- AIFS: AI Institute for Next Generation Food Systems [Oct 2020 - Sep 2025]. This is one of the seven NSF $20M AI Institutes.
- NSF: Data-Driven Disease Control and Prevention in Veterinary Health, Phase I ($1M) & Phase II ($5M) [Sep 2020 - Oct 2023].
- CNS Core: Medium: Collaborative: Exploring and Exploiting Learning for Efficient Network Control: Non-Stationarity, Inter-Dependence, and Domain-Knowledge. [Oct. 2019 - Sept. 2022].
- NeTS: Small: Learning-Guided Network Resource Allocation: A Closed-Loop Approach. [Sept. 2017 - Aug. 2020].
- BIGDATA: IA: A multi-level approach for global optimization of the surveillance and control of infectious disease in the swine industry [Sept. 2018- Aug. 2022]
- EARS: Utilizing Diverse Spectrum Bands in Cellular Networks - A Unified Information Learning and Decision Making Approach. [2016 - 2019].
- NSF: The Power of Online Learning in Stochastic System Optimization. [2014 - 2017].
- WiFiUS: Collaborative Research: Data-Guided Resource Management for Dense Heterogeneous Networks. [2015 - 2018].
AI-based Sensing in Healthcare
We collaborate with various PIs in the UC Davis Health Systems on machine learning applications in healthcare using sensing technologies, including neural muscular disease identification, ICU patient monitoring, hospitalization readmission prediction, and healthy aging. [more info in the UC-DASH project page]
Machine Learning Algorithm Development
Our effort on ML algorithm development focuses on reinforcement learning algorithms, including multi-armed bandits. We study how to bring RL to real-world applications by addressing challenges such as constraints, data efficiency, and explainability. We also study security and privacy issues of machine learning algorithms.
We focus on deep DDN uses network measurement and user behavior data, based on machine learning techniques, and control/optimization mechanisms, to solve network control and management challenges. We study various issues in wireless networks, including cellular network configuration, network slicing, resource management, 360-degree video transmission, user experience improvement, and encrypted network traffic classification.
Machine Learning Applications in Animal Health
We collaborate with Prof. Beatriz Martinez Lopes from UC Davis Veterinarian Medicine and Prof. Maria Clavijo from Iowa State Univ. on veterinarian health. We use machine learning methods to develop effective surveillance and control mechanisms for infectious disease and antibiotic resistence in the swine industry. The work is currently supported by two NSF grants.
- Resource Management on Mobile Platforms
- Cognitive Wireless Networks and Opportunistic Spectrum Utilization
- Green ICT
- Enterprise Wireless Mesh Network (SWiM)
- Cellular Network Security
- AT&T - Data-driving Networking; Cellular Traffic Scheduling
- Fujitsu Laboratories of America, Inc. - Hetnet
- Intel - Mesh Networks
Network Research Lab