The second Meet a Data Scientist talk, given on November 18, 2020, featured Dr. Ben Kirtman, a Professor of Atmospheric Sciences at the University of Miami Rosenstiel School of Marine and Atmospheric Science. Dr. Kirtman uses atmosphere-ocean general circulation models to study the predictability and variability of the Earth’s climate system. Read more “Catch the Replay: Meet a Data Scientist with Ben Kirtman”
IDSC is pleased to announce the second distinguished lecture in the Data Citizens series, scholar and author Deborah Stone, PhD. Professor Emerita at Brandeis University, Dr. Stone is a political scientist whose work spans public policy, sociology, philosophy, and law. Read more “Data Citizens: A Distinguished Lecture Series presents Deborah Stone 12/10”
Hurricanes, floods, and fires, oh my: predicting extreme weather and hazards from days-to-decades is a data-intensive undertaking that is also of tremendous societal interest. We use enormous amounts of data from our best observational estimates of the state of the climate systems and a large suite of state-of-the-art climate models to make these prediction and quantify the uncertainty in these predictions. However, many decision makers and even sponsors challenge us “reduce” the uncertainty beyond that which is scientifically justifiable. This Dialogue will seek participants’ input/critiques on how we ought to confront this challenge based on some examples from the Intergovernmental Panel on Climate Change (IPCC) and others. Read more “Forecasting the Weather: Safety, Ethics, and Really Big Data 11/6”
Catch the replay of Data Intersections 2020, and hear all the presentations and the panel discussion: Otavio Bueno, Heather Krause, Yeshimabeit Milner, Mike Monteiro, Panel Discussion with Ken Goodman. (share the playlist)
Read more “Catch the Replay: Data Intersections 2020 Videos”
Artificial intelligence (AI) has great potential to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. Read more “Yelena Yesha Lecture 2/5/2020 on Medical Imaging”
Can data science promote opportunity, equality, and equity? Hear from three distinguished speakers: Nick Petersen, Robert Latham, and Lynn K. Perry, who are focused on research that addresses these issues. Read more “SSI Lecture Series: “Gigabytes for Good” 11/1/2019″
This session will provide an overview of the University’s data-driven Advanced Computing Systems design as well as the design of the new AI-ready system, TRITON, which supports the biomedical architecture. The Biomedical Informatics architecture at UM currently supports nearly 7 PB of biomedical research data and numerous projects including EHR data from three Health Care partner organizations. Read more “Overview of New TRITON AI-Ready System 9/20/2019”