What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Artificial intelligence
Computer network, Vehicular ad hoc network, Real-time computing, Distributed computing and Simulation are his primary areas of study.
A large part of his Computer network studies is devoted to Wireless sensor network.
Yanmin Zhu interconnects Mobile computing, Global Positioning System, Mobile radio and Network packet in the investigation of issues within Vehicular ad hoc network.
His Real-time computing research includes elements of Wireless ad hoc network, Mobility model and Compressed sensing.
His Distributed computing study combines topics from a wide range of disciplines, such as Crowdsourcing, Scheduling, Rationality and Fair-share scheduling.
His Simulation study integrates concerns from other disciplines, such as Estimation theory, Assisted GPS, Location-based service and Accelerometer.
His most cited work include:
- TRAC: Truthful Auction for Location-Aware Collaborative Sensing in Mobile Crowdsourcing (280 citations)
- CDC : Compressive Data Collection for Wireless Sensor Networks (225 citations)
- Recognizing Exponential Inter-Contact Time in VANETs (214 citations)
What are the main themes of his work throughout his whole career to date?
Yanmin Zhu focuses on Computer network, Distributed computing, Real-time computing, Wireless sensor network and Vehicular ad hoc network.
The various areas that he examines in his Computer network study include Wireless, Wireless ad hoc network and Relay.
In the subject of general Distributed computing, his work in Distributed algorithm is often linked to Resource management, thereby combining diverse domains of study.
His work deals with themes such as Event, Simulation and Compressed sensing, which intersect with Real-time computing.
His Wireless sensor network research integrates issues from Sensor node and Key distribution in wireless sensor networks.
In his study, which falls under the umbrella issue of Vehicular ad hoc network, Data mining is strongly linked to Global Positioning System.
He most often published in these fields:
- Computer network (27.60%)
- Distributed computing (19.58%)
- Real-time computing (17.21%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (10.09%)
- Machine learning (5.93%)
- Artificial neural network (5.34%)
In recent papers he was focusing on the following fields of study:
Yanmin Zhu mainly investigates Artificial intelligence, Machine learning, Artificial neural network, Distributed computing and Cloud computing.
His Artificial intelligence research incorporates themes from Mobile device and Time series.
His Artificial neural network course of study focuses on Data mining and Graph and Recurrent neural network.
His Distributed computing research includes elements of Incentive, Payment and Latency.
In general Cloud computing, his work in Mobile edge computing is often linked to Lyapunov optimization and Flexibility linking many areas of study.
The Smart device study which covers Reinforcement learning that intersects with Real-time computing.
Between 2017 and 2021, his most popular works were:
- Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks (63 citations)
- A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations. (35 citations)
- CoLight: Learning Network-level Cooperation for Traffic Signal Control (32 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Artificial intelligence
His primary scientific interests are in Artificial intelligence, Artificial neural network, Speech recognition, Machine learning and Data mining.
His work on Deep learning as part of general Artificial intelligence study is frequently linked to Background subtraction, bridging the gap between disciplines.
His Deep learning research is multidisciplinary, relying on both Waveform and Simulation.
His work in Artificial neural network tackles topics such as Convolutional neural network which are related to areas like TRIPS architecture, Scheduling and Data science.
Yanmin Zhu has included themes like Point of interest, Prosperity, Preference and Hidden Markov model in his Machine learning study.
His work in the fields of Data mining, such as Big data, overlaps with other areas such as Coherence.
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