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Apply for the NCI Early Investigator Advancement Program 

The Early Investigator Advancement Program (EIAP) will accept applications from October 1 – November 1, 2022 for its second cohort of EIAP Scholars. With the support of the Equity Council, NCI launched EIAP in late 2021 to facilitate the advancement of scientists from diverse backgrounds to become independent investigators. Each year, EIAP supports the professional and career development of a cohort of eligible…



NIH launched the Long COVID Computational Challenge, which will award up to $500k in support of data-driven solutions that help us understand the risks of developing the condition. The primary objective of the Long COVID Computational Challenge (L3C) is to focus on the prognostic problem by developing AI/ML models and algorithms that serve as open-source tools for using structured medical records to identify which patients infected with SARS-CoV-2 have a high likelihood of developing PASC/Long…



Image result for images for cancer analyticsThe Second ECICC Community MicroLab on Cancer Challenges and Advanced Computing was held on September 25, 2019!

  • What is a MicroLab? MicroLabs are 60-90 minute, highly interactive, virtual events. Unlike webinars which are focused on disseminating information, the purpose of MicroLabs is to facilitate stimulating scientific discussions in smaller more intimate virtual breakout groups.

  • A multi-disciplinary group of over 100 clinicians, researchers, and academics in cancer and computational sciences participated in our second virtual, ECICC Community MicroLab on September 25, 2019!

  • Building on the breakout discussions from the first MicroLab held in June 2019, participants developed use cases for real-life situations and then identified what research challenges need to be overcome. The use cases were based on various personae derived from the 4 cancer challenge areas developed at the Envisioning Computational Innovations for Cancer Challenges (ECICC) Scoping Meeting held in March 2019.


MicroLab Presentations:


Presenters Included (partial list):

  • MicroLab Origins

    • Emily Greenspan, National Cancer Institute

  • Generating Large-Scale Synthetic Data to protect Personally Identifiable Information

    • Nick Anderson, University of California, Davis

    • Bill Richards, Brigham And Women's Hospital / Harvard University

  • Using Machine Learning for Iterative Hypothesis Generation

    • Amber Simpson, Queen’s University 

  • Creating a Cancer Patient “Digital Twin” to optimize personalized treatment decision-making

    • Tina Hernandez-Boussard, Stanford University

    • Paul Macklin, Indiana University

  • Developing Adaptive Cancer Treatments targeting unique tumor characteristics and trajectories

    • John McPherson, University of California, Davis

  • Use Case Demonstration

    • Paul Macklin, Indiana University


If you are interested in learning more -- or joining -- this multi-disciplinary community, please contact 


Created by Malachi Greaves Last Modified Mon April 25, 2022 4:00 pm by Lynn Borkon