50 citations · 51 across the 5 of their papers we have counts for
5 papers
MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity
Adiba Orzikulova, Dong Min Kim, Jaehong Yoon +1
Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individu…
Multimodal Federated Learning under Dual-Axis Modality Missingness
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5
Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modal…
Wearable Foundation Models Should Go Beyond Static Encoders
Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon +8
Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined hea…
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +1
Many healthcare sensing applications utilize multimodal time-series data from sensors embedded in mobile and wearable devices. Federated Learning (FL), with its privacy-preserving…
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Adiba Orzikulova, Han Xiao, Zhipeng Li +7
Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. W…