Multimodal models

…computational neuroscience and multimodal models. Research papers connect theoretical methods with applications across science and engineering. Review is double-blind. Standard submissions use six IEEE-format pages; up to four extra pages incur fees. Accepted authors must submit final papers and pay the corresponding registration fees by 12 April 2027, 23:59 AoE. Remote authors may supply prer

Multimodal large language models perform well on many visual and textual reasoning tasks, but assistive applications expose concerns about privacy and reliable real-world evaluation. This talk examines document understanding and spatial reasoning in three-dimensional environments. An agentic privacy-protection method masks personal information selectively while preserving visual context needed for question answering. SceneCritic, a symbolic evaluator, applies explicit spatial heuristics to test the semantic and geometric consistency of generated indoor layouts. Together, these projects investi

Distinguishing diatom and dinoflagellate blooms in Chesapeake Bay is difficult because its waters are optically complex and field observations rarely coincide with satellite measurements. This project combines historical phytoplankton observations, NOAA satellite bloom products, geospatial processing and ecological information in a reproducible workflow. Quality control addresses mixed pixels, land contamination, cloud, missing observations, saturation and inconsistent coordinates. Of 31,531 field-to-satellite records, 860 initially pass validation; neighbouring pixels offer further recoverabl

…We then constructed voxelwise encoding models to explain whole brain responses arising from viewing natural images from the Natural Scenes Dataset (NSD) - a large-scale fMRI dataset collected at 7T. Our results reveal that CLIP, as compared to convolution based image classification models such as ResNet or AlexNet, as well as language models such as BERT, gives rise to representations that enable better prediction