In this series of articles, we're publishing summaries with some of the key takeaways from a few of the workshops held at the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). Recent progress in Sphere Neural Networks demonstrates various possibilities for neural networks to achieve symbolic-level reasoning. This workshop aimed to reconsider various problems and discuss walk-round solutions in the two-way street commingling of neural networks and mathematics. This workshop brought together researchers from artificial intelligence and diverse scientific domains to address new challenges towards accelerating scientific discovery and engineering design. This was the fourth iteration of the workshop, with the theme of AI for biological sciences following previous three years' themes of AI for chemistry, earth sciences, and materials/manufacturing respectively.

Accepted papers spanned a diverse range of topics in cutting edge AI research and applications. This included computer vision, multimodal learning, fairness and ethics considerations, interpretability and explainability of models, learning effective representations, continual learning, generative modeling techniques, and novel applications in healthcare among others. We gave awards to three papers which share the common goal of aligning AI models, especially large language models, with human values, preferences and social intelligence. One proposes techniques for improved controllability of language model outputs through activation steering, allowing humans to guide model behavior. Another explores hybrid natural language and feedback signals to fine-tune models towards satisfying human feedback during training itself.

Eran Halperin, SVP of AI and Machine Learning in Optum Labs and a professor in the departments of Computer Science, Computational Medicine, Anaesthesiology, and Human Genetics at UCLA, gave a keynote talk on using whole-genome methylation patterns as a biomarker for electronic health record (EHR) imputation. Dr Halperin showed that methylation provides a better imputation performance when compared to genetic or EHR data. This approach uses a new tensor deconvolution of bulk DNA methylation to obtain cell-type-specific methylation that is in turn used for imputation. Irene Chen from the Massachusetts Institute of Technology (MIT) gave a keynote describing how to leverage machine learning towards equitable healthcare. Dr Chen demonstrated how to adapt disease progression modeling to account for differences in access to care.