What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Jie Zhang spends much of his time researching Computer security, Recommender system, Data mining, Robustness and Information retrieval.
His work deals with themes such as Scalability, Buying agent, Multi-agent system, Vehicular ad hoc network and Computer network, which intersect with Computer security.
His Recommender system study integrates concerns from other disciplines, such as Bayesian probability, Artificial intelligence and Competence.
His research in Data mining intersects with topics in Similarity, Server log and Web service.
His research in Robustness tackles topics such as Intrusion detection system which are related to areas like Peer-to-peer and Correctness.
His research investigates the link between Information retrieval and topics such as Order that cross with problems in Algorithm design, Advice and Preference.
His most cited work include:
- Improved Light Harvesting and Improved Efficiency by Insertion of an Optical Spacer (ZnO) in Solution-Processed Small-Molecule Solar Cells (438 citations)
- Efficient Solution‐Processed Small‐Molecule Solar Cells with Inverted Structure (427 citations)
- Collaborative Security: A Survey and Taxonomy (333 citations)
What are the main themes of his work throughout his whole career to date?
Jie Zhang focuses on Artificial intelligence, Computer security, Machine learning, Recommender system and Robustness.
His Artificial intelligence study typically links adjacent topics like Data mining.
His study in Information retrieval extends to Data mining with its themes.
His work is connected to Collaborative filtering and Cold start, as a part of Recommender system.
Robustness is closely attributed to Incentive in his work.
Jie Zhang is interested in Social network, which is a field of World Wide Web.
He most often published in these fields:
- Artificial intelligence (17.40%)
- Computer security (10.90%)
- Machine learning (10.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (17.40%)
- Reinforcement learning (3.25%)
- Theoretical computer science (3.71%)
In recent papers he was focusing on the following fields of study:
Jie Zhang mainly focuses on Artificial intelligence, Reinforcement learning, Theoretical computer science, Recommender system and Machine learning.
His Node research extends to Artificial intelligence, which is thematically connected.
His Reinforcement learning study combines topics in areas such as Routing, Constraint, Heuristics, Scheme and Job shop scheduling.
His research on Recommender system also deals with topics like
- Data science which is related to area like Empirical research,
- World Wide Web most often made with reference to Baseline.
His studies in Machine learning integrate themes in fields like Timeline and Focus.
His work carried out in the field of Artificial neural network brings together such families of science as Computer security, Service provider, Bernoulli's principle and Genetic algorithm.
Between 2018 and 2021, his most popular works were:
- Research commentary on recommendations with side information: A survey and research directions (29 citations)
- Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach (17 citations)
- Improving IoT Data Quality in Mobile Crowd Sensing: A Cross Validation Approach (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Heuristics, Recommender system and Reinforcement learning.
His biological study spans a wide range of topics, including Field and Random walk.
The Machine learning study combines topics in areas such as Data stream, Layer and Accounting.
His work in Heuristics tackles topics such as Travelling salesman problem which are related to areas like Vehicle routing problem and Selection.
His Recommender system study is concerned with the larger field of World Wide Web.
His Reinforcement learning research incorporates elements of Scheme, Path, Graph and Inference.
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