What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
His primary areas of study are Computer network, Cellular network, Distributed computing, Mobile telephony and Mobile computing.
The concepts of his Computer network study are interwoven with issues in Wireless, Mobile broadband and Relay.
His research in Cellular network intersects with topics in Resource allocation, Spectral efficiency and Base station.
His work carried out in the field of Distributed computing brings together such families of science as Cloud computing, Game theory, Markov process and Next-generation network.
His Mobile telephony research incorporates themes from Exploit, Routing, Markov chain and Social network.
His Mobile computing research includes themes of Computer security, Vehicular communication systems and Mobile search.
His most cited work include:
- A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges (635 citations)
- Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures (484 citations)
- System architecture and key technologies for 5G heterogeneous cloud radio access networks (353 citations)
What are the main themes of his work throughout his whole career to date?
Yong Li spends much of his time researching Computer network, Distributed computing, Cellular network, Artificial intelligence and Wireless.
Yong Li has included themes like Relay, Throughput and Mobile telephony in his Computer network study.
His work deals with themes such as Node, Transmission, Communication channel and Linear network coding, which intersect with Relay.
He interconnects Cloud computing and Mobile computing in the investigation of issues within Distributed computing.
His Cellular network research is multidisciplinary, relying on both Resource allocation and Mobile broadband.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Recommender system and Pattern recognition.
He most often published in these fields:
- Computer network (38.98%)
- Distributed computing (17.24%)
- Cellular network (13.51%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (11.65%)
- Machine learning (6.68%)
- Data science (4.19%)
In recent papers he was focusing on the following fields of study:
Yong Li mainly focuses on Artificial intelligence, Machine learning, Data science, Data mining and Recommender system.
All of his Artificial intelligence and Deep learning, Embedding, Feature learning, Feature and Artificial neural network investigations are sub-components of the entire Artificial intelligence study.
His Machine learning research integrates issues from Quality and Robustness.
Yong Li performs multidisciplinary study in the fields of Data science and Field via his papers.
His studies deal with areas such as Pairwise comparison and Leverage as well as Recommender system.
His Leverage study integrates concerns from other disciplines, such as Node and Filter.
Between 2019 and 2021, his most popular works were:
- Habit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population (15 citations)
- Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases (14 citations)
- PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning (13 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
Yong Li mostly deals with Artificial intelligence, Machine learning, Data science, Recommender system and Collaborative filtering.
His work in Artificial intelligence addresses issues such as Social network, which are connected to fields such as Semantics, Cluster analysis, Mobility model and Social commerce.
In Machine learning, Yong Li works on issues like Embedding, which are connected to Robustness, Variety, Causal inference and Interpretability.
His work on Analytics as part of general Data science study is frequently linked to Field, therefore connecting diverse disciplines of science.
The study incorporates disciplines such as Empirical research, Pairwise comparison and Leverage in addition to Recommender system.
Among his Set studies, there is a synthesis of other scientific areas such as Resource allocation and Distributed computing.
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