2 citations · 3 across the 10 of their papers we have counts for
10 papers
A Unified AI Approach for Continuous Monitoring of Human Health and Diseases from Intensive Care Unit to Home with Physiological Foundation Models (UNIPHY+)
Minxiao Wang, Saurabh Kataria, Juntong Ni +15
We present UNIPHY+, a unified physiological foundation model (physioFM) framework designed to enable continuous human health and diseases monitoring across care settings using ubiq…
PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation
Juntong Ni, Saurabh Kataria, Shengpu Tang +3
Photoplethysmography (PPG) is widely used in wearable health monitoring, yet large PPG foundation models remain difficult to deploy on resource-limited devices. We present PPG-Dist…
U-Cast: Learning Hierarchical Structures for High-Dimensional Time Series Forecasting
Juntong Ni, Shiyu Wang, Zewen Liu +4
Time series forecasting (TSF) is a central problem in time series analysis. However, as the number of channels in time series datasets scales to the thousands or more, a scenario w…
TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness
Zhiyuan Zhao, Juntong Ni, Shangqing Xu +3
Time-series forecasting is an essential task with wide real-world applications across domains. While recent advances in deep learning have enabled time-series forecasting models wi…
SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting
Tianning Feng, Juntong Ni, Ezequiel Gleichgerrcht +1
We present SeizureFormer, a Transformer-based model for long-term seizure risk forecasting using interictal epileptiform activity (IEA) surrogate biomarkers and long episode (LE) b…
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
Zewen Liu, Xiaoda Wang, Bohan Wang +3
Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as p…
Scalable Graph Condensation with Evolving Capabilities
Shengbo Gong, Mohammad Hashemi, Juntong Ni +2
The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size. To mitigate these issues, Graph Condensation (GC)…
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation
Juntong Ni, Zewen Liu, Shiyu Wang +2
Transformer-based and CNN-based methods demonstrate strong performance in long-term time series forecasting. However, their high computational and storage requirements can hinder l…
A Review of Graph Neural Networks in Epidemic Modeling
Zewen Liu, Guancheng Wan, B. Aditya Prakash +2
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmiss…
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
Mohammad Hashemi, Shengbo Gong, Juntong Ni +3
Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant…