What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Algorithm
His primary scientific interests are in Scheduling, Distributed computing, Dynamic priority scheduling, Symmetric multiprocessor system and Job shop scheduling.
His Scheduling study combines topics from a wide range of disciplines, such as Big data, Computer cluster and Parallel computing.
Kenli Li has researched Parallel computing in several fields, including Algorithm and Tuple.
His Distributed computing research incorporates elements of Workload and Real-time computing.
His Dynamic priority scheduling study frequently draws connections to adjacent fields such as Fair-share scheduling.
His research integrates issues of Mathematical optimization, Directed acyclic graph and Processor scheduling in his study of Job shop scheduling.
His most cited work include:
- A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues (227 citations)
- iProX: an integrated proteome resource. (205 citations)
- A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (180 citations)
What are the main themes of his work throughout his whole career to date?
Kenli Li mostly deals with Distributed computing, Parallel computing, Algorithm, Scheduling and Artificial intelligence.
His Distributed computing research is multidisciplinary, relying on both Workload, Real-time computing, Dynamic priority scheduling, Fair-share scheduling and Cloud computing.
His Cloud computing study incorporates themes from Quality of service, Virtual machine and Server.
His research combines Mathematical optimization and Algorithm.
His Scheduling research includes themes of Symmetric multiprocessor system, Schedule and Directed acyclic graph.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
He most often published in these fields:
- Distributed computing (23.03%)
- Parallel computing (18.76%)
- Algorithm (17.91%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (14.07%)
- Artificial neural network (6.82%)
- Distributed computing (23.03%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Artificial neural network, Distributed computing, Cloud computing and Pattern recognition.
His Artificial intelligence research includes themes of Machine learning and Task.
His research in Artificial neural network intersects with topics in Robustness, Control theory, Nonlinear system and Applied mathematics.
He interconnects Quality of service, Scheduling, Virtual machine and Mobile edge computing in the investigation of issues within Distributed computing.
His specific area of interest is Scheduling, where Kenli Li studies Job shop scheduling.
His Cloud computing research incorporates elements of Database, Resource, Encryption, Task and Nash equilibrium.
Between 2019 and 2021, his most popular works were:
- Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing (59 citations)
- Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning. (25 citations)
- A scheduling scheme in the cloud computing environment using deep Q-learning (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Algorithm
Kenli Li mainly investigates Cloud computing, Artificial intelligence, Scheduling, Convolutional neural network and Distributed computing.
His work deals with themes such as Quality of service, Database, Server and Nash equilibrium, which intersect with Cloud computing.
His study in the field of Deep learning also crosses realms of Occupancy.
His research on Scheduling often connects related topics like Swarm behaviour.
His Convolutional neural network research is multidisciplinary, relying on both Spatial relation, Data mining and Hierarchy.
His Distributed computing research is multidisciplinary, incorporating elements of Virtual machine, Overhead, Access time and Downtime.
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