Manish Kumar

Forget one-size-fits-all preprocessing: the best way to encode numerical features for tabular deep learning hinges on the task, backbone, and even the output size.

Forget sequential robot moves: coordinated "amoebot" swarms can morph into target shapes in near-instant time.

Forget spheres: DFT calculations reveal how to control the shape of manganese sulfide nanocrystals by tuning sulfur chemical potential, paving the way for morphology-dependent property engineering.