50 citations · 65 across the 6 of their papers we have counts for
6 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…
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…
Time-bound Contextual Bio-ID Generation for Minimalist Wearables
Adiba Orzikulova, Diana A. Vasile, Fahim Kawsar +1
As wearable devices become increasingly miniaturized and powerful, a new opportunity arises for instant and dynamic device-to-device collaboration and human-to-device interaction.…
DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing
Taesik Gong, Yewon Kim, Adiba Orzikulova +4
Many applications utilize sensors in mobile devices and machine learning to provide novel services. However, various factors such as different users, devices, and environments impa…