What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Algorithm, Message passing, Belief propagation, Compressed sensing and Communication channel are his primary areas of study.
His Algorithm research integrates issues from Control theory, Mathematical optimization, Bayesian probability and Orthogonal frequency-division multiplexing.
Philip Schniter interconnects Convergence, Theoretical computer science and Applied mathematics in the investigation of issues within Message passing.
His Compressed sensing study incorporates themes from Computational complexity theory and Robustness.
His Communication channel research includes elements of Transmitter, Upper and lower bounds and Antenna.
Philip Schniter combines subjects such as Relay, Transmission, Duplex and Fading with his study of Upper and lower bounds.
His most cited work include:
- In-Band Full-Duplex Wireless: Challenges and Opportunities (1461 citations)
- On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels (1135 citations)
- Blind equalization using the constant modulus criterion: a review (804 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Algorithm, Communication channel, Message passing, Belief propagation and Compressed sensing.
His Algorithm research is multidisciplinary, relying on both Robustness, Control theory, Mathematical optimization, Signal and Noise reduction.
His Communication channel study integrates concerns from other disciplines, such as Upper and lower bounds, Decoding methods and Electronic engineering.
His study in Message passing is interdisciplinary in nature, drawing from both Theoretical computer science, Bilinear interpolation, Inverse problem, Multivariate random variable and Approximation algorithm.
As part of one scientific family, he deals mainly with the area of Belief propagation, narrowing it down to issues related to the Applied mathematics, and often Mean squared error and Minimum mean square error.
His Compressed sensing study combines topics from a wide range of disciplines, such as Phase retrieval, Inference and Pattern recognition.
He most often published in these fields:
- Algorithm (41.81%)
- Communication channel (28.88%)
- Message passing (26.29%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (41.81%)
- Message passing (26.29%)
- Compressed sensing (16.81%)
In recent papers he was focusing on the following fields of study:
Philip Schniter mostly deals with Algorithm, Message passing, Compressed sensing, Artificial intelligence and Inverse problem.
The various areas that he examines in his Algorithm study include Interference, Inference, Communication channel, Bit error rate and Noise reduction.
Philip Schniter does research in Communication channel, focusing on Equalization specifically.
His Message passing study combines topics in areas such as Theoretical computer science, Multivariate random variable, Approximation algorithm, Applied mathematics and Function.
His Applied mathematics research includes themes of Mean squared error and Belief propagation.
His Artificial intelligence research incorporates themes from Machine learning, Computer vision and Pattern recognition.
Between 2015 and 2021, his most popular works were:
- Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (195 citations)
- AMP-Inspired Deep Networks for Sparse Linear Inverse Problems (159 citations)
- Vector approximate message passing (130 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His main research concerns Algorithm, Message passing, Approximation algorithm, Belief propagation and Compressed sensing.
Philip Schniter carries out multidisciplinary research, doing studies in Algorithm and Component.
Philip Schniter focuses mostly in the field of Message passing, narrowing it down to topics relating to Robustness and, in certain cases, Low complexity.
His studies in Belief propagation integrate themes in fields like Discrete mathematics, Graphical model, Fixed point and Applied mathematics.
The concepts of his Applied mathematics study are interwoven with issues in Convergence, Approximate inference and Multivariate random variable.
His Compressed sensing research incorporates themes from Inverse problem and Bilinear form.
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