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Study: When allocating scarce resources with AI, randomization can improve fairness
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency, says study from group including senior author and SDSC faculty Ashia Wilson and SES/IDPS student and lead author Shomik Jain
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AI model identifies certain breast tumor stages likely to progress to invasive cancer
The model, co-developed by SDSC faculty Caroline Uhler, could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
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AI method radically speeds predictions of materials’ thermal properties
Approach developed by team including SDSC faculty Tommi Jaakkola could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
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How to assess a general-purpose AI model’s reliability before it’s deployed
A new technique from researchers including SDSC faculty Navid Azizan enables users to compare several large models and choose the one that works best for their task.
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Tamara Broderick and Caroline Uhler awarded IMS Fellowships
IDSS faculty elected to the Institute of Mathematical Statistics Fellowship for their outstanding research and professional contributions.
