What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Gene
- Enzyme
Chen Wang focuses on Crystallography, Composite material, Molecule, Nanotechnology and Non-equilibrium thermodynamics.
His work deals with themes such as Gadolinium, Hydrogen bond, Scanning tunneling microscope, Peptide and Amyloid, which intersect with Crystallography.
As part of one scientific family, Chen Wang deals mainly with the area of Composite material, narrowing it down to issues related to the Microscopy, and often Tribology, Humidity, Adsorption and Atmospheric temperature range.
His Molecule study combines topics in areas such as Self-assembly, Monolayer, Computational chemistry and Ab initio.
His research in Nanotechnology intersects with topics in Nucleation and Dislocation.
His Non-equilibrium thermodynamics study incorporates themes from Statistical physics, Energy flux, Energy level, Unified field theory and Quantum master equation.
His most cited work include:
- DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion (234 citations)
- Positive regulation of interferon regulatory factor 3 activation by Herc5 via ISG15 modification. (166 citations)
- Exact solvability of the quantum Rabi model using Bogoliubov operators (165 citations)
What are the main themes of his work throughout his whole career to date?
Chen Wang mainly focuses on Cancer research, Crystallography, Scanning tunneling microscope, Artificial intelligence and Molecule.
Chen Wang has included themes like Carcinogenesis, Metastasis and In vivo in his Cancer research study.
The Scanning tunneling microscope study combines topics in areas such as Self-assembly and Stereochemistry.
His research links Computer vision with Artificial intelligence.
He works in the field of Molecule, namely Hydrogen bond.
He most often published in these fields:
- Cancer research (11.67%)
- Crystallography (7.94%)
- Scanning tunneling microscope (7.94%)
What were the highlights of his more recent work (between 2019-2021)?
- Cancer research (11.67%)
- Artificial intelligence (6.65%)
- Computer vision (4.70%)
In recent papers he was focusing on the following fields of study:
Cancer research, Artificial intelligence, Computer vision, Cell biology and Quantum are his primary areas of study.
His studies in Cancer research integrate themes in fields like Carcinogenesis, Cancer cell, Breast cancer and Wnt signaling pathway.
Chen Wang interconnects Robot, Pipeline and Deep learning in the investigation of issues within Computer vision.
His work carried out in the field of Cell biology brings together such families of science as Cell cycle, microRNA, Gene knockdown and Cytoskeleton.
The study incorporates disciplines such as Non-equilibrium thermodynamics, Condensed matter physics and Photon in addition to Quantum.
Within one scientific family, Chen Wang focuses on topics pertaining to Heat transfer under Qubit, and may sometimes address concerns connected to Quantum master equation.
Between 2019 and 2021, his most popular works were:
- TartanAir: A Dataset to Push the Limits of Visual SLAM (15 citations)
- Long noncoding RNA HULC accelerates the growth of human liver cancer stem cells by upregulating CyclinD1 through miR675-PKM2 pathway via autophagy (15 citations)
- 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints (14 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Gene
- Artificial intelligence
Chen Wang mostly deals with Cancer research, Chemical engineering, Carcinogenesis, Catalysis and Computer vision.
While working in this field, Chen Wang studies both Cancer research and Multimodal imaging.
The various areas that Chen Wang examines in his Chemical engineering study include Electrolyte, Polymer and Oxygen reduction.
His Carcinogenesis research includes elements of Liver cancer, Colorectal cancer, Stem cell and HULC.
His Computer vision research includes themes of Pipeline and Visualization, Robot, Robustness, Artificial intelligence.
When carried out as part of a general Artificial intelligence research project, his work on RGB color model, Pose and Deep learning is frequently linked to work in Inter frame, therefore connecting diverse disciplines of study.
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