The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.
The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.
The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members. More information about signing up for classes.
I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni.
Professor Deirdre K. Mulligan was principal deputy U.S. chief technology officer at the White House Office of Science and Technology Policy and director of the National Artificial Intelligence Initiative Office (NAIIO) in the Biden-Harris Administration.
Wednesday, October 23, 2024, 12:10 pm
- 1:30 pm PDT
Who should make decisions about ethical and responsible technology deployments? And how do impacted communities make political claims over data technologies?
Wednesday, September 18, 2024, 4:10 pm
- 6:00 pm PDT
When identifying organ transplant recipients — and in other matching problems — is it better to find a match more quickly, or more slowly and carefully? Afshin Nikzad’s research weighs the tradeoffs in different circumstances.
Can we combine data from satellites, mobile phones, and financial services providers with machine learning to identify the neediest people and better target humanitarian aid?
Timothy Tangherlini uses a computational folkloristic approach to analyze conversations on the social media platform Parler leading up to the January 6th attack on the Capitol.
Jevin West breaks down the threats of scientific disinformation, predatory publishing and pseudoscience, the reproducability crisis, and generative AI.
Wednesday, November 29, 2023, 12:10 pm
- 1:30 pm PST
Analyses of police misconduct rely heavily on self-reported law-enforcement data. Dean Knox proposes a research algorithm to deal with unreliable and distorted data.
Cornelia Ilin gives a high-level introduction to the transformer model architecture, using bidirectional representations from transformers (BERT) on electronic medical health records to predict pediatric patients’ diagnosis codes.
The creator of open-source projects FinRL, ElegantRL, and FinGPT outlines the deep learning revolution and his experiences applying it to the challenging domain of the financial market.
Bryan Pardo discusses his lab’s work bridging the gap between digital audio software interfaces and the musicians, podcasters, and sound artists who use the tools.
Dr. Doris Lee presents her dissertation research enabling data analysts to identify trends and patterns, generate and verify hypotheses, and detect outliers and anomalies.