  As machine learning models in critical fields increasingly grapple with
multimodal data, they face the dual challenges of handling a wide array of
modalities, often incomplete due to missing elements, and the temporal
irregularity and sparsity of collected samples. Successfully leveraging this
complex data, while overcoming the scarcity of high-quality training samples,
is key to improving these models' predictive performance. We introduce
``FuseMoE'', a mixture-of-experts framework incorporated with an innovative
gating function. Designed to integrate a diverse number of modalities, FuseMoE
is effective in managing scenarios with missing modalities and irregularly
sampled data trajectories. Theoretically, our unique gating function
contributes to enhanced convergence rates, leading to better performance in
multiple downstream tasks. The practical utility of FuseMoE in the real world
is validated by a diverse set of challenging prediction tasks.
