| Presenters | Title | Poster |
| Jaimie Murdock, Robert Abbott, Douglas Cale Crowder, Saaketh Desai, Remi Dingreville, James Elliott Fowler, Anthony Garland, Prasad Iyer, Scott Steinmetz, Kevin Yarritu, Curtis Johnson, David Stracuzzi, and Jeffery Tsao | AI for Technoscientific Discovery: A Human-Inspired Architecture | |
| Joseph Phillips | A Knowledge Base for Running Distributed Science Applications | |
| Jin Xu, Anirudh Sundar, William Gay, and Larry Heck | Conversational Papers: Advancing AI-Based Research Assistants through Multimodal Interactive Conversations with Scientific Texts | |
| Jaimie Murdock, Colin Allen and Simon DeDeo | Darwin’s Semantic Voyage – Reading and Scientific Discovery | |
| Anastasia Georgiou, Somdatta Goswami, and Yannis Kevrekidis | From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics | |
| Aditya Deshmukh and Lav Varshney | Multi-objective Prompt Optimization for Scientific Discovery at the Information-Theoretic Limit | |
| Veeramakali Vignesh Manivannan, Spencer Ho, Srikar Sai Eranky, Yasaman Jafari, Taylor Berg-Kirkpatrick, Duncan Watson-Parris, Leon Bergen, Yi-An Ma, and Rose Yu | Automated Benchmarking for Evaluating Scientific Foundation Models | |
| David Bortz, Rainey Lyons, and Dan Messenger | Weak Form-Based Learning of Models for Cellular Populations | |
| Shobeir Pirayeh Gar and Allan Zhong | AI and Physics Based Integrated Model for Design Optimization of Downhole Tools | |
| Rodrigo Ventura, Matilde Valente, Vasco Guerra, and Tiago Cunha Dias | A Novel Projection Method for Physics-Informed Machine Learning: Leveraging General Physical Principles for Improved Accuracy and Robustness | |
| Hojin Kim, Romit Maulik, and Shivam Barwey | Scalable, Adaptive, and Explainable Scientific Machine Learning with Applications to Surrogate Models of Partial Differential Equations | |
| Ping-Hsuan Tsai and Traian Iliescu | Enhancing Data-Driven Variational Multiscale Reduced Order Models with Machine Learning Techniques | |