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Ryan Orban

Ryan Orban

Subject
6 entries

Academia

Bookmarks

  1. Machine Learning and Complexity Theory

    A theoretical computer scientist's reflection on the relationship between machine learning and computational complexity theory after a Dagstuhl seminar — noting that the two fields remain largely disconnected despite studying adjacent phenomena. Raises questions about whether complexity theory has useful things to say about why ML works.

  2. 50 Years of Data Science

    David Donoho's essay arguing that 'data science' is a real intellectual discipline distinct from statistics — tracing 50 years of statistical evolution toward greater empiricism, computation, and scale. A foundational text for anyone who wants to understand what data science actually is and where it came from.

  3. Berkeley Institute for Data Science (BIDS) Launch

    The December 2013 launch of Berkeley Institute for Data Science (BIDS) — with Peter Norvig arguing that job automation would require everyone to become a data scientist. A snapshot of academic data science institutionalization at the moment the discipline was coalescing.

  4. Social Machine Learning — Cambridge Computer Lab Slides

    Slides from a Cambridge Computer Lab course on social machine learning — applying ML to social network data, link prediction, community detection, and behavior modeling. A 2010-era academic reference captured at the boundary between network science and machine learning.

  5. U.S. Pushes for More Scientists, But the Jobs Aren't There

    Washington Post investigation into the mismatch between US policy pushing STEM education and the actual job market for scientists — showing a structural oversupply of PhD scientists relative to tenure-track positions and industry demand. A counternarrative to the 'STEM shortage' framing that dominated 2012 policy discourse.

  6. Is Stanford Too Close to Silicon Valley?

    Ken Auletta's New Yorker investigation into whether Stanford University has become too entangled with Silicon Valley — raising questions about whether the pursuit of startup equity compromises academic independence and the university's long-term mission.

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