What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Jundong Li mostly deals with Feature selection, Artificial intelligence, Machine learning, Network embedding and Big data.
His Feature selection research incorporates themes from Data pre-processing, Algorithm design, Variety, Feature extraction and Data science.
His work on Feature as part of general Artificial intelligence study is frequently linked to Task, bridging the gap between disciplines.
His Network embedding research includes elements of Structure and Feature learning.
His work in Big data tackles topics such as Perspective which are related to areas like Similarity.
His research integrates issues of Theoretical computer science and Set in his study of Embedding.
His most cited work include:
- Feature Selection: A Data Perspective (627 citations)
- Label Informed Attributed Network Embedding (281 citations)
- Accelerated attributed network embedding (180 citations)
What are the main themes of his work throughout his whole career to date?
Jundong Li mainly focuses on Artificial intelligence, Machine learning, Feature selection, Data science and Data mining.
His Artificial intelligence research incorporates elements of Graph neural networks and Pattern recognition.
Jundong Li interconnects Structure and Pairwise comparison in the investigation of issues within Machine learning.
His Feature selection research includes themes of Data pre-processing, Curse of dimensionality, Dimensionality reduction, Feature extraction and Big data.
Jundong Li focuses mostly in the field of Data pre-processing, narrowing it down to matters related to Perspective and, in some cases, Information retrieval.
He has researched Data science in several fields, including Variety and Social media.
He most often published in these fields:
- Artificial intelligence (44.64%)
- Machine learning (33.04%)
- Feature selection (17.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (44.64%)
- Machine learning (33.04%)
- Data science (14.29%)
In recent papers he was focusing on the following fields of study:
Jundong Li spends much of his time researching Artificial intelligence, Machine learning, Data science, Recommender system and Theoretical computer science.
His work on Feature selection, Anomaly detection and Pairwise comparison as part of general Artificial intelligence research is often related to Hypergraph, thus linking different fields of science.
While working on this project, he studies both Feature selection and Carcinoembryonic antigen.
His work carried out in the field of Machine learning brings together such families of science as Structure and Graph neural networks.
The study incorporates disciplines such as Adversarial system, Social relation and Evolving networks in addition to Recommender system.
Within one scientific family, Jundong Li focuses on topics pertaining to Social network analysis under Theoretical computer science, and may sometimes address concerns connected to Measure, Enhanced Data Rates for GSM Evolution, Similarity, Interpersonal ties and Statistical classification.
Between 2019 and 2021, his most popular works were:
- A Survey of Learning Causality with Data: Problems and Methods (43 citations)
- Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics (22 citations)
- Learning Individual Causal Effects from Networked Observational Data (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Data mining
Jundong Li mainly investigates Artificial intelligence, Machine learning, Observational study, Causal inference and Statistical classification.
When carried out as part of a general Artificial intelligence research project, his work on Feature selection and Decision tree model is frequently linked to work in Prediction methods and Ovarian cancer, therefore connecting diverse disciplines of study.
His work on Logistic regression as part of his general Machine learning study is frequently connected to In patient, Tumor marker and Carcinoembryonic antigen, thereby bridging the divide between different branches of science.
Jundong Li undertakes interdisciplinary study in the fields of Causal inference and Big data through his research.
His study explores the link between Statistical classification and topics such as Social network analysis that cross with problems in Theoretical computer science.
His Recommender system research is multidisciplinary, incorporating perspectives in Adversarial system, Social relation and Autoencoder.
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