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This research explores the optimization of radiology practices and social media strategies, led by experts like Ken Goldberg and Alper Atamturk from UC Berkeley. The work highlights innovative methods used in prostate radiology and the development of superhuman robotic performance in surgical tasks through iterative learning. Additionally, it examines the potential of crowdsourcing insights from social media to gather diverse viewpoints and improve engagement. The multidisciplinary approach integrates engineering, humanities, and public interest to shape new media developments.
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Optimization for Radiology and Social Media Ken Goldberg IEOR (EECS, School of Information, BCNM) UC Berkeley College of Engineering Research Council, May 2010
Optimization for Prostate Radiology Ken Goldberg, AlperAtamturk, Laurent El Ghaoui (IEOR) James O’Brien, Jonathan Shewchuck (EECS) I.-C. Hsu, MD, J. Pouliot, PhD (UCSF)
Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations Jur van den Berg, Stephen Miller, Daniel Duckworth, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Ken Goldberg, Pieter Abbeel University of California, Berkeley
Social Media Ken Goldberg, Gail de Kosnik, Kimiko Ryokai Alec Ross, Katie Dowd (US State Dept)
Opinion Space: Crowdsourcing Insights Scalability: n Participants, n Viewpoints n2 Peer to Peer Reviews Viewpoints are k-Dimensional Dim. Reduction: 2D Map of Affinity/Similarity Insight vs. Agreement: Nonlinear Scoring Ken Goldberg, UC Berkeley Alec Ross, U.S. State Dept
Mission To critically analyze and shape developments in new media from trans-disciplinary and global perspectives that emphasize humanities and the public interest. bcnm.berkeley.edu
Humanities Philosophy Rhetoric Journalism Art History Education Architecture iSchool Public Health Film Studies Theater IEOR BAMPFA CITRIS Music EECS Art Practice ME Technology Art/Design BioE New Media Initiative