AI Research Accepted to NeurIPS 2026 Conference

An artificial intelligence research paper prepared by a research team from the Department of Computer Science and Engineering at the University of Tennessee at Chattanooga (UTC) has been accepted into the main track of NeurIPS 2026, one of the world's leading artificial intelligence and machine learning conferences.

The research paper titled "SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning," authored by Jingbo Wang, Dr. Shaogang Ren, Kegeng Tang, and Dr. Zihao Wang, will be presented at the 2026 Conference on Neural Information Processing Systems (NeurIPS), which will take place in December in Sydney, Atlanta, and Paris. Department Head Dr. Mengjun Xie stated that this may be the first NeurIPS paper in the university's history.

It was reported that 30,709 valid main track paper submissions were made for the NeurIPS 2026 cycle, of which 7,900 were accepted, resulting in an acceptance rate of 25.7 percent. The SCDM method developed by the UTC team utilizes a meta-learning approach to understand cause-and-effect relationships in large and complex systems.