries, including in medicine. Medicine has unique characteristics, however, that can make medical data more complex and in some respects harder to analyze compared to data outside of medicine. These issues include the complicated clinical workflow and the many human stakeholders and decision makers that all contribute at various time-points to any given patient’s medical data record. In this talk, Dr. Tison will discuss the application of ML/AI approaches in medicine, focusing on his prior work spanning several cardiovascular diagnostic modalities including electrocardiograms, echocardiograms, photoplethysmography, and angiography.

Dr. Geoffrey H. Tison, MD, MPH, is an Associate Professor of Medicine and Cardiology, and faculty in the Bakar Computational Health Sciences Institute at the University of California, San Francisco (UCSF). He is a practicing cardiologist who also leads a computational research lab at UCSF ( focused on improving cardiovascular disease prediction and prevention by applying artificial intelligence and epidemiologic and statistical methods to large-scale medical data. He received the DP2 New Innovator Award from the National Institutes of Health Office of the Director, and his work has been supported by the National Institutes of Health and the Patient-Centered Outcomes Research Institute, among others.