In her talk, Dr. Crawford shares insights on historical origins, labor practices, infrastructures, and epistemological assumptions that underlie the production of artificial intelligence. In doing so, she hopes to then offer new paths for thinking through the research ethics and policy implications associated with machine learning.

During the March installment of NCI’s Imaging and Informatics Community Webinar Stanford Cancer Center Director Dr. Eben Rosenthal will present on the potential impact of intraoperative molecular imaging (IMI) for head and neck cancer patients.

In this upcoming Cancer Genome Cloud (CGC) monthly webinar, Dr. Dennis Dean will share his experience working with members of the Patient-Derived Xenografts Development and Trial Centers Research Network (PDXNet) teams to make the data available through the CGC.

The latest NCI Genomic Data Commons (GDC) monthly webinar series will demonstrate how to work with the many types and formats of The Cancer Genome Atlas genomic and clinical data available in the GDC.

In this webinar, Brigham Young University’s Dr. Sam Payne will discuss how CPTAC-generated proteogenomic data and methods for multi-omics data analysis can be used to improve cancer care.

Duke University’s Dr. L. Ebony Boulware will present, “Where Clouds Meet the Ground: Democratizing Health Data to Address Community Health Equity,” at the upcoming National Institute on Minority Health and Health Disparities Seminar.

Dr. Jonas Almeida and Mr. Praphulla Bhawsar of NCI’s Division of Cancer Epidemiology and Genetics will demonstrate the power of the ImageBox2 program and how it allows for easy and fast access to large whole-slide images stored in cloud-based data repositories.

The first in a series of interactive workshops hosted by the NCI-DOE Collaboration, this presentation will set the stage for the future of computing and artificial intelligence in radiation oncology.

In this talk, Dr. Katherine Pollard, director of the Gladstone Institute of Data Science and Biotechnology, will describe how her team developed “Akita,” a deep convolutional neural network that can be used to accurately predict genome folding using only DNA sequence as the input.

In this Cancer Moonshot℠ Seminar, Vanderbilt University’s Dr. Bradley Malin will discuss how to support innovative cancer research through data privacy.