David Sassoon is the founder and publisher of InsideClimate News, the nonpartisan and nonprofit news organization that won the Pulitzer Prize for National Reporting in 2013. He has been a writer, editor and publisher for 25 years, involved with public interest issues, including human rights, cultural preservation, healthcare, education and the environment. In 2003, he began researching the business case for climate action for the Rockefeller Brothers Fund. As an outgrowth of his research, Sassoon founded a blog in 2007 that has grown and evolved into InsideClimate News. He earned his undergraduate degree from Harvard University and a master’s degree from Columbia University’s Graduate School of Journalism. He is the author of “Tiny Specks in a Hurry: The Story of a Journey to Mustang.”
David Sassoon
Publisher
InsideClimate News
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The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.