What is he best known for?
The fields of study he is best known for:
- Operating system
- Electrical engineering
- Computer network
Electronic engineering, Embedded system, Integrated circuit, Thermal management of electronic devices and systems and Real-time computing are his primary areas of study.
His Electronic engineering research is multidisciplinary, relying on both System on a chip, Temporal modeling, Electronics, Process variation and Circuit reliability.
His Embedded system research incorporates elements of Energy consumption, Multiprocessing and Chip.
Li Shang combines subjects such as Thermal and Granularity with his study of Integrated circuit.
Li Shang has included themes like Motion detection, Simulation, Cluster analysis and Mobile phone in his Real-time computing study.
His study looks at the relationship between Dynamic voltage scaling and topics such as Dynamic demand, which overlap with Routing.
His most cited work include:
- Dynamic voltage scaling with links for power optimization of interconnection networks (437 citations)
- Dynamic power consumption in Virtex™-II FPGA family (362 citations)
- Accurate temperature-dependent integrated circuit leakage power estimation is easy (209 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Electronic engineering, Embedded system, Integrated circuit, Collaborative filtering and Recommender system.
His work carried out in the field of Electronic engineering brings together such families of science as Interconnection, Electrical engineering and Reliability.
As a part of the same scientific family, Li Shang mostly works in the field of Embedded system, focusing on Multiprocessing and, on occasion, Chip.
His Integrated circuit study incorporates themes from Electronics, Thermal analysis, Thermal and Leakage.
The study incorporates disciplines such as Data mining and Cluster analysis in addition to Collaborative filtering.
His Recommender system research incorporates themes from Probabilistic latent semantic analysis and Privacy preserving.
He most often published in these fields:
- Electronic engineering (21.38%)
- Embedded system (15.72%)
- Integrated circuit (10.69%)
What were the highlights of his more recent work (between 2017-2021)?
- Collaborative filtering (11.95%)
- Embedded intelligence (1.26%)
- Internet of Things (1.26%)
In recent papers he was focusing on the following fields of study:
Li Shang focuses on Collaborative filtering, Embedded intelligence, Internet of Things, Wireless and Robustness.
Particularly relevant to MovieLens is his body of work in Collaborative filtering.
His Embedded intelligence research is multidisciplinary, incorporating perspectives in Multimedia and World Wide Web.
His studies examine the connections between Robustness and genetics, as well as such issues in Photovoltaic system, with regards to Unsupervised learning, Anomaly detection and SCADA.
His studies deal with areas such as Computation and Data mining as well as Unsupervised learning.
His biological study spans a wide range of topics, including Pipeline and Video processing.
Between 2017 and 2021, his most popular works were:
- AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering (15 citations)
- Hierarchical Anomaly Detection and Multimodal Classification in Large-Scale Photovoltaic Systems (6 citations)
- Collaborative Fault Detection for Large-Scale Photovoltaic Systems (2 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Electrical engineering
- The Internet
His primary areas of investigation include Collaborative filtering, Fault detection and isolation, Photovoltaic system, Robustness and Machine learning.
His work deals with themes such as Electricity generation and Computer engineering, which intersect with Collaborative filtering.
His studies in Fault detection and isolation integrate themes in fields like Anomaly detection, Data mining, SCADA and Unsupervised learning.
His Photovoltaic system study frequently draws parallels with other fields, such as Computation.
His work on MovieLens and Stochastic gradient descent as part of his general Machine learning study is frequently connected to Generalization and Adaptive learning rate, thereby bridging the divide between different branches of science.
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