What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction.
His Artificial intelligence study focuses mostly on Discriminative model, Eye tracking, Motion estimation, Image and Particle filter.
His work investigates the relationship between Computer vision and topics such as Robustness that intersect with problems in Video tracking and Information extraction.
His Pattern recognition study combines topics in areas such as Contextual image classification, Object detection, Background subtraction and Image retrieval.
His Machine learning research incorporates elements of Cognitive neuroscience of visual object recognition, Inference and Markov process.
His work focuses on many connections between Feature extraction and other disciplines, such as Salient, that overlap with his field of interest in Sparse matrix, Matrix decomposition, Feature vector, Low-rank approximation and Kadir–Brady saliency detector.
His most cited work include:
- Correction: Corrigendum: The Serum Profile of Hypercytokinemia Factors Identified in H7N9-Infected Patients can Predict Fatal Outcomes (1261 citations)
- Mining actionlet ensemble for action recognition with depth cameras (1170 citations)
- Learning Fine-Grained Image Similarity with Deep Ranking (664 citations)
What are the main themes of his work throughout his whole career to date?
Ying Wu mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Optics and Machine learning.
His Artificial intelligence and Feature extraction, Motion estimation, Discriminative model, Eye tracking and Tracking investigations all form part of his Artificial intelligence research activities.
Computer vision and Robustness are commonly linked in his work.
His Pattern recognition research includes elements of Contextual image classification, Image, Object detection and Feature.
His work carried out in the field of Optics brings together such families of science as Field and Nonlinear system.
His studies in Machine learning integrate themes in fields like Training set and Image retrieval.
He most often published in these fields:
- Artificial intelligence (39.71%)
- Computer vision (21.14%)
- Pattern recognition (17.28%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (39.71%)
- Optoelectronics (6.25%)
- Optics (10.85%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Optoelectronics, Optics, Electrical resistivity and conductivity and Condensed matter physics.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Computer vision and Pattern recognition.
As part of one scientific family, Ying Wu deals mainly with the area of Pattern recognition, narrowing it down to issues related to the Sequence, and often Class.
His Optics research is multidisciplinary, relying on both Field, Demodulation and Sideband.
His study on Electrical resistivity and conductivity also encompasses disciplines like
- Metal which intersects with area such as Parasitic element,
- Analytical chemistry that connect with fields like Insulator.
Ying Wu interconnects Visualization and Discriminative model in the investigation of issues within Feature extraction.
Between 2018 and 2021, his most popular works were:
- Semi-Supervised Transfer Learning for Image Rain Removal (66 citations)
- Single-photon-induced phonon blockade in a hybrid spin-optomechanical system (36 citations)
- N-Phonon Bundle Emission via the Stokes Process. (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Machine learning
Ying Wu focuses on Optics, Artificial intelligence, Field, Photon and Magnetic field.
Ying Wu usually deals with Optics and limits it to topics linked to Demodulation and Tunable laser and Sampling.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His research in Pattern recognition intersects with topics in Image and Inverse problem.
His study in Field is interdisciplinary in nature, drawing from both Quantum dynamics, Quantum electrodynamics, Sideband, Nonlinear system and Electromagnetically induced transparency.
Ying Wu has included themes like Phonon, Quantum chaos, Quantum information science and Atomic physics in his Photon study.
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