Academic Data Science

Academic Data Science

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Ida Sim: A healthcare pioneer transforming how data works for patients
Ida Sim: A healthcare pioneer transforming how data works for patients
Ida Sim has spent her career pioneering ways to make medical data more open and shareable. She helped pave the way for more accessible health data. Now, she wants CPH to train the next generation to harness artificial intelligence to develop more precise, personalized treatments. Through it all, she’s kept her role as a primary care doctor central to her work.
Ida Sim: A healthcare pioneer transforming how data works for patients
UM-Ann Arbor | MIDAS Intensifies Efforts to Build Cross-Sector Collaboration for AI in Science and Society
UM-Ann Arbor | MIDAS Intensifies Efforts to Build Cross-Sector Collaboration for AI in Science and Society
Nov. 5, 2024 - New projects at the Michigan Institute for Data and AI in Society mark its intensified effort to help academia, industry, government and community collaborate with each other to shape AI for the benefits of science and society.
UM-Ann Arbor | MIDAS Intensifies Efforts to Build Cross-Sector Collaboration for AI in Science and Society
Diversity in Backgrounds, Careers a Major Theme in Survey of Master’s Graduates | Amstat News
Diversity in Backgrounds, Careers a Major Theme in Survey of Master’s Graduates | Amstat News
The 2023 statistics and biostatistics master’s graduates who responded to the ASA follow-up survey in spring 2024 came to their graduate program with undergraduate majors in 45 fields and then—in an embodiment of John Tukey’s quote, “The best thing about being a statistician is that you get to play in everyone’s backyard”—diverged into careers and studies in a variety of sectors, regions, and fields.
Diversity in Backgrounds, Careers a Major Theme in Survey of Master’s Graduates | Amstat News
AI overwhelmingly prefers white and male job candidates in new test of resume-screening bias
AI overwhelmingly prefers white and male job candidates in new test of resume-screening bias
UW researchers tested three open-source, large language models (LLMs) and found they favored resumes from white-associated names 85% of the time, and female-associated names 11% of the time. Over the 3 million job, race and gender combinations tested, Black men fared the worst with the models preferring other candidates nearly 100% of the time.
AI overwhelmingly prefers white and male job candidates in new test of resume-screening bias