What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Central processing unit
Kai Li mainly focuses on Parallel computing, Operating system, Distributed shared memory, Distributed computing and Computer architecture.
His Bus sniffing study in the realm of Parallel computing interacts with subjects such as Software quality.
While the research belongs to areas of Operating system, Kai Li spends his time largely on the problem of Embedded system, intersecting his research to questions surrounding Universal memory, Flash memory emulator, Read-only memory and Distributed File System.
His Distributed shared memory research integrates issues from Data diffusion machine, Shared virtual memory and Shared memory.
His Computer architecture research focuses on Benchmark and how it connects with Set.
The study incorporates disciplines such as Information retrieval and Data mining in addition to Set.
His most cited work include:
- ImageNet: A large-scale hierarchical image database (22839 citations)
- The PARSEC benchmark suite: characterization and architectural implications (2815 citations)
- Search and replication in unstructured peer-to-peer networks (1662 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Parallel computing, Operating system, Artificial intelligence, Cache and Distributed computing.
His Parallel computing study combines topics from a wide range of disciplines, such as Parallel rendering and Distributed shared memory.
His work on Operating system is being expanded to include thematically relevant topics such as Embedded system.
As a part of the same scientific family, Kai Li mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Inference.
His study explores the link between Virtual memory and topics such as Network interface that cross with problems in Software and Overhead.
His studies in Nearest neighbor search integrate themes in fields like Data structure, Locality-sensitive hashing and Information retrieval.
He most often published in these fields:
- Parallel computing (22.73%)
- Operating system (19.55%)
- Artificial intelligence (14.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (14.55%)
- Proteogenomics (4.09%)
- Proteomics (4.09%)
In recent papers he was focusing on the following fields of study:
Kai Li mainly investigates Artificial intelligence, Proteogenomics, Proteomics, Computational biology and Inference.
Kai Li combines subjects such as Machine learning and Code with his study of Artificial intelligence.
His research integrates issues of Cell, Cancer research and Histone in his study of Proteogenomics.
His work carried out in the field of Peptide brings together such families of science as Visualization, Database search engine, Information retrieval and Mass spectrometry data format.
As a member of one scientific family, Kai Li mostly works in the field of Kernel, focusing on Computational complexity theory and, on occasion, Parallel computing.
The Parallel computing study combines topics in areas such as Scheduling, Software and Graph.
Between 2017 and 2021, his most popular works were:
- Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma. (96 citations)
- Proteogenomic Characterization of Endometrial Carcinoma. (56 citations)
- Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma (43 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Central processing unit
The scientist’s investigation covers issues in Proteogenomics, Proteomics, Computational biology, Cancer research and Transcriptome.
His Proteogenomics research focuses on Identification and how it relates to Software, Peptide, Mass spectrometry data format, Visualization and Information retrieval.
Kai Li has included themes like Cancer, Wnt signaling pathway, Serous fluid, Druggability and Histone in his Proteomics study.
His Computational biology research focuses on subjects like Genomics, which are linked to Workflow and Selection.
His Cancer research research includes themes of Gene silencing, microRNA, Gene and Interactome.
His work deals with themes such as Tumor microenvironment and Cell, which intersect with Transcriptome.
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