Yanzhi Wang

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Algorithm
• Quantum mechanics

Yanzhi Wang mostly deals with Artificial intelligence, Energy storage, Distributed computing, Electronic engineering and Artificial neural network. His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition. His Energy storage research integrates issues from Electricity generation, Stand-alone power system, Electrical engineering and Electric potential energy.

His Distributed computing study incorporates themes from Wireless, Scheduling, Energy consumption and Mobile cloud computing. His Electronic engineering study also includes

• Control reconfiguration which intersects with area such as Series and parallel circuits,
• Power together with Voltage and Energy. His Artificial neural network study integrates concerns from other disciplines, such as Matrix and Algorithm, Fast Fourier transform, Random number generation.

His most cited work include:

• GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2 (1948 citations)
• Thermal transport through a one-dimensional quantum spin-1/2 chain heterostructure: The role of three-site spin interaction (213 citations)
• CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices (144 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Electronic engineering, Artificial neural network, Pruning and Computer engineering. His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition. His Electronic engineering study combines topics from a wide range of disciplines, such as Power and Electronic circuit, Electrical engineering, Voltage.

Yanzhi Wang combines subjects such as Battery and Energy storage with his study of Electrical engineering. Yanzhi Wang interconnects Computation and Speedup in the investigation of issues within Pruning. His Computer engineering study also includes fields such as

• Efficient energy use which intersects with area such as Computational complexity theory and Energy consumption,
• Scalability most often made with reference to Stochastic computing.

He most often published in these fields:

• Artificial intelligence (21.59%)
• Electronic engineering (16.36%)
• Artificial neural network (15.68%)

What were the highlights of his more recent work (between 2019-2021)?

• Pruning (13.18%)
• Artificial intelligence (21.59%)
• Artificial neural network (15.68%)

In recent papers he was focusing on the following fields of study:

Yanzhi Wang focuses on Pruning, Artificial intelligence, Artificial neural network, Computer engineering and Inference. The various areas that he examines in his Pruning study include Recurrent neural network, Overhead, Edge device, Data mining and Speedup. His Artificial intelligence research includes themes of Machine learning and Pattern recognition.

His Artificial neural network research is multidisciplinary, incorporating perspectives in Dram, Distributed computing, Filter, Redundancy and Computation. His studies deal with areas such as Range and Resource allocation as well as Distributed computing. Yanzhi Wang has researched Inference in several fields, including Optimizing compiler and Algorithm.

Between 2019 and 2021, his most popular works were:

• AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates. (37 citations)
• Adversarial T-shirt! Evading Person Detectors in A Physical World (31 citations)
• PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices. (30 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Quantum mechanics
• Algorithm

His primary areas of investigation include Pruning, Artificial intelligence, Inference, Speedup and Computer engineering. His Pruning research is multidisciplinary, incorporating elements of Recurrent neural network, Overhead and Crossbar switch. He has included themes like Machine learning, Computation and Pattern recognition in his Artificial intelligence study.

His studies examine the connections between Computation and genetics, as well as such issues in Artificial neural network, with regards to Energy consumption and Process. His Inference research incorporates themes from Optimizing compiler and Algorithm. His studies in Computer engineering integrate themes in fields like Matrix multiplication and Performance improvement.

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