brc

Workshops & demos for researchers: Reproducibility, Security, HPC, and Cloud. Feb 11-14 is Love Data Week!

Love Data Week 2019, graphic

Please join us for a series of events on February 11th-15th during Love Data Week

This nationwide campaign is designed to raise awareness about data management, security, sharing, and preservation. Students, researchers, librarians and data specialists are invited to attend these events to gain hands on experience, learn about resources, and engage in discussion around data needs throughout the research process.

Christopher R. HOFFMAN's picture

Improving campus services for working with sensitive data

Strands of DNA

Increasingly, researchers in a wide range of fields at UC Berkeley are applying novel data science approaches to very large sensitive and restricted data sets. Working closely with Berkeley Research Computing (BRC), the Research Data Management (RDM) Program has been helping dozens of faculty, students, and postdocs working with sensitive data by providing consulting expertise in a number of disciplines, including the biological sciences, public health, social welfare, demography, computer science, and more. The combined approach of providing data management and computation support helps researchers integrate data management and curation best practices into their larger research workflows while protecting their data. 

BRC is recruiting again: Research Computing Support internships and Domain Consultant position

Berkeley Research Computing (BRC) logo

It’s the start of Fall semester, and the Berkeley Research Computing (BRC) Program is once again recruiting for undergraduate and graduate student positions! These positions have proven to be a great way for students to contribute to the research mission of the campus, and to learn a good deal along the way.

How the Materials Project connects computational and experimental materials science

The Materials Explorer app interface in the online Materials Project database. (Credit: Materials Project)

To invent the first commercially viable electric light bulb, Thomas Edison and his assistants tested thousands of materials to use for the filament until they found one that lasted long enough. This traditional “Edisonian” trial-and-error process of materials discovery is still fundamentally how we design materials almost 150 years later. However, through a method called “materials by design,” researchers can now avoid many of the expensive dead ends that slowed Edison down.

Steve Masover's picture

Enabling cars to see at Berkeley DeepDrive

Lazar Supic

How computers see is the through-line running from Lazar Supic’s Bachelor’s degree in Electrical Engineering at the University of Belgrade, to his UC Berkeley PhD in Nuclear Engineering, to his current work as a Postdoc in UC Berkeley’s DeepDrive Industry Consortium (BDD). His work has taken a path that began with computer vision in robots, moved to gamma-ray tracking in atomic reactions, and now focuses on machine learning for automotive perception.

Steve Masover's picture

Legal scholars mining millions of bankruptcy case pages

Professors Ken Ayotte (Berkeley Law) and Jared Ellias (UC Hastings School of Law)

Large corporate bankruptcy cases don’t easily lend themselves to empirical research, according to UC Berkeley Law Professor Ken Ayotte, because “sample sizes are small, and the financial data that’s available on the company leading up to bankruptcy is usually sparse and unreliable. We know when the company files, we have some basic background information about it, and we see whether the company reorganizes or liquidates at the end of the case, but we know very little about what happens during the case to drive those outcomes.”

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