News

Casey’s work was accepted into CBM for an active learning based AI model that can guide clinical pace-mapping procedure to intervention targets in real time for ventricular arrhythmia of the heart.

Yubo’s work was accepted to NeurIPS 2024 for establishing the identifiability of a hybrid neural-physical deep generative model via a novel use of meta-learning formulation.

Maryam’s work on adaptive task sampling for meta-learning PINNs is accepted for publication ICLR 2024.

Yubo’s work on developing PINN for cardiac electrophysiology simulation is accepted for publication in MICCAI 2023.

Nilesh’s work on developing an object-centric shape augmentation is accepted for publication in MICCAI 2023.

Xiajun and Ryan’s work on meta-learning to adapt heterogenous dynamics in time-series forecasting was accepted for publication as a spotlight in ICLR 2023.

Zhiyuan’s work in continual unsupervised representation learning was accepted for publication as a spotlight in ICLR 2023.

Linwei, in collaboration with colleagues at Rutgers University and University of Virginia, were awarded a new NSF CISE core grant on developing the methodological and computing infrastructure for Smart Surrogates!

Maryam and Nilesh’s joint work on hybrid mechanistic-neutral modeling of forward imaging operators in ECGI was accepted for publication in MICCAI 2022.

Xiajun’s work on few-shot meta-learning of personalized neural surrogates for cardiac simulations was accepted for publication in MICAAI 2022.

Xiajun’s extended work on incorporating geometry to improve generalization of image reconstruction networks was accepted for publication in IEEE Transactions on Medical Imaging.

Congratulations to a successful hybrid MICCAI 2022 in Singapore, which concludes Linwei's responsibility as the Program Chair for MICCAI 2022.

Ryan and the team's work was accepted for combining a disentangling deep-learning model and a patient-specific "active" guidance model for progressively prompting pace-mapping towards the origin of VT.

Prashnna's work was accepted in ICDM 2020 for improving SSL by explicit regularization of neural function smoothness.

Xiajun MICCAI acceptance in his first year of PhD, for incorporating geometry and physics into an image reconstruction network.

Prashnna's work was accepted in MICCAI 2020 with early acceptance, for improving SSL by global mix-up.

Jwala and Pradeep's work was accepted in Medical Image Analysis for enabling Bayesian optimization over high-dimensional space via embedded generative modeling.