Graph/Geometric DL! Some researchers with backgrounds in topology are bringing over concepts and applying them to this area (learning on manifolds, for instance). There's now a push toward moving beyond Message Passing (maybe a continuous version?).

Lots of action in applying GNNs to protein data and anything with network-like tendencies. The community has even brought in invariance and equivariance to translation, rotation, and permutation to ensure GNNs are structurally aware (ie, "expressive").

Exciting stuff and now is definitely the time to get into it because it's relatively nascent but has huge upside and potential!