This competition is co-organized by the Center for Artificial Intelligence Innovation at the National Center for Supercomputing Applications. The main goal of the hackathon is to let talented UIUC students showcase their skills in a friendly competition while working on challenging problems involving computational science and machine learning using state-of-the-art computational systems at NCSA.
The competition will take place on October 25-26, 2025 at the National Center for Supercomputing Applications. We encourage you to sign up soon using this Registration Link. If you already have a team be sure to have the team lead complete the form and include the name and contact information for the full team. Deadline for registration is October 10, 2025.
Eligibility: Teams must have two or more students (undergraduate and/or graduate) with at least one currently enrolled in the Computer Science Department. Students are encouraged to form teams of up to five students.
Criteria: Teams will be evaluated on the following:
Prize: 1st place $3000, 2nd place $1500, 3rd place $750
HACKATHON PROJECT
Virtual CRISPR: Can LLMs Predict CRISPR Screen Results?
Problem: CRISPR-Cas systems enable systematic investigation of gene function, but experimental CRISPR screens are resource-intensive. Here, we would like to investigate the potential of Large Language Models (LLMs) to predict the outcomes of CRISPR screens in silico, thereby prioritizing experiments and accelerating biological discovery. Our previous work (https://aclanthology.org/2025.bionlp-1.30/) produced a benchmark dataset (https://github.com/czbiohub-chi/immune-llm-acl) derived from BioGRID-ORCS and manually curated sources. We also evaluated the performance of several LLMs across various prompting strategies, including chain-of-thought and few-shot learning. Furthermore, we proposed a novel, efficient prediction framework using LLM-derived embeddings, achieving significantly improved performance and scalability compared to direct prompting. The goal of this hackathon competition is to push this work further to improve the performance of the prior work.