Stem cell layer

Unlocking the secrets of stem cell renewal

Diya Das / May 22, 2019

Molecular Biologist Diya Das deciphers cellular signaling and activation patterns to understand how stem cells regenerate healthy tissue after injury.

Barnes article thumbnail - output model

Leveraging parallel computation to analyze landscape evolution

Marsha Fenner / March 14, 2019

This new article by BIDS Data Science Fellow Richard Barnes introduces new ways of running hydrological models quickly by leveraging specialized processors called GPUs (graphics processing units).  Computers have been getting faster for years, but the way in which the new high-performance computers are getting faster has fundamentally changed. For instance, GPUs can be difficult to program and some types of problems do not work well on them. 

Diversity & Inclusion Working Group event image

‘Acknowledging past injustices’: UC Berkeley Division of Data Sciences holds event focused on increasing, celebrating diversity

/ March 6, 2019

Katherine Finman  |  The Daily Californian In order to increase diversity and inclusion within the data science field, the UC Berkeley Division of Data Sciences partnered with the Diversity and Inclusion Working Group of the Berkeley Institute for Data Science, or BIDS, to host an informational data research event in Doe Library on Tuesday.

Introducing TraefikProxy — a scalable and highly available proxy for JupyterHub

/ March 6, 2019

Georgiana Dolocan  |  Jupyter Blog In the JupyterHub context, the proxy is the unit in charge of directing the user requests to their notebook servers. The proxy manages a list of [user : notebook] mappings (the proxy routing table) in order to decide which request is sent where. The routing table must be continuously updated as users start and stop their servers without disrupting the requests being processed. The following drawing illustrates the proxy functionality in a JupyterHub deployment.

cervical cells

Advancing machine vision tools for the early identification of cancerous cells

/ February 15, 2019

In this new article by BIDS Senior Fellow Dani Ushizima and her colleagues at CRIC, the team introduces new computational tools for analyzing cell samples, and screening cervical cells to help researchers in the early detection of cancer of the womb. Using digitized images and incorporating deep learning techniques, the new tools improve on existing techniques for cellular analyses by detecting microscopic abnormalities accurately.