Fall Break (11/24 – 11/28)
Greetings IMCI crew,
The University of Idaho will adjourn for the week of 11/24 for Fall Break. IMCI will continue to support our collaborators as usual—though all IMCI events will be postponed until the semester resumes.
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Growing Team Research
All IMCI and IBEST participants, including faculty, postdocs, students and staff, are invited to participate in an extraordinary brainstorming session on January 30 and again on February 6 in the Clearwater room at the ISUB. The idea is that rather than assemble research teams in response to specific funding opportunities, researchers grow teams with common…
Brown Bag Lunch: Sketch Your Proposal – Nicholas Coombs
Come join us for Nicholas Coomb’s sketch your proposal where we’ll be getting ideas out in the open and on the board. Come in with an idea, leave with a proposal at this SYP Brown Bag Lunch.
Post-Brown Bag Lunch: Johan Paulsson: Single-cell and evolutionary approaches to basic microbial physiology and diagnostics
Our special guest, Johan Paulsson of Harvard Medical school put on quite the show with his exciting talk: Single-cell and evolutionary approaches to basic microbial physiology and diagnostics. Despite having such incredible technology paired with brilliant minds, we as a community continue to face challenges navigating a difficult social landscape. A Zoom recording to the…
Solve the Puzzle!
Several people reported the correct answer and students and postdocs were rewarded with chocolate at the CMCI Luncheon yesterday. The image was created by CMCI Postdoctoral Fellow Dharmesh Patel for the College of Science 2018 Photography and Graphic Arts Contest. Congratulations to CMCI student researcher Emmanuel Ijezie who submitted one of the top 5 images…
Brown Bag Lunch Series: LLM Reasoning in Healthcare: Faithful Explanations or Plausible Rationalizations?
Achieving trustworthy AI in healthcare remains a significant challenge, as existing eXplanation Artificial Intelligence (XAI) methods like attention maps or LIME/SHAP are often dismissed by clinicians and patients as uninterpretable. While the reasoning LLMs initially seemed promising for generating natural-language explanations, they often create unfaithful rationalizations that obscure a model’s true logic. To solve this,…
