We first theoretically explore the impact of neural quantization on federated knowledge transfer across quantized DNNs and provide the convergence proof of the quantized federated learning.
This project aims to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes an investigation of computational topology and contrastive learning for manifold learning.
The goal of this project is to advance the point-cloud post-processing using deep learning method to understand the global and local manifolds of a 3D object.
The goal of this project is to develop novel deep learning algorithms for video segment hashing and identification to support efficient and accurate duplicates identification and removal from phones and cloud storages.
The goal of the project is to secure authentication of a template, especially a biometric query, without compromising the template, the database, or the query; in case of database attack or a corrupted communication channel.
The objective of this project is to provide an accurate electricity day ahead price forecasting system in presence of congestions; using data comprising of power generation from various energy plants, weather conditions, and past nodal prices; by adoption of modern deep learning techniques.
The goal of the project is to develop a comprehensive framework for multi-parameter optimization of Heterogeneous Communication Networks for 5G and beyond.
The objective of this project is for AI to select the best music for elderlies and people in need. The success of this project will lead to some amazingly transformative results in the treatment of dementia through music.
We propose a novel “Semantic Deep Learning” method to analyze the electronic health records of real patients. Our previous work as successfully used a hypergraph- based approach in the clinical text notes from Stanford Hospital’s Clinical Data Warehouse (STRIDE). Previous experiments based on ontology (i.e., domain knowledge) annotated electronic health records show that hypergraph mining is successful in finding semantic (i.e., indirect) associations. This proposed method will take the success to the next level by adding the deep learning-based embedding in place of the basic hypergraphs of the previous approaches.
In this project, we will investigate ontology-based deep learning (OBDL) algorithms to predict and explain human behaviors in health domains. The main idea of our algorithms is to consider domain knowledge in the design of deep learning models and utilize domain ontologies for explaining the deep learning models and results.