What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Protein structure prediction and Pattern recognition.
His work carried out in the field of Artificial intelligence brings together such families of science as Function and CASP.
His Machine learning study incorporates themes from Crop yield and Regression.
His study focuses on the intersection of Data mining and fields such as Protein function prediction with connections in the field of Network science and Biological network.
Jian Peng has researched Protein structure prediction in several fields, including Protein structure database, Structural alignment and Threading.
The Pattern recognition study which covers Sequence that intersects with Deep learning.
His most cited work include:
- Template-based protein structure modeling using the RaptorX web server (1020 citations)
- Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders (322 citations)
- Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields. (298 citations)
What are the main themes of his work throughout his whole career to date?
Jian Peng spends much of his time researching Artificial intelligence, Machine learning, Reinforcement learning, Computational biology and Algorithm.
Jian Peng interconnects Data mining and Pattern recognition in the investigation of issues within Artificial intelligence.
His research investigates the connection with Data mining and areas like Protein structure prediction which intersect with concerns in Conditional random field, Protein structure database and Structural alignment.
His research integrates issues of Language model and Biological network in his study of Machine learning.
His Computational biology research is multidisciplinary, incorporating elements of Software and Gene.
The Algorithm study combines topics in areas such as Graphical model and Threading.
He most often published in these fields:
- Artificial intelligence (46.10%)
- Machine learning (26.39%)
- Reinforcement learning (24.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (46.10%)
- Reinforcement learning (24.16%)
- Artificial neural network (12.64%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Reinforcement learning, Artificial neural network, Pattern recognition and Machine learning.
His Object detection, Deep learning and Object study in the realm of Artificial intelligence interacts with subjects such as Knowledge transfer and Function.
As a part of the same scientific family, Jian Peng mostly works in the field of Reinforcement learning, focusing on Mathematical optimization and, on occasion, Monotonic function.
As part of the same scientific family, he usually focuses on Artificial neural network, concentrating on Approximate inference and intersecting with Graphical model.
He focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Task and, in some cases, Class, Contextual image classification, Margin and Contrast.
His Machine learning research is multidisciplinary, incorporating perspectives in Smoothing and Interpretation.
Between 2019 and 2021, his most popular works were:
- Characterization of SARS-CoV-2 viral diversity within and across hosts (20 citations)
- Anchor Box Optimization for Object Detection (17 citations)
- Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Jian Peng focuses on Artificial intelligence, Algorithm, Deep learning, Artificial neural network and Pascal.
His Artificial intelligence study integrates concerns from other disciplines, such as Indirection and Machine learning.
Jian Peng undertakes multidisciplinary investigations into Machine learning and Context in his work.
His study looks at the relationship between Algorithm and topics such as Inference, which overlap with Robustness, Hybrid Monte Carlo, Ensemble learning and Graphical model.
His studies deal with areas such as Bayesian network and Causal inference as well as Deep learning.
His Pascal research integrates issues from Object detection, Boosting and Pattern recognition.
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