Upcoming Events

The Opportunity and A Call to Action We are excited to announce the launch of the CM4AI Graph Community Detection competition on Kaggle!  Participating in this challenge will give you a unique opportunity to be part of groundbreaking advancements in biomedical research as part of the Cell Maps for AI (CM4AI) initiative. Challenge Dates: May 14, 2025 – July 31, 2025 Join the Frontier of Biomedical AI Research! The Bridge2AI Functional Genomics Grand Challenge (Cell Maps for AI/CM4AI) is pleased to announce our Kaggle competition focused on using the data and tools generated by CM4AI and leveraging emerging AI/ML methods, such as graph and quantum machine learning, to advance biomedical science and precision medicine. Competition Overview The goal of this competition is to develop methods that identify communities within biological networks to uncover hidden structures and provide new insights into biological systems. By participating, you will help push the boundaries of AI/ ML applications in the life sciences. Why Participate? Shape the Future of Science: Successful approaches can redefine how we understand cellular systems, paving the way for innovative therapeutic strategies for cancer and other human disease. Challenge Yourself: Engage in solving a cutting-edge problem that bridges the gap between AI/ML and molecular biology. Be Part of a Global Community: Collaborate and compete with experts, researchers, and enthusiasts from diverse fields with mentorship from CM4AI investigators.  Key Details CM4AI Resources: https://cm4ai.org and https://youtube.com/@CM4AI Competition Link: https://www.kaggle.com/t/b25c9b18a199411892011bfb88680cf3 Objective: Detect and identify communities from CM4AI SEC-MS data, contributing to the Cell Maps for AI initiative. Who Should Join? This competition is open to anyone passionate about artificial intelligence/machine learning, computational biology, or biomedical research. Whether you're a seasoned expert or an enthusiastic beginner, your contributions can help drive the next wave of discoveries. Don’t miss this opportunity to be part of a transformative journey at the intersection of AI and molecular biology! Join the Challenge Now

The MuCaRD challenge addresses a critical gap in AI‐driven fundus screening: ensuring consistent performance across both familiar and unseen camera systems. Participants will develop and benchmark models under realistic constraints—training on a limited set of images from one device and then evaluating robustness and adaptability on entirely new devices. By simulating clinical and commercial deployment scenarios, MuCaRD promotes methods that generalize beyond a single data source and can quickly fine‐tune to novel imaging hardware.

Please join us on Tuesday, July 15th, 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “The Pulse of Ethical Machine Learning in Health",  by Dr. Marzyeh Ghassemi Registration not required!  Additional details in the attached documents and message below. Bio: Dr. Marzyeh Ghassemi is an Associate Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES). She holds MIT affiliations with the Jameel Clinic, LIDS, IDSS, and CSAIL. For examples of short- and long-form talks Professor Ghassemi has given, see her Forbes lightning talk, and her ICML keynote.  Professor Ghassemi holds a Germeshausen Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. In 2024, she received an NSF CAREER award, and Google Research Scholar Award. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.  Professor Ghassemi’s work spans computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, The Boston Globe, and The Huffington Post.