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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Brainstorming a Modeler’s Workshop
Come to the CMCI Brown Bag Lunch: Brainstorming a Modeler’s Workshop Monday, April 1612:30 – 1:30 p.m.Collaboratorium, IRIC 352 The CMCI hosts a 1-2 day modeler’s workshop during the fall of each year. And it’s time to plan for 2018! What topics do you want covered? What have you loved about past workshops? What do you want…
BBL / BYOC Series: July 23rd – Topic is TBD
Please reach out to aeldridge@uidaho.edu with topic ideas or if you’d like to lead a discussion!
BBL / BYOC Series: August 6th – Building Reliable Tools for Science Using New AI Resources
This session will be led by Jennifer Johnson-Leung and Bert Baumgaertner.
- Events | Misc Events | News
Event: Advisory Committee Meeting
We’d like to announce the Advisory Committee Meeting happening Friday, May 1. Details regarding activities and events will be announced as we get closer. For now, please mark your calendars for 05/01/2026 when we will meet as a crew.
IMCI & COBRE Co Sponsored Workshop Event: A Practical Guide to Unlocking Nutrition & Health Databases for Research
Please come join us for a special workshop happening Wednesday, April 29, from 12:30-1:30 PM, in the Collaboratorium (IRIC 352) when IMCI will host a co-sponsored event with COBRE in Nutrition & Women’s Health. Titled: A Practical Guide to Unlocking Nutrition & Health Databases for Research,Léa Dussurget & Dr. Janet Williams will provide an introductory workshop on data access and extraction…
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,…
