We developed CHORA, a zoomorphic robot featuring biomimetic breathing and heartbeat behaviors. Through a mixed-methods study with 30 participants, we gathered physiological data, self-reports, and interview feedback. Our findings demonstrate how haptically experienced animacy can support emotional regulation by enabling four different coping strategies.
@article{vyas2026chora,title={Haptically Experienced Animacy Facilitates Emotion Regulation: A Theory-Driven Investigation},author={Vyas, Preeti and Guta, Bereket and Zhou, Tim G. and Himam, Noor Naila and Uusberg, Andero and MacLean, Karon E.},year={2026},url={https://arxiv.org/abs/2602.07395},}
We propose pairing large deep neural networks with smaller sidekick models to improve uncertainty quantification in a computationally efficient manner. Rather than ensembling multiple training runs, we combine predictions via learned weighted averaging. Our method achieves improved accuracy and uncertainty metrics across five image classification benchmarks with only 10-20% more computation, while the smaller model rarely degrades performance.
@article{zhou2025asymmetricduos,title={Asymmetric Duos: Sidekicks Improve Uncertainty},author={Zhou, Tim G. and Shelhamer, Evan and Pleiss, Geoff},year={2025},url={https://arxiv.org/abs/2505.18636},}
